Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

Literature review dataset

This table lists the surveyed papers concerning the application of spatial analysis, GIS (Geographic Information Systems) as well as general geographic approaches and geostatistics, to the assessment of CoViD-19 dynamics. The period of survey is from January 1st, 2020 to December 15th, 2020. The first column lists the reference. The second lists the date of publication (preferably, the date of online publication). The third column lists the Country or the Countries and/or the subnational entities investigated. The fourth column lists the epidemiological data utilized in each paper. The fifth column lists other types of data utilized for the analysis. The sixth column lists the more traditionally statistically-based methods, if utilized. The seventh column lists the geo-statistical, GIS or geographic methods, if utilized. The eight column sums up the findings of each paper. The papers are also classified within seven thematic categories. The full references are available at the end of the table in alphabetical order.

This table was the basis for the realization of a comprehensive geographic literature review. It aims to be a useful tool to ease the "due-diligence" activity of all the researchers interested in the spatial analysis of the pandemic.

The reference to cite the related paper is the following:

Pranzo, A.M.R., Dai Prà, E. & Besana, A. Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year. GeoJournal (2022). https://doi.org/10.1007/s10708-022-10601-y

To read the manuscript please follow this link: https://doi.org/10.1007/s10708-022-10601-y

Abstract The amount of researchers and scientific papers rapidly grows, annually. The metrics to analyze the quality and quantity of these publications have consolidated in the academic world. A bibliometric mapping of scientific papers on Geographic Information Systems (GIS) published between 2007 and 2016 was carried out. The sample analyzed 2,053 papers, extracted from twenty journals of the Web of Science Core Collection platform. The following were evaluated: total number of publications, production by area of knowledge and by country, authors, periodicals and the most cited words. The results shows that 2012 and 2013 were the most productive periods, and that the annual growth rate of publication was 1.8%. The most significant academic areas were Geography, Computer Science, Physical Geography, and Environmental Sciences/Ecology. The three major publishing clusters were North America, Western Europe, and Eastern Asia. The International Journal of Geographic Information Science was considered the most important journal. The most relevant topics were cellular automata, relationship between GIS and users, integration of GIS with remote sensing, different land use classification methods, and critical reflections on technologies and GIS.

Mixed-methods designs, especially those in which case selection is regression-based, have become popular across the social sciences. In this paper, we highlight why tools from spatial analysis—which have largely been overlooked in the mixed-methods literature—can be used for case selection and be particularly fruitful for theory development. We discuss two tools for integrating quantitative and qualitative analysis: (1) spatial autocorrelation in the outcome of interest; and (2) spatial autocorrelation in the residuals of a regression model. The case selection strategies presented here enable scholars to systematically use geography to learn more about their data and select cases that help identify scope conditions, evaluate the appropriate unit or level of analysis, examine causal mechanisms, and uncover previously omitted variables.

This paper presents the importance of simple spatial statistics techniques applied in positional quality control of spatial data. To this end, Analysis methods of point data spatial distribution pattern are presented, as well as bias analysis in the positional discrepancies samples. To evaluate the points spatial distribution Nearest Neighbor and Ripley's K function methods were used. As for bias analysis, the average directional vectors of discrepancies and the circular variance were used. A methodology for positional quality control of spatial data is proposed, in which includes sampling planning and its spatial distribution pattern evaluation, analyzing the data normality through the application of bias tests, and positional accuracy classification according to a standard. For the practical experiment, an orthoimage generated from a PRISM scene of the ALOS satellite was evaluated. Results showed that the orthoimage is accurate on a scale of 1:25,000, being classified as Class A according to the Brazilian standard positional accuracy, not showing bias at the coordinates. The main contribution of this work is the incorporation of spatial statistics techniques in cartographic quality control.

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.

