How to convert coverage to shapefile?
I'm using ArcMap 10.1 I recently downloading some files that are in .adf format.
I need to convert them to .shp.
I just simply need step by step instructions of how to convert the data.
you can use quick import tool in data interoperabilty toolbox to covert your data to arcgis geodatabase then expot those feature class to shape file.
Based on ESRI document you can convert by Feature Class To Feature Class tool:
"Converts a shapefile, coverage feature class, or geodatabase feature class to a shapefile or geodatabase feature class"
example: If you want convert polygon in coverage to shapefile polygon
The answer lies in ArcCatalog. Put all the coverage files in one folder, select all the ones you want to change into shapefiles, right click, export --> shapefile (multiple), then select the output folder and OK. It takes a while but it's so easy!
How to convert coverage to shapefile? - Geographic Information Systems
Количество зарегистрированных учащихся: 17 тыс.
In this course, you will learn how to find GIS data for your own projects, and how to create a well-designed map that effectively communicates your message. The first section focuses on the basic building blocks of GIS data, so that you know what types of GIS files exist, and the implications of choosing one type over another. Next, we'll discuss metadata (which is information about a data set) so you know how to evaluate a data set before you decide to use it, as well as preparing data by merging and clipping files as needed. We'll then talk about how to take non-GIS data, such as a list of addresses, and convert it into "mappable" data using geocoding. Finally, you'll learn about how to take data that you have found and design a map using cartographic principles. In the course project, you will find your own data and create your own quantitative map. Note: software is not provided for this course.
Geographic Information System (GIS), Cartography, Esri, Mapping, Spatial Analysis
A nice and well explained course again. i think if some sample data can be supplied to the trainees when you are demonstrating something in ArcGIS it would be more helpful to practice.
I received professional skills and it will develop my career , may thanks Coursera team for their hardwork and well arranged and designed courses both theory and practical
An overview of the Conversion toolbox
The Conversion toolbox contains tools that convert data between various formats.
The Excel toolset contains tools to convert Microsoft Excel files to and from tables.
The GPS toolset contains tools to convert files from GPS receivers to features. GPX is a common file output from GPS handheld collection units.
The KML toolset contains tools to convert from Keyhole Markup Language (KML) to features in a geodatabase.
The From PDF toolset contains tools that will export a PDF file to a Tagged Image File Format (TIFF).
With the tools in the From Raster toolset, you can convert the information in a raster dataset to a different type of data structure, such as a feature class, or to a different type of file, such as a binary or text file.
This toolset provides a tool to convert the features from WFS into a feature class to provide more functionality for those features.
All ArcGIS items have a description, which is also referred to as metadata. The Metadata toolset lets you handle metadata for ArcGIS items and stand-alone metadata XML files.
Tools in the To CAD toolset convert geodatabase features to native CAD formats. You can use these tools in geoprocessing models and scripts to define your own conversion procedures.
COLLADA—which stands for COLLAborative Design Activity—is an open-standard XML format for storing 3D models. It is often used as an interchange format for 3D applications, and is the format for 3D textured objects stored inside KML. COLLADA files have the .dae file extension and can reference additional image files that act as textures draped onto the 3D object. Exporting multipatch features to COLLADA allows the sharing of complex analysis results with others and also provides a mechanism for updating textured 3D GIS data, such as buildings, using third-party software such as SketchUp or 3DS Max.
Coverages combine spatial data and attribute data and store topological associations among features. Spatial data is held in binary files, and attribute and topological data is held in INFO tables.
dBASE tables are used to store attributes that can be joined to shapefile features by an attribute key. The Table to dBASE tool can be used to migrate INFO tables or even other dBASE tables so that they can be used by specific shapefiles.
The To Geodatabase toolset contains tools to convert and write data to a geodatabase.
The To GeoPackage toolset contains a tool to convert datasets into the OGC GeoPackage format.
Keyhole Markup Language (KML) is an XML-based language provided by Google for defining the graphic display of spatial data in applications such as Google Earth and Google Maps. KML enables these applications to support the open integration of custom data layers from many GIS users.
Raster information can be stored in several different data file formats that can be read by ArcGIS. With the To Raster toolset, you can convert these files into raster datasets. There are also tools that allow you to convert different types of feature information into rasters.
A shapefile is a simple, nontopological format for storing the geometric location and attribute information of geographic features. Geographic features in a shapefile can be represented by points, lines, or polygons (areas).
