Disconnecting ArcGIS Pro from PostgreSQL database?
What is the equivalent to the ArcCatalog's
Disconnectoption in ArcGIS Pro? I have successfully connected to a Postgresql database, but cannot figure out how to disconnect.
This is what I'm seeing on the right-click of my database connection:
I know the ArcGIS Pro client is still connected to the postgres database:
I also tried:
- Removing the connection.
- When that didn't work, I opened a blank project.
Neither closed the connection.
Update 2: I don't think this would matter to the connection behavior, but there's no sde/gdb in the postgresql database--just straight 9.3 postgresql database. Thought I would mention in case others stumble upon this question.
As commented by @JayCummins an enhancement request appears to have been logged as:
ENH-000084097 - Provide a way to disconnect database connections from ArcGIS Pro
GIS Analyst @ City of Pasadena
The City of Pasadena’s Department of Information Technology is currently seeking a GIS Analyst to assist in the design, development, analysis, implementation, and maintenance of the city’s Geographic Information System. Under general supervision, the GIS Analyst performs professional duties in support of City and departmental GIS databases and geographic information systems capabilities.
The ideal candidate will bring experience with some or all the following technologies:
• ArcGIS Desktop suite and extensions 10.6 or higher including ArcGIS Pro 2.6 or higher.
• Adept with scripting, automation, and programming languages
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• Web GIS technologies including ArcGIS Online, ArcGIS Enterprise, and ArcGIS Web AppBuilder.
A successful candidate possesses professional knowledge of the theory and principles of GIS software and database manipulation and product/output development.
GIS Analyst salary: $81,743.00 – $102,178.00 Annually
For more info and to apply, click here.
What is a geographic database?
Geographic databases can store more complex elements needed to describe the world and the roads or buildings built upon it. The basic data element is the point that is the combination of the longitude, latitude, and sometimes the altitude. The points can be joined together into polygons that might represent political boundaries or regions of a map. These polygons can be joined together or subtracted with set operations like union to build complex representations.
Capable geographic databases can compute complex functions like determining the distance between two points or whether one point lies inside a polygon. Some can adjust the answers to account for the curvature of the earth.
- Finding the closest entry to a point, a feature that can help find the nearest restaurant.
- Identifying the right demographic unit where a house might sit, a common task for computing real estate taxes.
- Computing the distance along a path or a road.
- Geocoding to convert a street address (or other political coordinates) into latitude and longitude.
- Reverse geocoding to find the best address or other coordinates for a latitude and longitude.
- Organizing hierarchical zones for a region like breaking up the country into census blocks and tracts.
- Supporting scientific investigation based upon location, a crucial feature for answering many economic questions about how geography affects work and health.
- Generating basic maps as well as maps with data overlays to visualize research.
- Tracking the performance of groups like sales teams when the groups are defined by geography.
- Looking for geographic correlations through visualizations.
- Kickstarting fraud detection, especially in areas where fraudsters practice in parallel.
Geographic databases have become increasingly essential in enterprises. Many geographic databases are built as a set of extra features that can be installed in a more traditional database, extending the standard data formats to handle geometry. For example, SQL becomes GEOSQL XML becomes GML JSON becomes GeoJSON, and so on. These collections of features take on the form of a GIS (for geographical information system). Many popular tools like ArcGIS rely upon the support of a geographically aware database.
Draw Tap GIS, LLC is expanding their GIS services and they have 3 GIS positions that are open. These positions will support the City of Pomona. Click below for more info and to apply:
The City of Pasadena’s Department of Information Technology is currently seeking a GIS Analyst to assist in the design, development, analysis, implementation, and maintenance of the city’s Geographic Information System. Under general supervision, the GIS Analyst performs professional duties in support of City and departmental GIS databases and geographic information systems capabilities.
The ideal candidate will bring experience with some or all the following technologies:
• ArcGIS Desktop suite and extensions 10.6 or higher including ArcGIS Pro 2.6 or higher.
• Adept with scripting, automation, and programming languages
• ESRI enterprise Geodatabase data management
• Web GIS technologies including ArcGIS Online, ArcGIS Enterprise, and ArcGIS Web AppBuilder.
A successful candidate possesses professional knowledge of the theory and principles of GIS software and database manipulation and product/output development.
Software similar to or like GeoMedia
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This article (also a picture) presents a timeline of events in the history of Microsoft Windows operating systems from 1985. History of Microsoft Windows Wikipedia
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Disconnecting ArcGIS Pro from PostgreSQL database? - Geographic Information Systems
Journal of Geographic Information System Vol.07 No.04(2015), Article ID:58803,9 pages
GIS Based Management System for Photovoltaic Panels
Muhammad Luqman 1* , Sajid Rashid Ahmad 1 , Samiullah Khan 1 , Farkhanda Akmal 1 , Usman Ahmad 1 , Ahmad Raza 1 , Muhammad Nawaz 2 , Asif Javed 3 , Hamad Ali 4
1 Institute of Geology, University of the Punjab, Lahore, Pakistan
2 Department of Space Science, University of the Punjab, Lahore, Pakistan
3 Punjab Group of Colleges, Kasur, Pakistan
4 Punjab University College of Information Technology, University of the Punjab, Lahore, Pakistan
Copyright © 2015 by authors and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
Received 11 June 2015 accepted 11 August 2015 published 14 August 2015
This paper provides a proposal of implementation of latest technologies in photovoltaic installation and management system. Punjab Government Cooperative Housing Society was selected to find out suitability for photovoltaic installation and its management after that. Google Earth imagery and scanned maps were selected for the preparation of spatial and attribute data of buildings in the study area by using ArcGIS software. In first stage data were digitized and suitability and potential for each building were estimated. In second stage all the tabular data which consisted of owner information, panel information, its potential and location were linked to each other for management purpose. Selected area for solar panels installation was linked to building information for query and visualization.
Photovoltaic (PV), Asset Management, Google Earth Imagery, ArcGIS 10, Geographical Information System (GIS)
GIS is an information system or technology which has strong capability to maintain and analyze geographic or spatial data. Data stored in GIS program have geography as well as attribute information. GIS technology maintains the data into layers and relating sets by geography. GIS is more easily conveyed in geographic layout than traditional tabular formats  . GIS functionality may be presented in desktop, mobile or in internet browser  . Application of GIS is increasing day by day due to its predictive models which are providing functions for the data storage, manipulation, calculation, analysis and output processing  . GIS capabilities have been utilized to provide spatial data management system and spatial decision support system (SDSS) with output display  -  .
In GIS applications, presentation of reality by using basic geometries of features, their accuracy, preparation and processing of data has different resources. Analysis is applied on data to get results   . GIS has deep impacts on the public organizations which deal with utility networks like gas, water and power supply. GIS is not limited to utilities it also deals with the management and planning of infrastructures easily  -  . Competition for the human technology is increasing in market it becomes mandatory for the users and proprietors to be up to date by using latest technologies  . GIS is a system which consists of hardware, software, data and people capable to capture, store, update, manipulate, analyze and display spatial data (ESRI, 1990). GIS is a complete software package capable of complete database system. GIS has all functionalities of computer system which are required to make cartographic presentations and maps  . ESRI (1990) is enhancing and updating the database with the passage of time, to handle new datasets and spatial data with new procedures and techniques  .
Electric energy short fall is very problematic issue in Pakistan at present era. Domestic and well as commercial users are facing the same problem. According to Pakistan Electric Power Company (PEPCO), the peak energy demand in June 2012 was 17,861 Mega Watt (MW) at the same while peak energy generation capacity of PEPCO (2012) was 14,317 MW  . It is clear that there is short fall of 3544 MW. Consumers have to face load shedding due to this energy shortfall and a fraction of them uses other sources of energy like electric generators, uninterrupted power supply, solar panels etc. Arifeen (2013) analyzed in his article in newspaper that electricity demand had increased by 80% in last 15 years in Pakistan  . Water and Power Development authority (WAPDA) estimated that electric energy demand by the consumer in the country would be 40,000 MW by 2020 in Pakistan. There was 4.8% increase in energy demand every year, but it was expected that, in the future, this demand would be 8% - 10% per year so 7% - 8% rate of energy increase was necessary in the energy generation.
All the energy sources like hydro power, oil, gas, nuclear energy, solar energy etc. are being used to produce electricity in Pakistan. But the sources like oil and gas are expensive. The Government of Pakistan made an autonomous board in 2003 for the research and generation of electricity from renewable energy. The board is called Alternative Energy Development Board (AEDB). AEDB was assigned a task to find out the all means of renewable energy. The board has a task of 5% share of total energy demand of electricity in the country by 2030  .
Solar radiations from sun have potential energy 6000 times the total energy demand of entire world  . Pakistan has a good geographic location for the reception of solar radiation as Pakistan has long shining hours and abundant amounts of irradiance. In Pakistan, it is estimated that there is 1.9 - 2.3 Mega Watt hour per meter square (MWh/m 2 ) solar energy potential while at particular places like Cholistan and desert areas the amount raises from average  . At present there are only two projects which are working using solar energy. The first project is working in District Kasur by a private stakeholder Roshan Power Pvt. Limited and the second is in Cholistan owned by CWE & WK (JV) producing 10 MW and 50 MW respectively. A solar power park project is under progress in Bahawalpur with proposed capacity of 100 MW by December 2014  . 27 LOIs have been issued for a consolidated capacity of 792.99 MW in Pakistan  . Solar energy is also being used in Pakistan in emergency phone both along the motorway and road light in Lahore etc.
Government of Pakistan has announced a policy to purchase electric energy from stakeholders. Stakeholders may produce electric energy with the help of solar technology. According to policy, this energy would be added to national grid for domestic as well as commercial uses. As solar energy generation is a spatial related task i.e. solar energy is not the same at all geographic locations, geographic information system is proposed as management information system for the production and addition of generated energy in to national grid. For this purpose, a study is proposed for the installation of solar panels at the rooftops of households. Usually GIS is considered as a system for spatial analysis and decision support but in our study we propose GIS as a management information system in energy sector.