Methods 1. Data collection using digital photographs and GIS

A flat 5 m x 5 m sampling block was chosen in a study grassland community and divided with bamboo chopsticks into 100 sub-blocks of 50 cm x 50 cm (Fig. 1). A digital camera was then mounted to a telescoping stake and positioned in the center of each sub-block to photograph vegetation within a 0.25 m2 area. Pictures were taken 1.75 m above the ground at an approximate downward angle of 90° (Fig. 2). Automatic camera settings were used for focus, lighting and shutter speed. After photographing the plot as a whole, photographs were taken of each individual plant in each sub-block. In order to identify each individual plant from the digital images, each plant was uniquely marked before the pictures were taken (Fig. 2 B).

Digital images were imported into a computer as JPEG files, and the position of each plant in the pictures was determined using GIS. This involved four steps: 1) A reference frame (Fig. 3) was established using R2V software to designate control points, or the four vertexes of each sub-block (Appendix S1), so that all plants in each sub-block were within the same reference frame. The parallax and optical distortion in the raster images was then geometrically corrected based on these selected control points; 2) Maps, or layers in GIS terminology, were set up for each species as PROJECT files (Appendix S2), and all individuals in each sub-block were digitized using R2V software (Appendix S3). For accuracy, the digitization of plant individual locations was performed manually; 3) Each plant species layer was exported from a PROJECT file to a SHAPE file in R2V software (Appendix S4); 4) Finally each species layer was opened in Arc GIS software in the SHAPE file format, and attribute data from each species layer was exported into Arc GIS to obtain the precise coordinates for each species. This last phase involved four steps of its own, from adding the data (Appendix S5), to opening the attribute table (Appendix S6), to adding new x and y coordinate fields (Appendix S7) and to obtaining the x and y coordinates and filling in the new fields (Appendix S8).

1. Data reliability assessment

To determine the accuracy of our new method, we measured the individual locations of Leymus chinensis, a perennial rhizome grass, in representative community blocks 5 m x 5 m in size in typical steppe habitat in the Inner Mongolia Autonomous Region of China in July 2010 (Fig. 4 A). As our standard for comparison, we used a ruler to measure the individual coordinates of L. chinensis. We tested for significant differences between (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler (see section 3.2 Data Analysis). If (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler, did not differ significantly, then we could conclude that our new method of measuring the coordinates of L. chinensis was reliable.

We compared the results using a t-test (Table 1). We found no significant differences in either (1) the coordinates of L. chinensis or (2) the pair correlation function g of L. chinensis. Further, we compared the pattern characteristics of L. chinensis when measured by our new method against the ruler measurements using a null model. We found that the two pattern characteristics of L. chinensis did not differ significantly based on the homogenous Poisson process or complete spatial randomness (Fig. 4 B). Thus, we concluded that the data obtained using our new method was reliable enough to perform point pattern analysis with a null model in grassland communities.

Based on open access data, 79 Mediterranean passenger ports are analyzed to compare their infrastructure, hinterland accessibility and offered multi-modality categories. Comparative Geo-spatial analysis is also carried out by using the data normalization method in order to visualize the ports' performance on maps. These data driven comprehensive analytical results can bring added value to sustainable development policy and planning initiatives in the Mediterranean Region. The analyzed elements can be also contributed to the development of passenger port performance indicators. The empirical research methods used for the Mediterranean passenger ports can be replicated for transport nodes of any region around the world to determine their relative performance on selected criteria for improvement and planning.

The Mediterranean passenger ports were initially categorized into cruise and ferry ports. The cruise ports were identified from the member list of the Association for the Mediterranean Cruise Ports (MedCruise), representing more than 80% of the cruise tourism activities per country. The identified cruise ports were mapped by selecting the corresponding geo-referenced ports from the map layer developed by the European Marine Observation and Data Network (EMODnet). The United Nations (UN) Code for Trade and Transport Locations (LOCODE) was identified for each of the cruise ports as the common criteria to carry out the selection. The identified cruise ports not listed by the EMODnet were added to the geo-database by using under license the editing function of the ArcMap (version 10.1) geographic information system software. The ferry ports were identified from the open access industry initiative data provided by the Ferrylines, and were mapped in a similar way as the cruise ports (Figure 1).