How to convert coverage to shapefile? - Geographic Information Systems
Downloadable KGS resources for karst mapping and geographic information systems:
&ldquoKarst Occurrence in Kentucky,&rdquo KGS Map and Chart 33 (series 12): digitized from 1:500,000-scale geologic map
Sinkhole coverage for the karst areas of Kentucky (compiled by Kentucky Speleological Survey)
Karst groundwater basin maps: Beaver Dam, Campbellsville, Bowling Green, Lexington, Harrodsburg, and Somerset 30 x 60 minute quadrangles
&ldquoInventory of Karst Springs of Fayette County,&rdquo KGS Map and Chart 28 (series 12)
Kentucky generalized karst block diagrams : KGS Map and Chart 15, 16, 17, and 18 (series 12)
&ldquoProtecting Kentucky's Karst Aquifers from Nonpoint-Source Pollution,&rdquo KGS Map and Chart 27 (series 12)
Karst-related KGS reports
- "Kentucky Is Karst Country: What You Should Know About Sinkholes and Springs&rdquo (Information Circular 4, series 12)
- &ldquoMass Flux of Agricultural Nonpoint- Source Pollutants in a Conduit- Flow- Dominated Karst Aquifer, Logan County, Kentucky&rdquo (Report of Investigations 1, series 12)
- &ldquoElectrical Resistivity Studies in the Inner Bluegrass Karst Region, Kentucky&rdquo (Thesis 1, series 12)
- &ldquoFlooding of Sinking Creek Karst Area in Jessamine and Woodford Counties, Kentucky&rdquo (Report of Investigations 7, series 12)
- &ldquoOrdinance for the Control of Urban Development in Sinkhole Areas in the Blue Grass Karst Region, Lexington, Kentucky&rdquo (Report of Investigations 29, series 11)
- &ldquoKentucky Geological Survey Procedures for Groundwater Tracing Using Fluorescent Dyes&rdquo (Information Circular 26, series 12)
The Kentucky Geological Survey does not maintain data on caves. For scientific, engineering, or cave surveying information, contact the Kentucky Speleological Survey, a nonprofit organization formed by cavers in 2000. For general caving and safety training, contact your local chapter of the National Speleological Society.
We provide support to the HyDS (Hydrogen Deployment System Model), a computer model of U.S. market expansion of hydrogen production from wind and other sources over the next 50 years.
This hydrogen data estimates the potential for producing hydrogen from onshore wind, solar photovoltaic, and biomass energy by county for the United States.
The zipped shapefile data set is designed to be used in geographic information system software applications.
This study found that approximately 1 billion metric tons of hydrogen could be produced annually from wind, solar, and biomass resources in the United States. The greatest potential for producing hydrogen from these resources is in the Great Plains region.
Nationwide Parcel Data
Dynamo Spatial's Pinpoint Parcel products represent over 151 million properties across 2,941 counties which covers 98.6% of the U.S. Population.
Parcel data and parcel GIS(Geographic Information Systems) layers are often an essential piece of many different projects and processes. With the help of our cadastral data, many characteristics of real estate and mineral properties can be visualized and analyzed over a broad area of interest.
Dynamo Spatial's first-class parcel layers and property records contain deep data attributes about property valuations, legal descriptions, land ownership, service areas, census statistics, environmental conditions and much more in addition to the parcel boundaries. Through spatial analysis, our parcel gis may also be used to increase the value of other reference layers, with methods such as intersection, proximity, buffer and overlay functions.
Each day we help companies find new efficiencies and money saving advantages by providing the highest quality parcel GIS and property record data with our fast and easy download process. We provide the parcel data in ESRI Shapefile(SHP file) format but we can also convert it into a number of industry standard formats for easy import into virtually any third-party software application. Examples include: ESRI ArcMap, LandWorks GIS, Quorum Land System, OGSYS, P2 Tobin Products, Autodesk Map, LandBoss, Enertia Products, GeoGraphix, Petra, ILandMan and more.
Here are some of the industries that have found parcel data to be an indispensable resource in their daily operations:
What is GIS?
GIS is the acronym for Geographic Information Systems which simply stands for a computer-based system for the compilation, management, analysis, and display of geographic information. The system comprises mainly of computer hardware and software, geographic data, procedures or techniques and skilled personnel.
In GIS, real-world features captured as data are represented as either vector or raster (imagery) formats. These geographically-referenced data are often associated with some extra descriptive information described as attributes. A GIS application displays datasets as layers each layer represents a particular feature of interest captured from the real world: eg. buildings, transport networks, population, political boundaries, rivers, elevation etc. A GIS analysis essentially is based on either the geographic relationship among the layers or their attribute information.
For some basic tutorials or a general overview of GIS, you may visit The Beginner's Guide to GIS .
What can GIS do?
GIS is known to have many benefits for all levels of organizations: government, private, commercial, industrial etc. The economic and strategic value of GIS, particularly, have led to a surge in interest and awareness about GIS worldwide. Due to its emphasis on location, its applicability is vast and almost ubiquitous. Some benefits or usefulness of GIS can broadly be described under the following themes:
1. Efficient decision making. GIS provides efficient ways in deciding where, how and why to locate facilities or services. This is important because the suitability of a location for a facility or service is key in determining such feature's success. GIS has been widely used in making decisions concerning issues like zoning, community planning, resource allocation, health-care access, conservation, route selection/planning etc. The efficiency brought into decision-making processes ultimately leads to huge cost saving outcomes for both government entities and private corporations.
