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Collect Values + Model Builder ArcGIS 10.1

Collect Values + Model Builder ArcGIS 10.1


I have a question relating to the use of Collect Values in Model Builder.

I've just come across this post (Piping output of Iterate Raster tool into Raster Calculator tool using ModelBuilder?) and it's pretty much the same as what I want to do. I'm also adding in a user defined weighting to the iteration and then attempting to collect the values before running the cell statistics. The problem is that I'm not getting a set of unique outputs at the Output Values stage and it only return results for the last original rasters.

Any idea where I'm going wrong with this?


Where do you like to put MVC view model data transformation logic?

Here is the scenario, I need to load up a view model object from a several domain objects that are being returned from multiple web service service calls. The code that transforms the domain model objects into the digestible view model object is a bit of fairly complex code. The three places that I have thought about putting it are:

  1. Internal methods inside of the controller to load up an instance view model.
  2. Static get method or property on the view model class itself that returns a loaded instance of view model.
  3. An altogether separate builder or helper class that has a Static get method, property, or overloaded constructor that returns the loaded instance of view model.

To be clear, I don't want to use AutoMapper, or tools like it. From a best practices standpoint I'd like to know where this logic should go, and why.

So here is what I have so far, this does give me "skinny" controller logic and separation of concerns. How can I make it better though?


Data Literacies

The amount of data produced, the democratization of data, and the increase of data tools have increased the need for data literacy among the general population and in organizations. Data literacy means the ability to find, evaluate the source and quality of the data, be able to understand the data, manipulate it, ask questions of it, make an argument from it and assess the arguments of others.

Raul Bhargava from MIT, and Catherine D'ignazio from Emerson College (Knight, 2017) divides data literacy into four components:

Reading the data: Comprehending data in various forms and being able to read the language of data.

Working with the data: People work with data in various forms which depend on the role of the person. Is the person a student, statistician, or data visualization expert? Each of these roles work with data in a different way.

Analyze the Data: Using various skills to analyze the data. The specific skills that are used to analyze the data depend on the goals of the person analyzing the data. This might range from analyzing the data for basic summary statistics to creating machine learning models with the data.

Argue with the Data: Using the data in order to support your idea or research.

Examples of data literacy skills

  • Evaluating the quality of data sources to be used in analysis.
  • Being able to interpret data visualizations such as histograms or scatter plots.
  • Communicating the results of a data analysis or visualization to a general audience.

The Data Lifecycle

Stage 1: Pre-project

Idea - You have generated an idea, found collaborators and began thinking about what to do.

Planning - This essential stage impacts every other stage.

  • Developing conventions
  • Identify storage - know your requirements, and what the storage offers
  • Keep best practices in mind for your whole project
  • You might consider pre-registering (specifying your research before you start your study and submitting it to a registry) your project in this stage
  • Apply for funding, identify tools, methods, and potential data sources

Stage 2: Active

Collection - In this stage, researchers gather data and other materials key to the project.

  • Understanding how to set up tools for accurate collection
  • Being able to identify reputable sources for data finding
  • Understanding the previous methods used to collect or create the data you find
  • Documenting primary (collected by you) and secondary (collected by others)
  • You will make those provisions so that those need to work with them in a structured way.

Stage 3: Explore

Wrangle - You prepare the data for analysis

  • Being able to use or reuse data depends on understanding what you have
  • Variable definition, expected values, relationships, units, etc.
  • Document your methods, code, decisions, this is key to understanding and explaining your findings and sharing with others
  • You use tools to look for relationships and structures within data

Stage 4: Results

Visualize - You create graphical representation of numbers, examine how to communicate the structures

  • Knowing the most appropriate and impactful way to communicate requires an understanding of what different visualization methods do
  • Choosing the appropriate variables in your data for the visualization
  • Understand the power of color and symbols
  • Citing sources used

Interpret - You articulate your preferences what data relationships tell us


How do culture and other factors affect the leadership of a community?

The information above showed that culture and other factors (social, economic, historical, and political) have an effect on the way a community organizes itself for self-help and support. The same can be said about leadership. There are different levels and types of leaders that support the social organization of a community. Sometimes, we make the mistake of assuming that there is only one leader in a community or that a leader has to look a certain way. Just as we respect and value the cultural diversity of communities, we have to respect and value the diversity of leadership.