Solar panels can easily be installed in any land. These lands may be barren lands, closed landfill sites, along the roads, in deserts, in agricultural land or on the roofs of the building. In this study residential buildings are selected so that there will be no loss of agricultural land and energy transmission loss. GIS database would have records for each house for energy generation. The energy generated by the roofs of the buildings would be added to national transmission grid. The energy production record for each building would be record in a database. Geographic information system has a SQL database in it. GIS has database for each house number, its size and selected roof area for panel installation, distance to national grid and energy generation from each building roof. Database management system is a software that collects, manipulates, queries and retrieves tabular data. In GIS, DBMS is related to each geographic feature as well. So GIS is a powerful software which maintains geographic as well as tabular data.
The Punjab Government Servants Housing Scheme (PGSHS) Lahore was chosen as it had unique design and high suitability for Photovoltaic (PV) installation. PGSHS is developed and organized by Punjab Government servants housing foundation (PGSHF). PGSHF allots houses to Government of Punjab’s employees. The Study area is shown in Figure 1.
This study area is located at 31.400 north latitude, 74.170 East longitude and total area of society is 524,692 m 2 . PGSHS has high potential for solar energy generation as climate of Lahore is warm city so study area has a high amount of irradiance. Temperature range of PGSHS ranges between 30.8˚C averages high to 17.8˚C average low (source: NOAA (1961-1990)). In 2010, 738 millimeters rain fall was recorded. PGSHS pertain least topographic variation and suitable for PV installation. Nearly all the roofs possess same height as well as same elevation. There are four classes of houses at study area. The classes, number of houses and their areas are as shown in Table 1.
Table 1 . Study area buildings and types.
2.2. Database Development and Digitization
Spatial data was prepared with the help of scanned maps provided by the PGSHS, satellite imagery and ground surveys while attribute information for the buildings and electricity bills was obtained by the PGSHS scanned maps, residences and Lahore Electric Supply Company’s website. Seven layers were created for database. These layers consist of boundary of society, parcels, selected area, owner information, years, and month and raster layer for the calculation of solar energy potential as shown in Figure 2. For each layers attributes for added as fields and these layers were linked to each other by using a primary key. In GIS environment there is a two type of data spatial as well as attribute data. Spatial data consist of parcels, roads, geometry, and proposed area for solar panel installation while attribute data consist of house number, their sizes, class, land use, potential etc. Figure 3 depicts flow chart for layers generation and their relationship in database. All stages from data collection to analysis and output are shown.
Figure 2 . Flow chart of current study.
Figure 3 . Classification of study area.
Google Earth imagery which provides free high resolutions satellite imagery visualization is utilized for digitization of rooftops in the study area. Imagery of the study area was downloaded by using Google Earth Pro software. March 18, 2014 was the latest image available there was Zero cloud cover in the imagery which makes it best for visual interpretation and digitization. This downloaded image was georefred according to coordinates of Google earth in Geographic coordinates system. The coordinate system was World Geodetic System 1984 (WGS). Environmental Systems Research Institute’s (ESRI) ArcGIS 10.1 software was used to georef this downloaded image.
Figure 4 depicts tables for Geodatabase. Their relationship was join with each other. Only three tables are geometry information these are boundary, selected area and parcels. These geometry tables were digitized in ArcGIS software. Parcels are showing building types, their size, and property unit number as shown in Figure 2. Google Earth satellite imagery was used as a base map to digitize area for PV installation at rooftops of PGSHS.
The zoom level for the digitization of polygons was 1:250 in the ArcGIS software. Screen shot of digitized rooftops is shown in Figure 5. Only a specific portion of rooftops is selected for PV installation while the remaining roof is free for domestic uses. As there are four classes of houses in the study area so every class have different size so same area was selected for each roof in each class. School buildings and Commercial buildings in the study area are also considered while digitization for PV installation. To overcome the shadow problem only the mounts of rooftop of buildings are digitized.
There are some limitation for the PV installation, PV installation is effected due to slope  - , aspect  , Direct normal irradiance or solar potential     , proximity to roads  , distance to transmission lines/power lines   , sand/ dust risk  , environmentally sensitive areas   , weather conditions  and many other factors like dam sites, cultural areas flood areas etc. Due to these factors only those roofs were selected during digitization for PV installation which meets the following criteria:
South, south east and south west directions are best for PV installation. All buildings in the study area have flat surfaces at the roofs, so orientation and slope are not hurdles for PV installation. There was lack of tilted roofs, shade free area was selected on the rooftops in this study. While digitization and selection best care is adopted to select the only roofs which receives direct solar radiations. Those rooftop areas which were shaded by other buildings, trees, structures or vegetation those may affect the solar yield were not included in the study. In the study area, there is unavailability of heating, ventilation, and air conditioning (HVAC) system so maximum roof is available for solar PV installation.
A Global Positioning Device (GPS) manufactured by the Garmin was used to know elevation in PGSHS. After averaged of eight points it was found that the elevation in the study area is 202 meter. Forty consumer bills were borrowed from the residence and scanned. It was found that there was a direct relation between the building size and utility bills. The Bigger the house the more the utility bill and vice versa. We collected bill for each class. GIS software is a convenient way to generate and develop maps of solar radiation potential and spatial relationship to other data (Fu and Rich, 1999). In present study, ESRI’s software ArcGIS’s tool is utilized for GIS modeling in study area to estimate solar radiation ns. The input for this tool for processing is a Raster dataset. This raster was converted from digitized roof polygons by using vector to raster conversion tool from toolbox of the software. These polygons were assigned average elevation values collected from the field by using GPS device. Electricity rate for all kind of consumers e.g. commercial as well as residential and bill data was download from Lahore Electric Supply Company (LESCO) website.
By using solar flux tool in ArcGIS software it was found that average potential in the study are for generation of electric power was 1448 kWh/m 2 /year for the year of 2014. We considered that all the roof have same slope and elevation so all the roofs would produce same amount of energy as topography of study area is plan and Lahore is a plan city. By using the same software potential for each house was calculated for the time span of yearly, monthly and daily basis as shown in Figure 6.
For management purpose area of each house was calculated in ArcGIS software. Similarly area for PV installation for each house was calculated. The software have complete record for each house whether there is a difference of even one meter of a building to another building in tabular data as well as spatial data or geographically.
The information in Table 2 was summarized for the database of the study area. The record of each house, its area selected for PV installation its monthly, yearly electric energy generation is shown in Figure 7.
Figure 6 . Annual solar radiation calculated for 2014.
The Database can serve equal to any other database softwares. Any kind of information regarding house or solar panel would be added. Payments records, problem record, contact numbers etc. are example for the management. A separate database file containing information regarding house is shown in Figure 8. This file is joined spatially with the primary key of house unit number. The table can also be related to common field as a relationship. Meanwhile a number of separate table or field in same SQL attribute table would be added for monthly record for each coming years.
Figure 7 . Spatial and tabular data depiction.
Figure 8 . Detailed tabular data of parcels.
Table 2 . Suitable rooftop area by general building type.
This study suggests that management and administrative process at a geographical area are so easy to manage using the geographic information system. The position of any object can easily be marked in GIS. This will be very helpful for maintenance work as location and direction of each building are available. This kind of application could be applied in any human resources organization.
MuhammadLuqman,Sajid RashidAhmad,SamiullahKhan,FarkhandaAkmal,UsmanAhmad,AhmadRaza,MuhammadNawaz,AsifJaved,HamadAli, (2015) GIS Based Management System for Photovoltaic Panels. Journal of Geographic Information System,07,392-401. doi: 10.4236/jgis.2015.74031
By Alexander Perepechko and Dmitry Sharkov
Published on August 13, 2017
Abstract: In this paper, we explore how a counter-mapping framework can contribute to developing a freely accessible worldwide geographic information (GI) service for Russia, a country with a legacy of classified (closed and semi-closed) information systems. The Russian Federation Digital Data (RFDD) project and the Central Eurasian Interactive Atlas (CEIA) were the major products of these efforts. This article describes the volunteered geographic information (VGI) methods which were used to create these products. We portray how in 1994-2004 volunteers and paid part-time citizen science at the University of Washington and its consortial partners (the Evergreen State College and South Seattle Community College) obtained geographic data from Russia, developed GIS databases in the United States using appropriate hardware and software, assured the quality of these data and created a clearinghouse and geoportal for delivery and use of the dataset. A focus on error detection to improve data quality was crucial for this project. The consortium was organized in 2000 to promote the creation of a GIS database on Russia and dissemination and use of these datasets using the Web. Unfortunately, the project has failed to publish the dataset beyond the Universities’ Consortium. CEIA is for limited use and serves as a curricular and research focus of Russian Studies programs in the Universities’ Consortium.
Keywords: Central Eurasian Interactive Atlas (CEIA), Clearinghouse, Counter-mapping, Data quality, Database, Geographic information (GI), Geographic information service, Geoportal, Russia, National spatial data infrastructure (NSDI), Russian Federation Digital Data (RFDD), Data quality, Universities’ Consortium Geographic Information Service (UC GI Service), Volunteered geographic information (VGI).
We acknowledge support for portions of this article provided by U.S. Department of Education Title VI Program for Technological Innovation and Cooperation for Foreign Information Access (TICFIA) Award Number P337A990006-01, the Suzzallo Libraries at the University of Washington, and the IFS Family Foundation. We are thankful to Michael Goodchild, Nicholas Chrisman, Massimo Craglia, and Ellen O’Meara for valuable suggestions on earlier drafts of this paper.
The term counter-mapping was coined by Peluso (1995). In the GIS literature it is commonly defined as user-generated GI, supported by GIS technologies. Goodchild (2007 also Elwood, Goodchild, & Sui 2011) refers to these new ways in which we develop maps as volunteered geographic information (VGI). He contrasts the VGI with more conventionally produced and mediated forms of GI based on a national spatial data infrastructure (NSDI) model. In this older NSDI model, national mapping agencies (NMAs) play key roles as GI producers and providers and developers of standards (Goodchild, Fu, & Rich 2007). The acquisition, processing and use of Russia’s GI discussed here augments the literature on counter-mapping, VGI and NSDI for a country with a legacy of classified (closed and semi-closed) information systems. Here we investigate how volunteers and paid part-time citizen science can make a contribution to the development of a focused data quality control service integrating a geographic database, clearinghouse and geoportal for delivery and use of datasets in the absence of Russian governmental willingness to launch a GI service. We deal in this paper with the electronic form of counter-mapping. Major components of this service are located in Russia and the United States. We call our service the Universities’ Consortium Geographic Information Service (UC GI Service or simply Service). To place this study in context it is necessary to summarize some of the new theories and points of view put forward by GIScience.