2. Better communication of information. The ability to visualize geographic data with their associated attribute information through GIS also enhances people&rsquos understanding of locational problems. GIS can be used to display geographic data and also maps.
3. Cost efficiency. The comprehensive nature of GIS (several separate functions put together) helps cut down the cost of acquiring various tools or functions. A myriad of separate analysis can all be carried out within a single GIS platform. This advantage eliminates extra financial or time cost that may be incurred in procuring tools for each separate analysis.
4. Better data management. GIS offers a relatively easy way of manipulating, managing and storing datasets. For example, data can be edited, queried, integrated or merged, copied, categorized and even converted into other formats within GIS. These capabilities enhance productivity.
Shapefiles can be visualized in SMS as well as be converted to feature objects or scatter data. This can be done by using either the Shapes → Feature Objects or Polygons → TIN command in the Mapping menu. It is important to check for bad polygons when converting shapefile data. These may be polygons with zero area or with duplicate nodes. This problem can be fixed by using the Clean command in the Feature Objects menu. If using the Clean option does not fix the problem initially, try increasing the tolerance until all problematic feature objects are removed.
For additional information, see Importing Shapefiles.
This tool can convert a dataset from a spherical coordinate system with angular units (such as Geographic) to a planar coordinate system with linear units. Most Coverage tools, among them Build and Clean , assume you have a planar, two-dimensional dataset . So if your dataset is in a geographic coordinate system in decimal degrees (DD, angular units), the Project tool projects your dataset to any suitable projected coordinate system in linear units (meters or feet).
A coverage can maintain an explicit definition of the coordinate system in which it is stored. This can be created using the Define Projection tool. If not defined, the projection will be listed as unknown.
Output projection information can be specified using a Project File or from an empty output coverage. The Project File must contain both input and output projection definitions. Use of a Project File will override any projection information stored in the data's PRJ file.
Clarke 1866 is the default spheroid if it is not inherent to the projection (such as NEWZEALAND_GRID).
Do not name an output file the same as the Project File, even if the Project File has a .prj extension.
When projecting a coverage, the Output Coverage can be an existing, empty coverage. The coordinates of the Input Coverage will be projected into the coordinate system defined by the PRJ file of the Output Coverage.
Depending on the input and output projection definitions, an arc in the input coverage may need to be clipped into more than one segment while the output coverage is being generated. This will occur whenever an arc encounters the horizon line or crosses the line of longitude opposite the central meridian.
Whenever a vertex is encountered that cannot be projected, the previous vertex will be interpreted as the end of an arc, and the partially projected arc will be written to the output. It is possible for an arc to be split into several arcs if subsequent vertices are encountered that can be projected. In this case, the output retains the original IDs so attributes can be relinked. Examine this illustration arcs 2 and 3 will be clipped by the horizon during projection of the line. The output coverage will contain one arc 2 but two arc 3s. In cases such as these, Project will generate arcs having duplicate User-IDs.
If regions exist in the input coverage, regions in the output coverage will be preliminary regions. When the Build tool is used to re-create the polygon topology, region topology will also be re-created.
To find tables of predefined geographic coordinate system , projected coordinate system , and geographic (datum) transformations, see An overview of map projections.
But the biggest advantage the PostGIS reader gives is the ability to have dynamic costs for the road network.
GraphHopper requires the following OSM columns when creating it native network data (please refer to OSM documentation for the proper values of these columns) and is thus required by the PostGIS data reader for the conversion process :
GraphHopper will use the values of maxspeed, oneway, and fclass to determine the cost of the route and/or create routing profiles. Now since the PostGIS data reader can use SQL Views as well as Tables, these columns can be derived from other tables linked together by SQL when creating a View.
This will create a View, myroad_view, that can be converted into GraphHopper. Since all the data will have the same maxspeed as 0 and all fclass as tertiary values, the resulting network will always give only a Shortest Path for all route queries.
In the case of the DRM data, the SQL below is used to match the road classification of the DRM with that of OSM’s fclass:
Since the speed is all set to 0 value, the searches will just use the road classifications as the value for the cost when creating a search path.
Now if there are traffic information in an another table, another View can be created that links the traffic information table with the road network table via SQL that gives the maxspeed a higher value for roads that have less traffic and lower values that have more traffic. This will give priority to roads with less traffic when doing path searches.
So again as a retrospect, with the ability to use other information as cost, which goes beyond the basic OSM attributes, the PostGIS data reader provides more interesting applications that can be created for GraphHopper.