What qualities do you think a leader should have?

In every ethnic or cultural group there are different individuals who are regarded as leaders by members of the group. Every leader has a place and a role in his or her community. Leaders can be categorized by type (e.g., political, religious, social), by issue (e.g., health, education, economic development), by rank (e.g., president, vice president), by place (e.g., neighborhood block, county, city, state, country), by age (e.g., elderly, youth), and so on.

Let's use the same communities described before. In Chinese communities, the leader is typically the head of the family. If family refers to a grandfather, father, mother, sons and daughters, and grandchildren, then the leader is the grandfather. If family refers to the congregation of a church, the leader is the pastor. If family refers to a clan, the leader is the President of the clan's association (or hui guan).

In African American communities, the leader is typically a spiritual leader. A leader can also be someone who is successful in overcoming the barriers of institutionalized racism and provide opportunities for other African Americans to be treated equally by others in the mainstream society (e.g., a business person, an educator, or an elected official).

In Central American communities, the leader is also typically a spiritual leader. It can also be the coach of a soccer team or the president of an association that links a city in Central America with one in another country.

What do all these leaders have in common?

They provide guidance, they have influence over others, others respect them, they respond to the needs of others, and they put the welfare of others above their own. Every leader serves a specific function within the social organization of a community however, the same type of leader in one community does not necessarily have the same role in another community. For example, a spiritual leader in a Chinese community is not regarded as a political leader, as he might be in the African American community.

What did you learn from the above information and examples?

How can you, the community builder, learn about the social organization of other ethnic and cultural groups?

  • Go into the process with an open mind.
  • Don't assume that the same leader, organization, or institution serves the same function across groups.
  • Keep in mind that the social organization and leadership of a group is influenced by its culture, history, reasons for migration, geographic proximity to its homeland, economic success, intra-group tensions, and the way it fits into the political and social context of its new and surrounding society.
  • Look for the formal and informal networks.
  • Interview members of a group and ask where and whom they go to for help or when they have a problem.

Keep in mind : Among different groups, the church has different functions. For example, Korean and Chinese churches do not have strong political functions compared to Latino or African American churches. Korean churches serve their members socially by providing a structure and process for fellowship and sense of belonging, maintenance of ethnic identity and native traditions, social services, and social status. Korean pastors consider their churches as sanctuaries for their members and do not wish to burden them with messages related to political or economic issues. Instead, they focus on providing counseling and educational services to Korean families as well as clerical and lay positions for church members. Korean immigrants hold these positions in high regard.


Discussion

The status and trends in the tortoise population in the El Paso Mountains reflected local and regional human activities occurring over the last century and were typical of activities in the geographic range of the species and in the American West (Leu et al. 2008 Berry et al. 2013, 2014). Several human uses directly and indirectly influenced distribution, causes of death, and condition of habitats. The frequency of anthropogenic sign (98.4% of plots) is evidence of the ubiquitous human use and degraded habitat. Those signs of human use were facilitated by the network of roads and routes to access areas of interest for mining, livestock grazing, shooting, and vehicle-oriented recreation. Models of tortoise distribution revealed the importance, in descending order, of Cooper's goldenbush vegetation association, Predators, Mines, Trash, and Vehicles in shaping distribution in the years before and during the study. The correlation analyses provided similar results with additional relationships between some variables. Cooper's goldenbush association was the most important vegetation association it was midway in elevation and numbers of predominant species of shrubs compared with the other two vegetation associations but also shared similar counts of plots with tortoise sign with California buckwheat. By contrast, the creosote bush-white bursage association had only three abundant species, and one was a short-lived shrub species, cheesebush, typical of disturbed land (Vasek 1979/1980). Predators, Mines, Trash, and Vehicles were associated with human presence, a result, in part, of access via the network of designated roads and routes. Additional support for the models came from causes of death for the tortoises, specifically predators, shooting, and vehicle kills associated with roads or routes.