Before the massive effects of the arrival of Google, MapQuest, Microsoft, and Yahoo! in the late 2000s, the world of GI was dominated by NMAs and expectations for access to data were very different. Although the terms used to portray delivery and use of datasets vary widely, scholars used to describe geographic databases (for example, Rigaux et al, 2002) and data quality (for example, Devillers et al, 2010) concepts and approaches through the lens of the dominating role of NMAs. For instance, Groot and McLaughlin (2000) analyze infrastructure integrating foundation (“baseline”) data layers (framework data) from local to national to global levels. Rajabifard and colleagues (2003) emphasize dynamic relations between “people-data” (data supply, data quality control, and data use) and technological components (standards, access network, and policy) of GI services.
Since each country’s institutions, cultures, laws, and legacy information systems are different, Masser (2010) proposes to distinguish between countries that are national data producer-led and those that are not. European scholars, such as Craglia (2006 2010) and Annoni (2005), tend to see GI services as a governmental function when traditional mapping agencies ensure an on-line infrastructure supported by distributed on-line feeds with a focus on quality control.
There were two generations of GI services (Craglia et al, 2003 Masser, 2005) before the time of VGI. The first generation, in the 1990s, was focused on the creation of GIS databases and platforms for access to data. The second generation, in the 2000s, concentrated on the sharing and reusing of datasets. Clearinghouses and geoportals are key elements of the first and second generations, respectively. As Chrisman (2006) cogently argues, when data from multiple sources, jurisdictions, efforts and stakeholders are involved the data quality issue becomes the central problem of a unified GI service – a geospatial one-stop (GOS).
Goodchild (2007, 2009) expresses scepticism about these old authoritative sources of geographic data. He discusses a citizen-participation approach, in which VGI enthusiasts and scientifically trained citizens or networks of citizens collectively use open-source GIS and bi-directional collaboration Web to establish and maintain GI services. According to this vision, local knowledge, trust and adequate expertise would provide the basis of a mechanism for quality control. Goodchild considers that hybrid solutions, when GIS agencies and businesses rely on private local volunteers and paid part-time citizen science to acquire and maintain geographic resources, may represent the best of both approaches.
UC GI Service for Russia, a country with a closed or semi-closed information system
The visions discussed above provide a foundation for the examination of various types of GI services in many organizations, countries and regions. What is missing from these discussions, however, is GI services for countries with closed or semi-closed information systems in terms of public access to spatial data and metadata. These countries have a relatively low GNP per capita, numerous bureaucratic classes, and are known for poor data quality. In these countries, the more powerful an organization or institution, the more information it has at its disposal (Taylor et al, 1995 Taylor et al, 1991 Tosta, 2001). Russia is one of these countries. According to Miller (2010), the President of the Russian GIS-Association, approximately 80% of maps in the major national mapping agency have never been viewed. Further, in Russia only 15% of users can buy GIS maps about 85% of users are left with no choice but to create GIS products by themselves. Duplicated efforts of several players have resulted in multiple data standards and waste of resources. In this situation a research group in American academia made a contribution to the acquisition, processing and use of Russia’s geographic data sources.
The research group included part-time GIS professionals with knowledge of Russian regional geography, citizen science participants familiar with Russian culture who received training in GIS software and full-time post-Sovietologists employed by the Universities’ Consortium (UC). This group was based in the Seattle area and relied on networks of volunteers and paid contractors in Russia acquiring geographic data resources. The major motivation for this initiative was (1) the creation of GIS layers using multiple on-line and off-line sources and (2) the distribution of these data to users worldwide using a clearinghouse and geoportal. The development of processes for data quality control and data updating was a key element of this initiative. The U.S. Department of Education provided the greater part of funding University of Washington Libraries 21 st Century Fund and the IFS Family Foundation were the two other donors. Applying Masser’s typology of SDIs to our study, these characteristics of the UC GI Service may well classify it as a hybrid type. It involved neither a national data producer nor a formal government mandate.
A key aspect of the UC GI Service is that its components (and relations between them) are situated in two countries with very different legacies of information systems. The creation of the RFDD and CEIA databases includes the data supply stage in Russia and the quality control and updating stages in the United States (figure 43). Desktop GIS and Web 1.0 technologies facilitate flows of digital data between these stages. NGO and tourist networks in Russia make it possible to deliver printed sources to the United States. The use of RFDD and CEIA involves a clearinghouse and geoportal, respectively. Data supply, quality control and data use involve important issues of standards, access network and policy – the main technological components of the UC GI Service. Technological components of the Service in Russia and the United States have very different ontologies. While American standards, access network, and policy are relatively well outlined, in Russia they can be roughly described as belonging to an unstable institutional and political environment. To access datasets, suppliers, data producers and data users need to utilize these technological components. The complexities entail instrumentalism as the methodological perspective in building of the UC GI Service.
Figure 43. Structure and dynamics of the UC GI Service. (Generated by the authors).
The two-country nature of the UC GI Service is not unique. For instance, Institute National des Langues et Civilizations Orientales’ (INALCO) research laboratories acquire and process Russian GI in Russia and use results in France. INALCO’s model is relatively inexpensive. It is based on a network of academics in Russia with access to spatial data and uses inexpensive mapping technologies.
The remainder of this paper describes the methods used to build the UC GI Service. We analyze the scope and evolution of the UC GI Service, the GI providers in Russia, the data quality control mechanism in the United States and data use in the United States and worldwide, and how these stages are related to standards, access networks and policy in the two countries.
The scope and evolution of the UC GI Service
Data layers created in RFDD in 1994-1998 can be seen as key drivers of what is considered the first generation of GI services. The creation of this database includes the acquisition of geographic data in Russia, the incorporation of these data into datasets, and the provision of a clearinghouse – the platform for community access to these data.
Figure 44. The highest level territorial administrative units of the Russian Federation. (Source: adapted from https://futuristrendcast.wordpress.com/2015/07/29/ladas-guide-to-the-85-subjects-of-the-russian-federation/).
Figure 45. Three GI services: UC GI Service (A, B), Wikimapia (C), and Kosmosnimki (D). (Source: Generated by the authors based on a personal copy of the CEIR coverage and data from scanex.ru, kosmosnimki.ru, openstreetmap.org and wikimapia.org).
A complex and hierarchical set of territorial administrative units is found in the former Soviet Union and Russian Federation that is not entirely standard across the region (Perepechko et al, 2005). The highest-level units are oblasts, republics, autonomous okrugs, krays, the two federal cities (Moscow and St. Petersburg), and one autonomous oblast (figure 44). These Russian territorial-administrative divisions are essentially analogous to state-level units in the United States. Except for the two federal cities, almost all of the highest-level territorial-administrative units are further divided internally into two categories, rural raions and urban municipalities (figure 45: A, B), roughly analogous to U.S. counties and cities. Russia’s 65 largest municipalities (cities) have internal urban raions. Urban raions are typically associated with the administration of large cities. Moscow’s 126 urban raions are aggregated into 10 administrative okrugs. St. Petersburg’s 111 urban raions are called municipal okrugs or municipal entities and are aggregated into 20 administrative raions, two of which do not have internal municipal entities. These numbers are for 2002. The number of administrative units at various levels changes over time. Incorporation of Russian-produced raion/municipality level information into the UC GI Service involved hundreds of printed and digital maps and sources of tabular data. In terms of hierarchical capability for GI services (Eagleson et al, 2003 Rajabifard et al, 2003), our raion and municipality territorial administrative units may be seen as building blocks supporting the provision of spatial data needed by a GI service at higher levels in the hierarchy, such as oblast or national levels.
Data layers created by the authors in the Russian Federation Digital Data project (RFDD) (Perepechko et al, 1998) and the Central Eurasian Interactive Atlas (CEIA) (2004) include only municipality and rural rayon administrative units. Thus, problems existed regarding the accuracy of data derived from the development of a large geographic database using numerous sources at various levels of aggregation. A major practical challenge was to devise methods of error detection to improve data quality.
Sharing and reusing of datasets is the focal point of the second generation of GI services, which requires a shift from the product to process model (Masser, 2005). The major advances in Web and GIS technologies in the early 2000s resulted in a shift from the creation of databases to the development of geoportals. Tait (2005) defines a geoportal as a web site where geographic content of data can be discovered, accessed and used. Technologically the second phase of our initiative is related to the second generation of GI services. CEIA data layers, built on the basis of RFDD, and a geoportal for these new datasets were created in 1999-2004. The development of data quality control and data updating processes was of central importance for the both phases of this initiative.
Russian suppliers of geographic data: old and new players
Old national government sources of geographic data in Russia
The State Administration of the Russian Federation on Statistics (GosKomStat) (Central Statistical Administration of the Department of Interior before the Revolution Central Statistical Administration or TseSeU in the Soviet period) and the Federal Agency of Geodesy and Cartography, or Roscartografia (Corps of Military Topographers from 1887 right up to 1917 Chief Administration of Geodesy and Cartography or GUGK in the Soviet period) are the two main institutions collecting and managing, respectively, non-spatial (also called attributes, characteristics, variables) and spatial data in Russia (Shibanov, 1975). In 2008 the Federal Agency of Geodesy and Cartography and the Federal Agency of Cadastre were merged into the Federal Service for Government Registration, Cadastre, and Cartography by a decree of President Medvedev (Medvedev, 2008). They operate (or operated) under legal rules (statutes) that establish the objectives of these organizations (Chrisman et al, 1985 Harley, 1989 Harley et al, 2001). However, this is only a part of the truth.
Another part of the truth is that they operated and operate under the patronage of state, military, and intelligence services. In particular, Roscartografia had and has privileges to obtain expensive advanced equipment from munitions factories, information from satellites, and access to hard currency sources for purchasing advanced technologies in Western countries. Even in post-Soviet Russia, Roscartografia does not have digital metadata and is still a semi-closed organization in terms of public access to the raion/municipality-level spatial data. Geographers have not seen many of the special maps (some exist only in a few copies) produced by Roscartografia and its Soviet predecessors.
The political climate in Russia has strongly affected processes of data collecting and management by state institutions. The best locational and attribute data sets have been produced in periods of relative political liberalization.
The First Russian population census of 1897, which included eighty-nine volumes produced by the Central Statistical Administration of the Department of the Interior, is the best archival source of spatial and attribute data about pre-Revolutionary Russia. Yearbooks of Russia, published by the Central Statistical Administration during 1905-1915, are another valuable source of demographic and socioeconomic information. Another useful statistical source for historical, geographical and political science studies is the population census of 1926 at the uezd level (taken during the time of the New Economic Policy or NEP) The uezd was a basic administrative unit in pre-Russia and Soviet Russia until the 1920s uezds were larger than raions but much smaller than oblasts. Uezd-level territorial-administrative units are further divided internally into the lowest-level territorial units, called volosts. Data on election returns in the pre-Soviet period can be found in documents published by the TsentrArkhiv (Central Archive) of the USSR. Some pre-Revolutionary maps with detailed boundaries of uezds (for example, Map of Amur Oblast (1916) Map of Zabaikal’sk Oblast (1918) Map of Gold Mining Raions (1901) General Table Map (1913)) are useful sources for geographers, historians and political scientists.
The reliable spatial and attribute data collected for our databases were produced mainly in the period of Khrushchev’s thaw (1956-1964) and Kosygin’s reforms (1965-1968). In the period of Yeltsin’s democratization and decentralization (1991-1999) local publishers produced detailed raion/municipality-level maps and it was possible to purchase these maps freely in Russia. By reliability we mean here the controllability and blunders (gross errors) of the data (see Detreköi, 1995) as a result of deliberately falsified public maps and attribute data for the country, distorted boundaries, misplaced rivers and streets, and omitted geographical features on orders of the secret police and military (Keller, 1988). Administrative boundaries and socioeconomic, demographic, and other conventional framework data can hardly be created and updated through remote sensing or VGI methods (Elwood, Goodchild, & Sui, 2011). The framework data is created and maintained by Russia’s national mapping and statistical agencies. In periods of liberalization these agencies release relatively accurate maps that in periods of centralization had been classified as state secrets.
On the basis of maps produced in the 1950s and 1960s, we created a prototype of the raion/municipality-level GIS layer (Russian Federation Digital Data). This work led to a grant from the U.S. Department of Education to develop the Central Eurasian Interactive Atlas. Because services concerned with the management of national information assets did not exist in the Russian Federation, we requested permission from Roscartografia to reproduce the boundaries of raions and municipalities from maps published in 1993-2001. The copyright owner did not respond. For that reason, we used the prototype layer to create a newer raion/municipality-level digital layer for the Central Eurasian Interactive Atlas. We used newer maps and Russian-made GIS products as well as a hand-updated revision of a 1994 handbook of administrative territorial divisions of the former USSR as sources to check and update the boundaries of some raions and municipalities. Archival sources of attribute data are the official population census of 1989, the 2002 All-Russia population census, selective censuses of 1990-2001, and official electoral statistics of the Central Electoral Committee.
New producers and resellers of geographic information in the Russian Federation
The transitional processes in Russia under the Yeltsin administration indicated, to a certain extent, a decentralization of the top-down, authoritarian state cartographic and statistical agencies in which progress was slow and deliberate. For Roscartografia and GosKomStat, the commercialization and diversification of products and increasing sales at market prices became important issues. The old imperial agencies were not willing, however, to accept the idea of information freedom. They continued using secrecy to maintain scarcity and control over their products. The period of relatively free access to raion/municipality spatial and attribute data in the 1990s, when the KGB was being reorganized to the FSS (Federal Security Service), was marvellous but very short. Nevertheless, our research group was able to obtain spatial and attribute data for our projects “over the counter” from the Central Electoral Committee of the Russian Federation, regional and city statistical offices, private contractors, small mapping businesses and academics, and by swapping data with our colleagues from western academic institutions in view of existing or potential professional and social relationships. NGO and tourist networks in Russia helped to deliver obtained data to Seattle. In the absence of a policy umbrella for GI in Russia and with too slow progress in traditional mapping and statistical agencies in Russia, it was inherently easier to develop data sharing arrangements on a commercial and informal professional basis and through non-governmental networks. Not entirely unlike sharing arrangements in local government in the United States, described by Harvey (2003), “trust”, not regulation, was the key word in our data sharing arrangements in Russia.
Democratic suppliers of geographic information: electoral committees and local statistical offices
In our two database projects, official attribute data published by the Central Electoral Committee of the Russian Federation and regional and city statistical offices were used.
Based on the Constitution of the Russian Federation (Article 96) (Constitution, 1995, Federalnyy, 1994) and the Law on State Duma (lower house of the Russian parliament) Elections (Chapter IV) (Federalnyy, 1995), a five-level system of electoral committees was established: 1) Central Electoral Committee, 2) electoral committees of the components of the Russian Federation (Oblasts, Republics, Krays, Federal Cities, Autonomous Oblast, Autonomous Okrugs), 3) district electoral committees, 4) territorial electoral committees (raions, municipalities, and others), and 5) precinct electoral committees. According to the Law on State Duma Elections, electoral committees must publish all documents on elections, election returns (Article 10) and plans of electoral districts (Article 11). Therefore, our research group had legal access to spatial and attribute information on elections from the macro- to the micro-scale.
During the Yeltsin decentralization, some functions with respect to collection and dissemination of GI were delegated to the oblast and city levels. Regional and city statistical bodies not only collected socioeconomic and demographic information at the raion and city level for a central office in Moscow but also acted as specialized publishers of local data. They maintained metadata records on behalf of GosKomStat and became data producers concerned with particular thematic interests such as environmental, transportation and agricultural data and provided services based on these data.
Imputs from contractors, small mapping businesses and geographic community
Raion/municipality locational and attribute socioeconomic, demographic and political geographic data for the whole Russian Federation used in our projects were often not found in official Russian sources. Free agents (private contractors) in Russia’s GI market were valuable sources for obtaining these locational and attribute data (and were often unwilling to divulge details to others about their relationships with our group).
Small mapping businesses are new spatial data suppliers and users in Russia’s information market. They sometimes operate under the auspices of state agencies and are the “commercial extensions” of these agencies. GI scientists (see Nebert, 2004) mention that small businesses are often value-added resellers they add some new feature to existing products, and then make them available as new products. Skilled geographers, cartographers, statisticians, GIS professionals, and programmers often hold two jobs—working both for state enterprises/institutions and for private companies—and are involved in projects with western Universities, research centers and foundations. These professionals frequently become free agents or emigrate to the West. Small businesses in the field of cartography, GIS, information technology, and computer graphics and design have entered the Russian market. The economics of the situation in Russia also requires cooperation among small firms and individuals and the sharing of facilities. Russian business culture is relaxed about formalized arrangements involving contracts, especially for small transactions, which facilitates flows of GIS products in all directions. Small mapping businesses were trusted partners of the UC for obtaining locational and attribute data.
An information business is supposed to operate on the basis of the Russian Federation Civil Code (1994). According to the Code (Sub-section 3 Chapter 6, Articles 128, 139), information is protected by civil rights. The possessor or holder of information that includes official or trade secrets must take measures to protect their official reports (e.g., individualization of information using firm names, trademarks, and stamps of secrecy (such as “For official use only,” “Secret,” “Top secret”). A person who receives official or trade secret information by unlawful means is obligated to compensate for the inflicted damage. The Code does not make clear how to assess the damages and to whom compensation must be paid.
Diffusion of Russian-produced GI through small businesses to the West happens for three reasons. First, the legal Code does not reflect the actual situation of Russia’s information market. Many sources of locational and attribute information at the raion/municipality level are converted into digital form. They do not have a producer’s name, trademark, or stamp of secrecy and are being offered as anonymous products or sub-products in Russia’s semi-chaotic information market. These products often do not meet important institutional standards such as agreements on exchange protocols (Buttenfield, 1997). In many cases these products are the only sources of information for scholars from Western countries. For instance, municipality data for industries in digital form (“file 555”) was an article of trade in the 1990s.
Secondly, copyright regulations confuse those who want to extract information from maps created by Roscartografia and statistical materials published by GosKomStat. Here we have to deal with international differences in copyright protection, “standards” (Curry, 1991), acceptable kinds of behavior and expected patterns of action. For example, in the United States the most important copyright issue is whether copying would have a harmful economic impact on the sale of a copyrighted publication (Monmonier, 1993, 2007). In Russia the legal criteria are framed by the vague definition of “national security interests”. In fact, decision-making on copyright permissions is based on unwritten inter-institutional regulations. Obtaining permission depends on three essential factors: “to give to whom” (the user), “to give what” (content of information), and “to give why” (purpose of use). The first is more important than the second, the second is more important than the third. In fact, all three are components of the old Soviet censorship.
Thirdly, in many countries locational and attribute information at the highest levels of spatial resolution have been available for a long time. Russia is still different. Russia’s emerging information market is an arena of international competition. Different members of the Russian geographic community link the Russian information market to international flows of information. Providing a colleague from a particular Western country with access to geographic data may be in alignment with the Russian geographer’s international (“Americanists”, “Germanists”, and others) or corporate (research institute, state agency, political group, and others) priorities and may be important for the career of the geographer and needs of his or her organization.
While we were collecting data for our projects, we found ourselves between two main traditions in intellectual property regulation, the Anglo-American labor-based tradition and Hegelian-based personality theory, or theory of moral right (Curry, 1997 Hendley, 1997). Soviet/Russian state institutions, responsible for the collecting, processing, and producing of locational and attribute data, use a normative basis for property rights. The moral right of the state to hold the information monopoly is based on Russian anti-individual social attitudes (Obolonsky, 1995), a holistic doctrine of national security, and idolatry of the Russian written text.
The paradox of regulations in the area of GI in Russia can be defined as follows. One can obtain GI legally, but it is uncertain whether one can legally use the content of this product. This ambiguity of the institutional environment—where new organizations, regional agencies, small mapping businesses, academics and contractors act like value-added resellers of data which often do not meet important standards—negatively affects the integrity of the data itself (Perepechko, 1999). Under the circumstances, desktop GIS and Web 1.0 played a key role in our ability to integrate these inputs and test them for quality.