The probability of tortoise distribution (index of intensity of sign) was higher adjacent to EPMWA and in the northeastern and eastern parts of the study area than elsewhere (Fig. 2). The low end of the confidence interval for the density of adults (4.8/km 2 , 95% CI = 2.7–7.5/km 2 ) was the same as the density reported for the adjacent critical habitat unit during 2007 (2.7/km 2 ), but not in 2008 (0.4 /km 2 USFWS 2009, 2012). The midpoint of adult densities in the study area, however, was marginally above the estimated minimum viable density of adults (3.9 adults/km 2 ) necessary to maintain viable populations (USFWS 2015 Allison and McLuckie 2018).

Size-age structure of live and dead on-plot individuals included juvenile and immature tortoises, indicating that females were producing young. However, the numbers of young individuals surviving to maturity were insufficient to offset deaths of adults (Turner et al. 1987). The population structure was comparable to the population in RRCSP and in the adjacent critical habitat unit (Fig. 1 Berry et al. 2008).

Human activities influenced causes of death and the death rate of adults. The annualized death rate (6.9%) of adults was too high to sustain a population requiring up to 2 decades to reach sexual maturity and with low recruitment and survival of juveniles (Turner et al. 1987 Allison and McLuckie 2018 Berry and Murphy 2019). Small tortoises were vulnerable to predation (Berry and Murphy 2019). Signs of predator attacks on most live tortoises and shell-skeletal remains supported the importance of predators. Although predators have a negative effect on tortoises, the positive association in models and the correlation analyses between Predators and tortoises was a result of a co-occurrence of sign of mammalian predators and observations of Common Ravens on most plots. Predator scat was observed in association with tortoise remains, and tortoises occasionally had a burrow in a mound of kit fox dens. The incidence of extensive chewing typical of domestic dogs was comparable to, or higher than, that observed at some other sites (Berry et al. 2013, 2014). Predators, such as dogs, Common Ravens and coyotes—subsidized by human resources—thrive in proximity to areas with human activities. Populations of Common Ravens, the majority of observed avian predators, have increased multifold in the Mojave Desert (Boarman and Berry 1995), and excessive predation rates can lead to local extirpations of tortoises (Kristan and Boarman 2003). Similarly, coyote populations have grown in conjunction with urbanized landscapes (Fedriani et al. 2001). Esque et al. (2010) described increased rates of coyote predation on G. agassizii populations at several locations throughout the Mojave Desert during drought conditions. Factors influencing higher predation rates included proximity to human populations and road density (Esque et al. 2010). In our study, the highest levels of predator observations and sign were close to populated areas north and northeast of the study area and where road networks and a state highway occurred.

High mortality and clinical signs of disease in adult tortoises were limiting factors for long-term survival of the population. The proportion of shell-skeletal remains with evidence of crushing by vehicles and gunshots (12.5% of plots) reflected high levels of human use and access via the network of roads, routes, and unauthorized trails (Berry 1986 Nafus et al. 2013). The positive relationship between Vehicles and tortoise sign in the models resulted from co-occurrence of evidence on plots. Tortoises are vulnerable to death from vehicle traffic on roads and routes that bisect or cross their large home ranges (cf. Harless et al. 2009). Population of tortoises are depleted within several hundred meters of dirt and paved roads as a result of deaths or collection, or a road impact zone or road-effect zone (von Seckendorff Hoff and Marlow 2002 Nafus et al. 2013). In our study, dead tortoises were found nearer to roads and routes than live tortoises. The correlation between Vehicles and Shooting also was positive. Berry (1986) reported associations between deaths of tortoises caused by gunshots on plots and proximity to vehicle-oriented, concentrated recreational use zones and high visitor counts per year. The relationships between Vehicles and Trash were positive, but between Trash and tortoise sign were negative. Tortoises are known to consume trash and balloons, which can lead to death (Donoghue 2006 Walde et al. 2007).

Roads also led to mining activity, and the model and correlations showed positive relationships between tortoise sign and Mines. Yet, this relationship is both positive and negative: horizontal tunnels provide escape from extremes of temperature, spoil piles are construction sites for burrows, but vertical shafts and pits are sources of deaths. The existing network of roads also created opportunities for illegal collecting (not observed) and unauthorized off-road travel (Figs. 1, 3).