Key role of desktop GIS and Web 1.0 in dissemination of Russia-produced geographic information in the international market
Each new technology changes the mentality of society as much as institutional and cultural barriers permit. As Rhind (2000) demonstrated, strong institutional and cultural barriers can slow down the diffusion of new technologies but cannot stop them. In post-Soviet Russia, GIS technologies create a computer-led mentality in the geographic data business just as the internal-combustion engine created motor vehicle-led mentality 100 years ago.
The developing market of electronic GI has social effects. In some way it allows the re-emergence of civic culture (Pickles, 1995) in Russia. It is a potential source of progress for the previously marginalized community of geographers. GIS technologies provide geographers with communicational space apart from state controlled information monopolists. By creating comprehensive geographic layers, geographers can more actively participate in the social construction of nation, region, and place.
Reflecting the opinion of the “old guard” in the Russian geographic community, Gritsay, Kotlyakov, and Preobrazhenskiy (1994) complain that the country risks becoming “a geographic information colony” for Western partners. For the practical user of Russian-made GI, these authors do not consider the local nature of that information. The three authors recognize — though indirectly — that the geographic domain for Russian experts has become much more limited: the theory and practice of Soviet socioeconomic geography went bankrupt. Simultaneously, Russian-made locational and attribute data on regions of the former Soviet Union remain deficient in the international market, and access to this information is still partially closed. Russian electronic GI is conceived as a commodity for sale, exchange, or use by international projects.
Russian spatial and statistical data are products of the specific work organizations (and work ethic) in which they were created. Digital data are the computerized version of printed information. To be successful in the international market, Russian electronic geographic data need to conform to some Western standards. The absence of homologies between institutions as well as differences between representations of social, economic, and political phenomena in the West and Russia seems to be the main problem for Russian-made information in the world market.
Diffusion and adoption of the PC (Zittrain, 2009), dispersion of GIS technology (Coleman et al, 2000) and the explosion in using the Web 1.0 in the 1990s played a key role in dissemination of Russia-produced GI. Open to new functionality with minimal gate keeping, shrink-wrapped, single-user, PC-based desktop mapping systems (e.g., ArcView, Arc/INFO, and ArcGIS) greatly liberated individual citizens in Russia from state controlled information monopolists. Geographers, working for state organizations or small mapping businesses or acting as free agents, developed GIS data layers and made them available in the international market.
To check and update boundaries of raions and municipalities, we used Russian-made GIS products for several regions of the Russian Federation. Socioeconomic, demographic, and official electoral statistics (up to seven hundred attributes in annual progressions over a number of years) were obtained in the Russian version of SUPERCALK interoperable with EXCEL (Perepechko et al, 2005).
Russian GIS data utility and application can work well in its original environment. GIS software interoperability interfaces (McKee, 2000) eliminated much of the need to convert and manipulate whole Russian-made data sets. However, the degree to which these data can be successfully integrated with GIS data produced in Western countries involves issues related to quality. It is through the UC GI Service framework that users in American academia are willing to accept Russian-produced GIS inputs as reliable.
US based GIS processing of Russia-produced geographic data: error detection and data quality improvement
Different approaches to data quality are linked to different attitudes about the user and the data. Alternative definitions of data quality (Chrisman, 1984) can include: 1) conformance to expectations (fulfilling arbitrary thresholds), 2) following established procedures (as with geodetic standards), 3) fitness to the use (truth in labeling). Before anyone transfers data, one needs information describing that data. Metadata are “data” about data. Various countries have developed or adopted from other countries guidelines for metadata content. Data quality should consist of attribute accuracy, positional accuracy, logical consistency, lineage and temporal consistency, and completeness (Paradis et al, 1994 United, 1994).
Since the degree to which wide acceptance and productive use is a function of data compatibility (Doucette et al, 2000), data quality aspects were of crucial importance for our projects. Socioeconomic, demographic, and electoral statistics were available for more than eighteen hundred raions and five hundred municipalities. The development of a large geographic database using multiple sources of information at different levels of aggregation therefore led to problems of accuracy (Brusegard et al, 1989 Devillers et al, 2006 Guptill, 1989). To deal with this practical challenge, we designed and implemented several techniques of error detection and data quality improvement.
A few routines were utilized to detect errors. While converting a layer from paper maps to Albers equal-area projections, gaps in arcs appeared, dangling nodes emerged, and adjacent polygons opened. Missing arcs and label points, more than one label point in a polygon, overshoots and undershoots, and incorrect user-ID values were among the most common errors.
These errors affect attribute accuracy, and unless addressed, the geographic database will not be valid. For example, a polygon that does not have a label point cannot have statistical attributes attached to it. If a polygon is not closed, it will leak into surrounding polygons when trying to shade it in. Software which uses a topological data structure (e.g., ArcGIS) can refuse to accept these not completely “clean” data layers (Chrisman, 2006). To identify errors and fix the geometry, topology construction and edit environment were employed.
These errors also affect positional accuracy. The positional accuracy of raion/municipality layers must be checked first. Two different procedures were used. In the first test, the Krassovsky Spheroid was transformed to the Clarke Spheroid and then to the Albers equal-area projection. This projection is adequate for portraying the Russian Federation, which has a considerable west-east extent. Then the distance between the centroids of two small polygons — Moscow and St. Petersburg — was calculated. In the second test, two separate layers of these cities were digitized in geographic coordinates and transformed into the Albers equal-area projection. These new layers were then paired by overlay (see McAlpine & Cook, 1971 Goodchild, 1978 Burrough & McDonnell, 1998: 237-239) with the Russian Federation raion/municipality-level basic layer and the double representations of Moscow and St. Petersburg were compared. The results were close: the boundaries of each of the paired polygons were highly correlated.
Since consistency allows for recognition of adjacent polygons, it becomes an important element of positional accuracy. To check consistency of polygons (Chrisman, 1982), a visual inspection procedure was performed for raion/municipality units. Because the raion/municipality layer includes over twenty-three hundred areal units for the first project and over twenty-five hundred areal units for the second project, only Moscow, St. Petersburg, Yekaterinburg, and the North Caucasus areas with the most dense boundary networks were inspected. The visual inspection dealt with the shape of lines (positional accuracy), the labeling of polygons (attribute accuracy) and the detection of differences between networks and attributes on the screen and map.
Initially, raion/municipality-level spatial data were produced on the basis of maps published through 1973 and then were updated using maps produced in 1993-2001. The newer maps seem to be of higher accuracy. Logical consistency of representation (Goodchild et al, 2007) is an essential task of data quality. The incorporation of newly created raions and municipalities involved adding, splitting, and sometimes merging initially existing units. For some new units only the geographic coordinates of central places were known. The problem is aggravated by the fact that distorted and incorrect geographic locations of some boundaries and towns are part and parcel of Soviet cartography (Monmonier, 1996). Accordingly, we decided not to use official Soviet gazetteers (Gazetteer, 1970) listing coordinates of towns. It appears that contemporary Russian cartography has not yet escaped the legacy of Soviet secrecy.
Owing to the scale of data aggregation (municipalities and rural raions) these generic limitations on spatial data are not obstacles for such users as academics, researchers and students at universities and teachers and students in high schools. Locations of some cities may be displaced by as much as 5 or even 15 kilometers from the true position. At the same time, boundaries of newly created raions may be imperfectly specified or arbitrarily drawn, as happens in the Chechen and Ingush Republics where civil administration is a fiction. Since boundaries of split raions and approximate locations of administrative centers of newly created raions are known, these centroids can be used for generating a boundary between new raions. Batty and Longley (1994) demonstrate the use of this well-known technique in urban land-use applications where reference points are geometric centroids and zones are regularly shaped. Boyle and Dunn (1990) apply this method to generate unit postcode boundaries comprising the polygon around each address location contained in that postcode.
The polygon construction procedure was used thirty-five times (on 1.5% of raion/municipality units) in RFDD and twenty-eight times (on 1.1% of raion/municipality units) in CEIA. In both projects we struggled in most cases with the same newly created raion-level units. In each project, we recorded information pertaining to the lineage (data life history) (Harding, 2006) of crude boundary estimates in a separate table. This table described the data source, the date of input of the boundary estimate and the date of its revision. Our data suppliers had access to these tables and in several cases found the information helpful in finding spatial information about newly created administrative units. When this spatial information became available we superseded the boundary estimate by the true boundary line in the basic layer and updated the status of the boundary in the lineage table record. Thus, the lineage tables provided information related to the temporal consistency (sequences and updating dates) (Servigne et al, 2006) of our data.
A design for building a geographic database typically involves three components – location, theme, and time (see Sinton, 1978). Each can be used in statistical and cartographic analyses. To measure one of the three components of information, a set of rules for the control of the two other components must be established (Chrisman, 2001 Perepechko et al, 2007). For our layers, completeness deals with the attribute measurement. Completeness of locational data for both layers is controlled by electoral statistics (theme) for the 1995 Duma election (time). In the parliamentary election of 1995, more than 5 million, or 6.7%, of Russia’s votes were not in our databases. Most were from Russian citizens in the former republics of the USSR.
Manning and Brown (2003) point out that many jurisdictions in both developed and developing countries are not ready to adopt the same global reference frame and geodetic datum. The reasons can be political, military, administrative, or technological. For a few raions and municipalities, electoral returns are known but locational information has not been found. Votes in 27 secret cities and military locations (Rowland, 1999) have only attribute properties (see Table 7). These units were missing from maps and could not be used in our projects. Here, we have a special case of errors of omission revealing the special concerns of post-Soviet cartography. These errors of omission indicate that several raion level jurisdictions in the Russian Federation are not ready to adopt any reference frame.
Indeed, some of these secret cities can be found on the latest Russian maps. Nevertheless, Federal Law N 22-F3 of 2009 “On Navigation Activities” (Article 8) (Federalnyy, 2009) still does not allow taking coordinate measurements on territories requiring “special protection measures”.
To be consistent, flexible for upgrading and available for other users, the layers should not be overloaded by excessive small area details. A rule of minimum area was established: while zooming to the oblast-level scale, polygons for raions and municipalities must be unambiguous for visual detection by size and shape. These errors of completeness limit the information.
Table 7. Errors of omission: Russia’s secret cities and military locations, 1995
Secret city or military location
Source: Generated by the authors.
A geographer dealing with Soviet and post-Soviet maps must take into account differences between these cartographic products. Our work with products of two different historical and technological periods has resulted in two major observations.
First, the use of Soviet and post-Soviet cartographic products for the same project affects geographic accuracy of cartographic features. Compared with Soviet maps, post-Soviet cartographic products are more detailed. Raion boundaries are drawn on many maps of post-Soviet Russia’s big regions, but one can hardly find maps of Soviet Russia’s economic regions (comparable in terms of theme and scale) portraying raion/municipality boundaries. Compared with post-Soviet cartographic products, line reduction algorithms for raion/municipality boundaries, rivers and lakeshores are more often used on Soviet maps. Visual hierarchies (Dent et al, 2009) for post-Soviet and Soviet maps also differ. Settlements are more important than raion boundaries and raion boundaries are more important than raion areal units on post-Soviet maps. Raion/municipality areal units are placed higher than settlements, and settlements are placed higher than raion boundaries on Soviet maps. In other words, post-Soviet Russian cartography tends to make boundary line maps while Soviet cartography was disposed to produce area-colored maps.
Second, the use of maps produced in different periods may affect positional accuracy. Here, we deal with Russian and Soviet projections that are a special aspect of the country’s cartographic culture.
On many pre-Revolutionary maps the prime meridian is drawn through the Pulkovo Observatory near St. Petersburg. That avoided having the country’s territory crossed by the 180º. After the revolution, the Soviets joined the international agreement of 1884 whereby the prime meridian was drawn through the Royal Observatory in Greenwich. Primarily in the 1930s, Soviet cartographers created new projections. The Krassovsky Spheroid, a new datum, was adopted in 1942 (Maling, 1960).
To preserve positional accuracy using American GIS technologies when lacking metadata documentation, Soviet projections with unspecified algorithms and with the Krassovsky Spheroid required consultations with our colleagues in Russia, transformations and assumptions. Problems with using the Krassovsky Spheroid and several Russian projections have been solved in more recent ESRI software.
Clearinghouse and geoportal for the UC GI Service
Data quality is always related to the degree of user satisfaction, a result only observed when the product or service is used. Along with suppliers, producers and technological components, the users of the data are the fundamental components of a GI service. Discussions on data quality (Chrisman, 2006 Grant et al, 2003 Hangouët, 2006 Wang et al, 1996) indicate that GI services need to be user driven. To maximize the use of GI, delivery mechanisms must be available, steps to overcome issues relating to restrictions on data availability and access need to be taken, and procedures for maintaining and updating data have to be defined and implemented. Data updating requires relevance, timeliness and completeness which come from actual or possible users and reflect data quality for users.
The use of our data involves a clearinghouse and a geoportal. The Washington State Geospatial Data Archive (WAGDA) is a clearinghouse providing the platform for access to data layers created by the RFDD project in 1994-1998. WAGDA represents the first generation of GI cervices and is accessible worldwide (figure 43). The authors of this paper are copyright holders of RFDD data and hitherto have granted all requests for data download and use for research and educational tasks. Feedback from users through e-mail (e.g., “Boundary error found”), digital on-line feeds, and printed off-line feeds are essential to the mechanism for maintaining and updating RFDD data.
Maguire and Longley (2005 see also Feeney, 2003 Peng et al, 2003 Thompson et al, 2003) define two key differences distinguishing second generation geoportals from their first generation clearinghouse counterparts. First, geoportals make it possible to access both metadata describing services and the actual services (data download, mapping, etc.) themselves. Second, these services can be accessed from desktop GIS applications and a thin client browser. To maximize use and offer more interactions between the user and CEIA data, a geoportal (figure 46) was designed.
Metadata services are built on the functionality of three ESRI products—ArcIMS, ArcCatalog (an ArcGIS application) and ArcSDE. Metadata services use ArcIMS for framework and architecture, ArcCatalog for creating, authoring, and publishing metadata, and ArcSDE as the interface to the relational database that stores published documents. Metadata stored in a metadata server is accessed using Microsoft Internet Explorer. At the beginning of the 2000s these technologies were cutting edge in GIS and we used them in the CEIR project.
Open Geospatial Consortium (OGC) standards (see ESRI Support, 2017) were used because implementation of these standards in the ArcGIS platform are central to ESRI support.
Data provenance is a more general form of lineage (Closa, Masó, Prob, & Pons, 2017). The usefulness of provenance is linked to the level of granularity – the dataset, feature, and attribute levels – at which the data were collected. Data quality of the source data is important because errors introduced by faulty data tend to inflate as they propagate to derived data (Veregin & Lanter, 1995). Provenance about a dataset enables the user to evaluate its quality for their application and to roughly navigate the data. Therefore less fine granularity is needed to describe the provenance of raion and municipality maps. Besides metadata about the raion and municipality source data, transformations applied to create CEIR layers can assist a user to establish the authenticity of the data and avoid spurious sources. Metadata comprising the derivation history of CEIR products was collected as annotations and descriptions about the source data and transformations. This is an eager form of representation in that provenance is pre-computed and readily usable as metadata. We designed a user interface which allows end-users to query provenance information.
Figure 46. Prototype of the CEIR geoportal circa 2003. (Source: Compiled by the authors).
In addition to search functions and mapping capabilities, we planned to add a publishing function to this geoportal. User generated content is crucial for maintaining and updating data for the Russian Federation, a country with semi-closed information systems. Municipal reform implemented in Russia since 2006 radically changes the map at the lowest level of the administrative hierarchy. This reform considerably increases the number of volosts and sometimes touches the level of raions, which becomes the second level of local governments. Since Russian-made spatial data on regions of the Russian Federation remain deficient, the on-line publishing process could have allowed networks of citizens and VGI enthusiasts in Russia to, in a timely manner, detect errors and improve data quality and fill gaps in CEIA data, including omissions or incomplete data, through a web page, and thus could have made this dataset more relevant and complete.
On-line publishing processes require quality control procedures. The quality of content submitted for publishing on the portal web site can be assured by review and approval by Russian geography experts and editing and validation by a special administrator. Because knowledge of Russian regional geography and the CEIA database is crucial to the professional evaluation of the content submitted for publishing, GIS community involvement and collaborative mapping is imperative to the success of the CEIA geoportal.
The development of a geoportal usually follows the building of a database. However, for a country with closed or semi-closed information systems, the building of the database and geoportal can be a concurrent activity. Since more Russian-made GI may become available at any time, maintaining and updating data using an on-line publishing function and digital on-line and printed off-line sources is a never-ending process.
To ensure that the data remain current, the strategy of data maintenance without versions was implemented. This strategy does not entail working with multiple versions but simply makes use of the underlying DBMS transaction model. Nonversioned edits are similar to standard database transactions. Indeed, users and applications that simultaneously access or modify the data may potentially block one another. However the ease of use of this strategy outweighs other considerations.
In a vicious circle of restricted use and discontinued development: obstruction to using, sharing, maintaining and updating data
As some scholars (Devillers et al, 2006 Kabel, 2000 Van Loenen et al, 2004) hold, an advanced technical delivery solution alone may not guarantee effective use of a GI service. The ease with which the user can use data involves other important issues, such as copyright and confidentiality of data sources. When CEIA data layers and the geoportal were almost ready for users worldwide, the decision was made that use of these datasets would be limited to the UC only. In other words, only the universities which provided the initial motivation for the UC GI Service can use these data to fulfil their research and educational tasks. The Service now serves a set of campuses that total 6.9 square kilometers with 88 thousand students, faculty and academic staff. We can only speculate why the decision was made to “block” this unique GI service. From the authors’ point of view, three sets of factors were involved in this decision: 1) data securitization, 2) project management and 3) conflicting ontological visions of the participants.
Data securitization. September 11, 2001, impacted access network and security policies pertaining to GI services. But most likely, the obligations of UC and the U.S. Department of Education to provide access to data were offset by obligations to protect data sources in Russia due to international differences in copyright protection, the end of relative political liberalization and the beginning of recentralization in the Russian Federation. Indeed, some geographic data sources used in this initiative are still not in the public domain in Russia. Consequently, this process has turned into a vicious circle. It cost about $300 thousand to acquire, process and make available worldwide Russia’s difficult–to-access raion-level data sources. Ironically, not unlike India, where the framework (foundation) data of up-to-date and georeferenced administrative boundaries of the entire country are not available (Singh, 2009), we ended up with “classified” raion/municipality boundaries of Russia!
Project management. Dr. Roger F. Tomlinson (2003), commonly referred to as the “father of GIS,” claims that GIS steering committees and the system development teams responsible to them are the key bodies in the implementation of any GI service. Surprisingly to those who remembered that the University of Washington was one of the historical leaders of spatial science, these crucial bodies were not created for the UC GI Service project. Therefore, standard GIS management practice for this $300 thousand initiative did not exist. Then what was there? In the authors’ opinion, three issues were involved in our GIS organization and the business environment in which it operated. First, working with Russia’s GI in fluctuating settings of the Universities’ Consortium is a sensitive political activity. Ironically, segmentation of the labor market and the marginalization of immigrants and foreigners are among acute problems in departments run by Marxists. In the late 1990s and early 2000s some politically correct players in the humanities unleashed “culture wars” on campus. Along with the end of grant money, this new political environment was among the major factors which brought our project to a halt. Secondly, bureaucracy promotes planning and executing some research projects so that they become “free of managers’ errors” (Schalbe, 2002 Thambain, 2005). But this management does not enhance trust, communications, and work ethic among participants on the project. Thirdly, GIS initiatives are not traditionally parts of a library system’s repertory. Setting the UC GI Service in a library system can help to offset the cost of GIS labor but detaches the Service from the GIS community. Yet, GIS community participation and collaborative mapping is imperative to the success of GI services (Tait, 2005).