Implications for Recovery

The El Paso Mountains support tortoise populations, but high levels of visitor use (>70,000/yr) contribute to excessive mortality of adults (and other size classes) and degradation of habitat. These findings align with results from studies in RRCSP, the adjacent critical habitat unit in Fremont Valley, and elsewhere in the geographic range (Berry et al. 2008, 2014 Berry and Murphy 2019). We expect that the El Paso population tracked the reported 51% decline in the Western Mojave Recovery Unit and Fremont-Kramer critical habitat unit between 2004 and 2014 (USFWS 2015 Allison and McLuckie 2018). The future for a viable population in the study area is doubtful. Range-wide, Allison and McLuckie (2018) concluded that the species was on the path to extinction in critical habitat under current conditions.

The anthropogenic activities affecting the El Paso Mountains and other populations are well known. In the first recovery plan, the USFWS recommended management actions to implement in recovery areas, including several associated with closure and limitations to vehicular access (USFWS 1994). A partial list of prohibited activities included vehicle activity off designated routes, habitat-destructive activities that diminish the capacity of the land, livestock grazing, littering, dogs, and firearms use. All these activities occurred in the El Paso Mountains study area. Importantly, the study area is a popular, high visitation site for vehicle-oriented recreation. The route network was reaffirmed in a government plan (USBLM 2019). The El Paso Mountains are not part of critical habitat or recovery efforts for the tortoise (USFWS 2015).

At the Desert Tortoise Research Natural Area ∼5.6 km to the south in the Fremont Valley, fencing to exclude recreational vehicles and grazing successfully protected tortoise populations from two sources of mortality and habitat degradation, but not from infectious diseases and predation by Common Ravens (Berry et al. 2014). This natural area had higher densities of tortoises (10.2 adults/ km 2 ) than on private land (3.7 adults/km 2 ) or the critical habitat unit in Fremont Valley (2.4 adults/km 2 ).

Regardless of management decisions for the study area, the adjacent EPMWA with elevations of 850–1598 m has potential as a refuge from high visitation, vehicles, and habitat degradation as well as potential climate change (Cook et al. 2015 Allen et al. 2018 Sarhadi et al. 2018). The intensity of tortoise sign was higher along the EPMWA boundary, indicating that populations might be larger in EPMWA. Models have demonstrated that tortoises are likely to occur at higher elevations in warmer climates (Barrows 2011 Barrows et al. 2016). Future survival in EPMWA will be dependent on population viability, minimizing human-caused sources of mortality and habitat degradation, and the severity of future droughts. Although G. agassizii has physiological and behavioral adaptations for survival in deserts, prolonged periods of drought without water or forage and extreme temperatures will challenge the persistence of populations in otherwise suitable habitat areas (Turner et al. 1984 Henen et al. 1998 Berry et al. 2002 Longshore et al. 2003).


Abstract

Neighborhood and historical conditions are important factors in land dynamics. However, models that explicitly incorporate spatial and temporal dependencies face challenges in data availability, methodology and computation. In this research, parcel-level dynamics are investigated using the geocoded Auditor's tax database for Delaware County, Ohio, including 73,560 parcels over the period 1990–2012. A binary spatio-temporal autologistic model (STARM), incorporating space and time and their interactions, is used to investigate parcel-level dynamics. The results show that the model is able capture the impacts of contemporaneous and historical neighborhood conditions around parcels, as well as the effects of other variables such as distances to various facilities and infrastructures, agricultural and residential land-use shares within a half mile radius circle, and population density and growth expectation at the census tract level.