Clashing ontological perspectives. Interactions in GIS projects usually involve democracy and collaboration (Jankovski et al, 2001), although the GIS environment may sometimes look chaotic, with little in the way of formal structures. There is always geographic theory behind the development and use of GI. Post-Sovietologists were expected to direct the research group, which consisted of GIS professionals and the citizen science participants they trained. Viewing GIS as a tool in the service of post-Soviet theoretical constructs, ideologies and myths made epistemological coherence of the research group barely achievable. As a result, the dichotomous structure of the research group set up a “Huizinga stage” (Huizinga, 1955)–-a game among groups isolated within their own fields and bounded by rules of their own objectives and visions—between the post-Sovietologists and GIS empiricists. Incongruencies between the communicative rationality of post-Sovietologists, on one side, and the instrumentalism of GIS professionals and capabilities locked into software, on the other side, have resulted in an un-updated, in fact abandoned, unique GI service.
We have examined the UC GI Service created in the United States for Russia, a country with semi-closed information systems in terms of public access to data and metadata. In the 1990s and early 2000s, Russian governmental agencies had little to show other than good intentions to share framework data, and our hybrid GI service was created by geographers and citizen science participants to fill the gap in GI at the raion/municipality level. Using the counter-mapping framework and VGI methods, we attempted to create, share, and disseminate framework layers of Russia – administrative and political boundaries (figure 45: A, B) with attached cultural, society, demographic and economic attributes.
This Service involved neither a national data producer nor a formal government mandate. It is a Universities’ Consortium data-producer-led initiative funded by federal, public and private sponsors with goals (1) to create a GIS database using on-line and off-line sources and (2) to disseminate these data to users using a clearinghouse and geoportal. The development of data quality controlling and data updating processes was of central importance for this initiative. The initial proclamation to make these datasets available worldwide was praiseworthy. However, use of the CEIA database is restricted to the Universities’ Consortium. The older RFDD database is available for users worldwide through a freely accessible clearinghouse. It is an inversion of the usual situation in which the second generation of GI services maximizes use and reuse of data compared to the first generation.
The data-supply component of the UC GI Service is located in Russia and operates in an unstable political and institutional environment. The political climate in Russia has defined the process of data acquisition. For RFDD and CEIA we acquired geographic data, usually in printed format, primarily produced by GUGK and TseSeU in the period of reforms (1956-1968) and by Roscartografia, GosKomStat and Central Electoral Committee in the period of relative political liberalization (1991-1999). Newer, often digital, data were frequently obtained from new organizations, regional agencies, private contractors, small mapping businesses, academics and contractors in Russia. On the basis of maps produced in the 1950s and 1960s, a prototype of the raion/municipality-level digital layer for RFDD (1998) was generated first. This prototype was then used to create CEIA (2004). Newer Russian-made maps, books and on-line and off-line GIS products and semi-products, created in 1993-2001, were used as reference sources to check and update raion/municipality boundary networks. This mechanism allowed solving the delicate issue pertaining to the reproduction of the boundaries of raions and municipalities.
The stages of data quality control and updates for the UC GI Service are located in the United States where standards, access network and policy components of GI services are relatively well outlined. Major problems regarding accuracy followed the development of a large geographic database using numerous sources at various levels of aggregation. At the quality control stage positional and attribute errors in the constructed layers were detected and data quality was improved. To check the positional accuracy of the raion/municipality basic layer, tests involving projection transformation, distance measuring and overlay were performed. To detect errors and improve data quality pertaining to attribute accuracy, we employed topology construction, edit environment and visual inspection procedures. The logical consistency of representation involved incorporating newly created raions and municipalities by adding and splitting initially existing units. Completeness of locational data for the layers involves attribute measurement and is controlled by electoral statistics for the 1995 Duma election. Secret cities and military locations represent two special cases of errors of omission.
Spatially adjacent and proximate GI services have been found to have more impact on each other than on services from non-adjacent jurisdictions, and that within each level of the GI service hierarchy (e.g., local, state, or national) services are interconnected and have horizontal relationships with other services at the same level (Warnest et al, 2010). Therefore, methods of data quality control adopted for the RFDD and CEIA databases may facilitate exchanging, sharing and integrating our spatial data with datasets produced in the European Union. Indeed, our raion/municipality polygon-based data and the respective jurisdictions of Finland, Estonia and Latvia are spatially adjacent and proximate.
Limited use of the CEIA database and its blocked geoportal are the biggest bottlenecks in the UC GI Service. Data availability and data updating require relevancy, timeliness and completeness based on input from actual or possible users – important components of data quality for users. Also, limiting the number of potential users impairs the data updating and quality control mechanisms. Although the data use component of the Service involves US based standards, access network and policy, it also echoes changes in the Russian political and institutional environment. The end of a pro-Western swing in Russia’s politics required the protection of data sources in Russia and impacted data availability. Because the international environment plays an increasing role with respect to access to and use of GI, data protection, and protection of the individual, UC should investigate ways to exploit this trend in order to unblock the blocked CEIA database and geoportal. The absence of new organizational arrangements for the UC GI Service seems to be an important obstacle for data updating and further development of the Service. If the shortcomings in data use are left unaddressed, the UC GI Service will degrade over time into a corporate database and may even become irrelevant to users.
The emerging lesson is that constraints limiting the effective implementation and use of the UC GI Service have less to do with GIS technologies and technical infrastructure than with international differences in copyright protection, changes in Russia’s politics, and problems of organization of the research group. We face a paradox when more advanced technology – the geoportal versus the clearinghouse – is blocked by corporate, institutional and cultural barriers and cannot resolve issues of data accessibility, updating and use.
In 2006 the Russian Government issued Order №1157-r, which approved the concept of creation of NSDI in the Russian Federation (Concept, 2010). A draft of the national standard for spatial data (GOST r 53339-2009) was adopted (Manylov, 2010). The State Registration Inventory and Mapping (Rosreestr), the Russian federal agency responsible for the development of this NSDI, was created under the auspices of the Ministry of Economic Development.
There are reports (Vandysheva et al, 2010) about spatial data infrastructure pilot projects in Russia. The implementation stage (2009-2015) of the NSDI in Russia took about six years. The creation of the NSDI was authorized by Federal Law No. 431-FZ of the Russian Federation in 2015 (Federal Law, 2015) and has provided a policy umbrella for GI in Russia for the past 2 years. This law opens an opportunity for Russia to leave the group of countries with closed or semi-closed information systems. Two examples below illustrate new developments in GI services in Russia.
Figure 45 (D) illustrates Russia’s GI service kosmosnimki.ru which is a subsidiary of Russia’s SCANEX Holding. SCANEX specializes in satellite monitoring. In kosmosnimki.ru a user can combine 1) several Wikimapia (a privately owned open-content collaborative mapping project) framework layers (C) and 2) cadastral digital data from Russia’s NMA Rosreestr and satellite imagery from SCANEX. The GIS platform GeoMixer for kosmosnimki.ru was developed by SCANEX. GeoMixer allows a user to manipulate GI using the Internet and local Web. GeoMixer includes the application program interface (API).
Figure 47. A sample of the boundary map of Russia circa 2015. Postal codes/municipality boundaries in the Solntsevo area of Moscow (Source: Courtesy of Kantynent Infarmatsyynyya Tekhnalogii).
Today a user can download boundary maps of Russia (in .shp, .prj, .dbf, and .shx formats) from Kantynent Infarmatsyynyya Tekhnalogii (Continent Information Technologies) (figure 47), a privately-owned spatial data manufacturer located in Minsk, Belarus. The raion map of Russia released in 2015 would cost about $2000 and the municipality map about $4000.
In the meantime the UC GI Service has been disabled (Central Eurasian, 2004) and a unique source of Russia’s geographic, socioeconomic, demographic and political information cannot be located.
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8.0 GROUNDWATER DATA MANAGEMENT AND ANALYSIS TOOLS
Following are some of the software packages used for groundwater data management and analysis.
AQTESOLV: Design and analysis of aquifer tests including pumping tests, step-drawdown tests, variable-rate tests, recovery tests, single-well tests, slug tests and constant-head tests.
AquaChem: AquaChem is an integrated software package developed specifically for graphical and numerical analysis of geochemical data sets. It features a database of common geochemical parameters that can be customized and configured to include an unlimited number of attributes per sample. The built-in analysis tools include many common calculations used for analyzing, interpreting and comparing aqueous geochemical data. The built-in graphics include many common geochemical plotting methods such as Piper, Stiff, Durov, Radial, Schoeller, Langlier-Ludwig and more. AquaChem also includes a direct link to the popular PHREEQC program for geochemical modeling.
AquiferTest Pro: Graphical analysis and reporting of pumping test and slug test data.
AquiferWin32: A software system for analysis and display of aquifer test results.
Arc Hydro Groundwater: Arc Hydro Groundwater (AHGW) tools include Groundwater Analyst, MODFLOW Analyst, and Subsurface Analyst. It can access and visualize your groundwater, time series, and geologic data within ArcGIS.
ChemStat: RCRA compliant statistical analysis of groundwater data. It includes most methods described in 1989 and 1992 USEPA statistical guidance documents.
EnviroInsite: EnviroInsite is a desktop, groundwater visualization package for analysis and communication of spatial and temporal trends in multi-analyte, environmental groundwater data. It facilitates the generation of data queries to generate time history graphs, pie charts, radial diagrams, data tables and dot-plots in plan, on vertical profiles, and in 3D views.
Environmental Visualization System (EVS): Unites advanced gridding, geostatistical analysis, and fully three-dimensional visualization tools into a software system developed to address the needs of all earth science disciplines. The graphical user interface is integrated with modular analysis and graphics routines. The more advanced versions allow these modules to be customized and combined. The software can be used to analyze all types of analytes and geophysical data in any environment (e.g. soil, groundwater, surface water, air, etc.). It includes integrated geostatistics.
EQWin Data Manager: EQWin Data Manager is a database application used to validate, store, analyze, and report on environmental data. Using either MS Access or SQL Server databases, EQWin manages many frequently monitored entities, including ground/surface water, soil, air, and meteorological data. EQWin is integrated with Microsoft Excel as its default import/reporting tool.
GW Contour: Data interpolation and contouring program for groundwater professionals that also incorporates mapping velocity vectors and particle tracks.
GW-Base: GW-Base was developed for expedient evaluation of groundwater information for use in water management, monitoring, investigation and remediation of damage cases. It combines a powerful database with easy-to-use evaluation tools like contour maps, bar‑charts, pie-charts, statistical tools, query options and GIS-functionalities.
HydroGeo Analyst: Groundwater and borehole data management and visualization technology.
LogPlot: Log plotting software for the environmental, petroleum, mining, and academic geoscientist.
PUMPTEST (IGWMC): The PUMPTEST program package is a menu-driven set of independently run programs. It includes three different methods to analyze pumping test data: the time-drawdown method (JACOBFIT) distance-drawdown method (DISTANCE) and recovery method (RECOVERY).
RockWorks: Geological data management, analysis and visualization.
Strater: Strater is a well log and borehole plotting software program that imports data from a multitude of sources (database files, data files, LAS files, ODBC, and OLE DB data sources). Strater provides innumerable ways to graphically display the data. All the logs are fully customizable.
WinLoG: WinLoG can be used to quickly create, edit and print geotechnical, environmental, mining, and oil & gas borehole and well logs. The graphical windows interface displays the log as it is changed and shows exactly how the log will look when it is printed.
Services to the Community
The EGS Industrial Minerals Department offers an advisory and consulting service to investors by providing information on the location and availability of industrial minerals within Egypt, and by carrying out contracted exploration and evaluation projects. Three levels of service are offered:
1. Information and advice on known prospects based on existing information. The product consists of a short report, packaged with available published or open file reports, maps and data.
2. Field visits, carried out by experienced EGS staff, to demonstrate prospects on the ground to potential investors and carry out additional sampling as directed.
3. Resource evaluation projects to survey individual prospects, determine quality and quantity, and provide feasibility assessments for future mining operations. These projects are usually carried out in partnership with the EGS Mining Development Department.
The objective of the EGS Mining Development program is to increase the contribution made by the Egyptian mining sector to the growth and diversification of the national economy, reduce reliance on imported raw materials, and encourage safe and environmentally sensitive development of mining operations in Egypt. We do this by:
Carrying out pre-feasibility studies of mineral resource locations using extensive drilling campaigns and ore processing tests in the laboratory and on-site
Advising on investment opportunities and market requirements for specific mineral commodities
Consulting on all aspects of exploitation methods and infrastructure, including mine waste management, environmental impacts and after-use options
Testing ores to refine processing methods and investigate new raw materials for industrial processes
Compiling and publishing national mining statistics for Egypt including information on mine locations and reserves, and on source, consumption and demand for each commodity.
The Information Revolution provides ever-growing opportunities to develop new tools and methodologies for geoscience surveys and research, new ways of handling, sharing and visualizing our data, and new ways of delivering knowledge and services to a global audience. In EGS, Information Technology underpins the management and publication of all our data and information, and is enhancing and diversifying the capability of all our geoscience programs, support services and administrative functions. In support of our geoscience programs, the main objectives of the Information Technology program are to:-
Compile, document and update geologic databases for mineral localities and drilling sites, and to archive sampling and analytical data for rock, soil and hydrogeologic investigations.
Establish a comprehensive digital database of geologic survey, exploration and applied geology data, and design and compile Geographic Information Systems for data analysis and delivery to customers.
Process satellite images and Thematic Mapper digital data for use by our survey, exploration and applied geology programs
Develop new systems and databases for primary digital recording of data by fieldworkers
Integrated Geoscience Database
Development and population of the EGS Integrated Geoscience Database is a major strategic project in EGS with the objective of digitally capturing, preserving and indexing the work of our predecessor organizations, and merging this with new, digital geoscience data collected by current EGS programs. The resulting database will serve future clients and programs of EGS with comprehensive and inter-operable digital geoscience data that can be visualized and delivered in a wide variety of user-specified formats and themes, and can be browsed and supplied to the community via electronic delivery
The Integrated Geoscience Database project has two main components:
Development and management of the Integrated Geoscience Database is emphasizing inter-operability of datasets to ensure wide application and exchange of information between the EGS and other organizations with similar interests, especially in management of groundwater, natural hazards, and mineral resources. Data structures are compatible with a variety of proprietary output software including GIS, statistical and borehole logging packages.
The database is managed using Microsoft SQL Server and built around a single relational data model with 12 interlinked domains and over 450 individual data tables. The domains reflect key applications of EGS information such as mineral occurrences, groundwater, and natural hazards, as well as survey activities such as mapping, drilling, geophysics, and chemical analyses. The principal domains are:
Geological Map Database
Geological Observations Database
Geophysical Survey Database
Geochemical Survey Databse
Water Resources Database
Borehole and Drilling Database
Geographic Information Systems
The EGS GIS department is responsible for digital capture and attribution of all maps produced by EGS survey and exploration programs. The department is well equipped with workstations running the latest ESRI ArcGIS 8 software. All new EGS printed maps are now produced by digital methods.
In addition to providing GIS data capture and digital map production services for current EGS projects, the department is also carrying out a number of strategic digitization and development projects.
This project is developing an integrated, relational data model and database for future management and delivery of GIS data by EGS. The data model will standardize the basic attribution and symbolization of point, line and area features on EGS maps to ensure seamlessness of GIS data coverage for the entire Kingdom. Links will be created to other attributes tables that describe a wide range of geological, environmental and resource properties. The GIS database will be incorporated into the EGS Integrated Geoscience Database to unify all EGS geoscience data and information within a single data management system.
This ongoing project has the objective of systematically digitizing and attributing all existing printed geological and topographic map coverage of Egypt, with the aim of building an integrated and seamless national databank of digital mapping. Current work is concentrating on the 1:250,000 scale geological and topographic maps, and is being prioritized to ensure that digital map and GIS coverage of the more densely populated and resource-rich parts of Egypt is completed during the early stages of the project
Spectral imagery data covering the thermal-infrared, infrared, visible, ultraviolet, radar, and gamma -ray wavelengths, obtained by sensors mounted in aircraft or satellites, provide a unique perspective and information about the composition and structure of rocks and other materials exposed at the earth&rsquos surface. Remote sensing imagery is especially valuable as a geologic survey tool in sparsely populated arid areas, and its interpretation is therefore an indispensable step in all EGS geologic mapping, mineral reconnaissance and geohazard assessment projects.
The Remote Sensing section offers services and training to EGS staff in the enhancement and interpretation of satellite data, and provides a range of hard-copy and digital products both for EGS internal project use and for external clients. Imagery is processed, enhanced and delivered using ERDAS 8.4 software.
Services to the community
EGS supplies remote sensing imagery mosaics to a wide client base in the government, industry, defense and education sectors.
Satellite imagery/maps can be delivered in a variety of paper sizes (A0, A1, A3, A4 or user defined) and types. Coated or glossy paper is more suited for posters and displays, whereas light coated paper is recommended for desk interpretation and fieldwork.
The Unit also offers training workshops for Earth Sciences students. The workshops familiarize trainees with the latest remote sensing technologies and provide practical exercises to demonstrate applications.
Publications and outreach
The EGS Publications department publishes the results of EGS programs, ensuring that these outputs are peer reviewed and edited to the highest scientific standards. It also manages the EGS library and maintains stocks of EGS maps, reports and books for external sale. Our main activities are:
Providing the technical and editorial support, including arrangement of peer review, to produce high quality geoscientific maps and reports in-house
Preparing educational and promotional material for publication and for conferences, exhibitions and seminars
Producing printed aerial photographs and topographic maps on stable and robust media for use by EGS fieldworkers
The Egyptian Geological Survey maintains a scientific, technical and professional Earth-Science library at the EGS Cairo office. It is one of the largest of its kind in Egypt and is an important source of published and on-line Earth-science information for EGS staff and visitors, the Government and private sectors, mining companies, Universities, and Research Centers. It also houses archives of original field notes, manuscripts, and analytic data for EGS and earlier exploration and geologic projects.
The library contains a wide range of data and documents and a varied collection of geologic books and journals. The available materials are:
Technical and Open-File Reports
Geological, Geophysical, and Topographic Maps
Professional and Research Papers
Booklets, Pamphlets, and Brochures
Major international geoscientific journals
On-line bibliographic databases.
The Egyptian Geological Survey and its predecessor organizations have produced hundreds of geologic maps, at various scales, over the preceding 4 decades. Most of these map products are for sale as hard-copy documents. A limited number of the newest map products can be obtained in digital formats.
The &ldquoGM map series&rdquo (Geologic Map Series) at 1:250,000 scale is the premier geologic map series of the EGS. This is an ongoing series of color printed maps, with accompanying explanatory notes, that will eventually cover the entire Egypt. The maps are 1 degree by 1.5 degree quadrangles, and conform to the 1:250,000-scale topographic map boundaries of the State..
In many cases, particularly in coverages of the GM-map products are compilations built on field mapping programs carried out at 1:100,000 scale. Many of the 1:100,000-scale maps are available for purchase although, as base datasets, these maps are part of reports of varying size and complexity and are individually priced.
In addition to the geologic maps available at 1:250,000 and 1:100,000 scales, EGS also has available a number of country-wide maps, shield-wide maps (at 1:1 million scale), and a range of specialized maps covering geology, aeromagnetic coverages, industrial minerals, etc.
The Egyptian Geological Survey (EGS) has produced several types of technical reports, which can be accessed through the following links:
EGS has an extensive range of training programs with emphasis on developing the geoscientific, information technology, managerial and English-language skills of its staff. Training comprises a combination of specific job-related courses, seminars and lectures, and participation in local and international conferences. EGS trainees also participate extensively in externally-provided courses on a part-time or time-release basis, and in geoscientific courses and seminars provided by staff from our partner organizations and other collaborators.
Laboratories and logistics
EGS maintains laboratory and logistical facilities to support its own survey and exploration programs and as services to external customers in the government, research and industry sectors.
EGS laboratories prepare and analyse rock, mineral, soil and water samples for a variety of downstream geoscientific, engineering and industrial applications. All our laboratories carry appropriate accreditation, and samples and analyses are prepared according to the applicable industry and research standards. Our laboratories are:
The EGS Chemical Laboratory carries out chemical analyses to provide baseline knowledge of the geochemistry of the rocks, soils and sediments of Saudi Arabia and its adjacent offshore territories. The analyses have applications for geologic mapping, metallic mineral exploration and the identification of sources and pathways for natural and man-made contamination of the environment.