2 Answers 2

You need to use STRING_AGG() to aggregate the text values and one possible approach (based on the attempt in the question) is the following statement. The aggregation of the task names is for each item in the Object JSON array:

You need to use STRING_AGG() function, which applies to the DB version SQL Server 2017 and later, together with the below GROUP BY expression as

Using WITHIN GROUP (ORDER BY TaskName) is optional, if you do not want ordering, then you can remove that part from the function as in the below demonstration :


Methods

Study area

The study was conducted in Ghana, as shown in Fig. 1. However, because LF appears to be localized in northern and southern Ghana, the study area was subdivided only to include highly endemic areas in these two zones. To investigate risk factors in the two highly endemic zones and how they compare with the result from the entire country, three zonal analyses were performed: countrywide (CW), NZ, and SZ. The area considered as the SZ in this study included districts that lie along the coastal savannah, tropical rainforest and some portion of Ghana’s moist semi-deciduous forest region, while the NZ comprised the Sudan savannah and some part of the Guinea savannah.

Map of Ghana showing the districts included in the two study zones, NZ and SZ shaded in grey. (This map was generated by authors with ArcGIS V.10.6 software (ESRI, Redlands, CA, USA) and no permissions are required to publish it)

The NZ lies in the dry Guinea Savannah Ecological zone [32] with a sub-Sahelian climate made up of a wet and a dry season. The wet season extends from April to October, with a mean annual rainfall of approximately 1365 mm. Similarly, the dry season is subdivided into the Harmattan from November to mid-February and the dry, hot season from mid-February to April. Monthly temperatures range from 20 °C to 40 °C.

In contrast, the SZ lies within the high rain forest ecological zone of the West African sub-region, with strands of mangroves [11] and lots of wetlands. The climate in this region is tropical, characterized by two distinctive seasonal rainfalls a major one between April and June and a minor one that occurs between September and October. The relative humidity is generally high, averaging between 75 to 85% in the rainy and 70 to 80% in the dry seasons. The highest mean temperature is 34 °C, whereas the lowest is 20 °C.

LF prevalence data

Data on mf cases in Ghana was obtained from published articles in peer-reviewed journals ([2]: [22]). The data spanning 2000 to 2014 contained information on the year samples were collected, the number of years of MDA, the number of people examined, and the number of mf positive cases recorded for each study community. In all, 430 communities were surveyed for LF infections as part of a transmission assessment survey in Ghana. Details of this dataset were described by Biritwum et al. [2]. Spatial locations of these communities were extracted from multiple sources, including Google Earth Pro, Open Street Map, directory of cities and towns (world database), and database of the Ghana National Identification Authority card registration projects. Figure 2 shows a map of the spatial distribution of mf cases in Ghana (Fig. 2a), the NZ (Fig. 2b), and SZ (Fig. 2c).

mf cases for surveyed communities from 2000 to 2014 (yellow indicates absence and red indicates presence), a) CW, b) NZ and c) SZ Zones. (This map was generated by authors with ArcGIS V.10.6 software (ESRI, Redlands, CA, USA) and no permissions are required to publish it)

Geo-environmental and climatological data source

To identify the combination of explanatory variables that create a suitable environment for the transmission of lymphatic filariasis, land cover, socioeconomic and climatic predictors were obtained from various remotely-sensed datasets. Enhanced Vegetation Index (EVI) was generated from the Moderate Resolution Imaging Spectro-radiometer (MODIS) satellite image, specifically MOD13Q1 v006 [30]. This data is generated every 16 days at 250 m spatial resolution.

From the United States Geological Surveys (USGS) earth explorer project (US [39]), a raster dataset of elevation produced by the Shuttle Radar Topography Mission (SRTM) and Slope covariate were derived. Additionally, Landsat 7 ETM + 1 level 1 at 30 × 30 m resolution of less than 1% cloud cover was downloaded from the same site for Land Use/Land Cover (LULC) classification.

To determine rural and mostly poor areas in Ghana, Night-light emissivity from 2000 to 2014 captured by the operational linescan system instrument was used as a proxy [16]. This instrument measures visible and infrared radiation emitted at night time. The values range from 0 to 62, representing undetectable emissivity and maximum emissivity, respectively. Night-light emissivity has been shown to correlate with economic development in subnational regions of developing countries [5]. Another socioeconomic variable used was housing prevalence with improved drinking water and sanitation, sufficient living area, and durable construction across sub-Saharan Africa [38]. The prevalence of houses built with finished materials is higher in urban areas than in rural areas showing 84 and 34% improvement, respectively.

Precipitation and temperature variables were downloaded from the WorldClim database [41]. This dataset provides a set of global climate layers obtained by interpolation of weather station datasets distributed across the world. Other covariates used in the SDMs with details on the sources are provided in Table 1. Input grids were resampled to a common spatial resolution of 1 km 2 using bilinear resampling for analysis performed with CW data. In contrast, a finer resolution of 250 m 2 was used for the NZ and SZ to capture detailed information [40]. Raster layers were coerced to the same boundary extent to enable stacking for analysis. Raster manipulation and processing were undertaken using raster package in R V.3.5.3 and final map layouts created with ArcGIS V.10.6 software (ESRI, Redlands, CA, USA).

Variable selection and model development

To identify the optimal suite of covariates to include in the specie distribution models, the variables were grouped into three categories land cover, socioeconomic and climatic variables [29]. A test for variable collinearity with the Variance Inflation Factor (VIF) diagnostic method was adopted within each group. Since there are no formal criteria for deciding when a VIF is too large, a generic cutoff value of VIF ≥ 10 was used [8]. This approach reduces any potential collinearity and confounding effects such that for p - 1 independent variables,

where ( _i^2 ) is the coefficient of determination obtained by fitting a regression model for the ith independent variable on the other p − 2 independent variables. After the collinearity check, only Bio1 (Mean Annual Temperature) had a collinearity problem.

Variable relative contribution

After strongly correlated variables were removed, the range of variables influencing the occurrence of mf, were identified using boosted regression trees (BRT). This method draws insights and techniques from both statistical and machine learning traditions. The advantage of this method over the others is its strong predictive performance and consistent identification of relevant variables and interactions. Here, the probability of mf occurrence, y = 1, in a sampled community with covariates X, is given as p(y = 1| X). This probability models via a logit function f(x) = p(y = 1| x).

Analytically, BRT regularization involves jointly optimizing the number of trees (nt), learning rate (lr), and tree complexity (tc). The optimal number of trees was estimated by the default 10-fold cross-validation (CV) method [15]. With a slow enough lr of 0.01, the CV estimates of nt are reliable and close to those from independent data. To ensure the modelling of possible interactions between predictors, a tc of 5 was selected. A tc of 1 fits an additive model, while a tc of 2 fits a model with up to two-way interactions, and so on [15]. It has been proven that stochasticity improves model performance, and fractions in the range of 0·5–0·75 have given best results for presence–absence responses [15]. Therefore, a bag fraction of 0·75 and an error structure of Bernoulli was used from here on.

The relative importance of the variables was computed by measuring the number of times a predictor variable is selected for splitting, and weighted by the squared improvement to the model as a result of each split, then an average over all the trees is determined [20]. Expressing in mathematical terms, the relative influence, ( >_j ) of the input variables xj for a collection of decision trees ( _m ight>>_1^M ) , is given by

where M is the number of iteration. The relative influence (or contribution) of each variable is scaled so that the sum adds to 100, with higher numbers indicating a stronger influence on the response. A threshold of 10% was set below which a variable is considered to have no substantial contribution to the model [33]. Variables that contributed less than 10% in both study zones were EVI, DEM, maximum night land surface temperature, Bio19, Bio18, maximum day land surface temperature, LULC, mean and minimum night land surface temperature. In addition to the above variables, Bio17, distance to an inland water body, population density, and mean day Land surface temperature also had less than 10% contribution in the northern zone whereas improved housing, Bio12, and minimum night land surface temperature had an insignificant contribution to the model for the southern zone.

Model selection

Six model classes i.e., generalized linear models (GLM) [31], multivariate adaptive regression splines (MARS) [19], artificial neural networks (ANN) [21], generalized boosted models (GBM) [15], Random Forests (RF) [4], and surface range envelope (SRE) [3] were tested using Biomod2 package in R [36]. Out of these, the Random Forest and GBM were the best performing models for this data and were therefore used for modelling and predicting LF suitable environments. Hundred (100) model runs for each algorithm was performed iteratively, and the evaluation values of each run were stored and then averaged to make the final result more robust. Model evaluation was performed based on the area under the receiver operating characteristic (ROC) curve. This measures the ability of the final ensemble model to fit the presence-absence data and predict across unsampled locations.


Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

As soon as live music activities ceased following the outbreak of the COVID-19 pandemic, actual measurement of live music's impact gained in prominence. Studies on live music's sociocultural and economic relevance were vital for this sector in order to qualify for government support and to understand the financial consequences of the lockdown (see for example: HoC, 2020 Musicians Union, 2020 and, UK Music, 2020). These types of studies build on a long history of public and private research on live music. Over the last decades, the attention to measuring cultural activities had already grown because of evidence-based policymaking and the calls on cultural organisations to prove their relevance to society (Gielen et al., 2014 O'Brien, 2010). These measures are expected to legitimise the investment of public resources such as subsidies and spaces (Getz et al., 2017 Wall, 2008).

This paper sets out to compare different methodologies for measuring live music's values and to explore the different motivations amongst a range of organisations engaged in that work. In doing so, we focus on popular styles of live music, while acknowledging that the boundaries between popular music and, for example, classical music are not always easy to draw. We understand live music as events “in which musicians (including DJs) provide music for audiences and dancers gathering in public places where the music is the principal purpose of that gathering” (Webster et al., 2018, p. 115). The values of these events concern their potential impact and benefits for people, communities and places. This includes, among others, social, cultural and economic values. The actors involved in measuring the values of live music include not only those involved with the business of music, but local and national government agencies and policy makers and also companies such as property developers linked to business planning. While the number of studies measuring live music's impact is growing, theoretical and methodological reflection is missing. By comparing the motivations and methodologies of different actors, we gain a better understanding of how different ways of measuring live music affect policymaking and conceptions of what live music is and should be. We aim to build bridges between diversified organisations to help them to understand the limitations, challenges and opportunities of their approaches, and where they may benefit from a cross-contamination of methodologies.

This paper presents measuring live music's impact as a complex, multi-faceted phenomenon. We argue that measuring live music is not a neutral activity, but itself constructs a vision on how live music ecologies should function. If live music ecologies are understood as the network of people and organisations enabling musical performances, this implies that those actors who engage in measuring live music are actors in this ecology. In fact, data-derived services (e.g. Songkick, Skiddle) engage in live music measurement as part of their business models. Measurements of live music activity feedback into how live music ecologies function and are organised. For example, when measures emphasise the economic impact of live music, it is likely that policymaking will be directed towards these economic goals. Furthermore, if particular methodologies are incomplete in their measurements, this could lead to oversights in decision-making.

The paper consists of three sections. First, we examine how live music is measured by discussing a range of methodologies (e.g. mapping, censuses and economic impact studies) and data sources (i.e. qualitative and quantitative). Second, we explore who does the measuring, distinguishing actors in the fields of industry, academia and policy. Third, we present a model to compare different approaches, reflecting on their commonalities, tensions and gaps. This model can serve as a resource for those planning new research projects on the impact of live music. We conclude by discussing potential new methodologies and approaches to measuring live music's impact. We draw on the experiences of measuring live music gained in projects from the Netherlands and UK: Staging Popular Music (POPLIVE), [1] Birmingham Live Music Project (BLMP) [2] and the UK Live Music Census [3].


5 Research and Management Implications

While summarizing the best current knowledge of factors driving variability in thermal regimes, our models also provide a foundation for future improvements as monitoring data and GIS coverages improve. Our model will be useful in (1) producing regional maps of thermal regimes characterized both by summer median temperatures and daily range [Maheu et al., 2015 ], (2) predicting reference condition in the absence of anthropogenic impacts, and (3) identifying critical thermal refugia. As evidenced by our outlier analysis (section 2.2), these models are sufficiently accurate to allow managers to identify aberrant temperature regimes related to discharges. We intend to use these maps in conjunction with regional fish monitoring data to examine potential impacts of development on fish communities, as well as the influence of moderating factors. While our current analyses focused on characterization of reference (or impacted) condition at a static point in time, in the future we will expand our approach to evaluate landscape factors affecting the thermal sensitivity of streams. This will allow us to model combined scenarios of land use and climate change to evaluate and prioritize alternative mitigation strategies for minimizing impacts.


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