Based on the available data from the identified cruise ports, a database (see Table A1–A3) was created for a Mediterranean scale analysis. The ferry ports were excluded due to the unavailability of relevant information on selected criteria (Table 2). However, the cruise ports serving as ferry passenger ports were identified in order to maximize the scope of the analysis. Port infrastructure and hinterland accessibility data were collected from the statistical reports published by the MedCruise, which are a compilation of data provided by its individual member port authorities and the cruise terminal operators. Other supplementary sources were the European Sea Ports Organization (ESPO) and the Global Ports Holding, a cruise terminal operator with an established presence in the Mediterranean. Additionally, open access data sources (e.g. the Google Maps and Trip Advisor) were consulted in order to identify the multi-modal transports and bridge the data gaps on hinterland accessibility by measuring the approximate distances.

In the last decade, a plethora of algorithms have been developed for spatial ecology studies. In our case, we use some of these codes for underwater research work in applied ecology analysis of threatened endemic fishes and their natural habitat. For this, we developed codes in Rstudio® script environment to run spatial and statistical analyses for ecological response and spatial distribution models (e.g., Hijmans & Elith, 2017; Den Burg et al., 2020). The employed R packages are as follows: caret (Kuhn et al., 2020), corrplot (Wei & Simko, 2017), devtools (Wickham, 2015), dismo (Hijmans & Elith, 2017), gbm (Freund & Schapire, 1997; Friedman, 2002), ggplot2 (Wickham et al., 2019), lattice (Sarkar, 2008), lattice (Musa & Mansor, 2021), maptools (Hijmans & Elith, 2017), modelmetrics (Hvitfeldt & Silge, 2021), pander (Wickham, 2015), plyr (Wickham & Wickham, 2015), pROC (Robin et al., 2011), raster (Hijmans & Elith, 2017), RColorBrewer (Neuwirth, 2014), Rcpp (Eddelbeuttel & Balamura, 2018), rgdal (Verzani, 2011), sdm (Naimi & Araujo, 2016), sf (e.g., Zainuddin, 2023), sp (Pebesma, 2020) and usethis (Gladstone, 2022).

It is important to follow all the codes in order to obtain results from the ecological response and spatial distribution models. In particular, for the ecological scenario, we selected the Generalized Linear Model (GLM) and for the geographic scenario we selected DOMAIN, also known as Gower's metric (Carpenter et al., 1993). We selected this regression method and this distance similarity metric because of its adequacy and robustness for studies with endemic or threatened species (e.g., Naoki et al., 2006). Next, we explain the statistical parameterization for the codes immersed in the GLM and DOMAIN running:

In the first instance, we generated the background points and extracted the values of the variables (Code2_Extract_values_DWp_SC.R). Barbet-Massin et al. (2012) recommend the use of 10,000 background points when using regression methods (e.g., Generalized Linear Model) or distance-based models (e.g., DOMAIN). However, we considered important some factors such as the extent of the area and the type of study species for the correct selection of the number of points (Pers. Obs.). Then, we extracted the values of predictor variables (e.g., bioclimatic, topographic, demographic, habitat) in function of presence and background points (e.g., Hijmans and Elith, 2017).

Subsequently, we subdivide both the presence and background point groups into 75% training data and 25% test data, each group, following the method of Soberón & Nakamura (2009) and Hijmans & Elith (2017). For a training control, the 10-fold (cross-validation) method is selected, where the response variable presence is assigned as a factor. In case that some other variable would be important for the study species, it should also be assigned as a factor (Kim, 2009).

After that, we ran the code for the GBM method (Gradient Boost Machine; Code3_GBM_Relative_contribution.R and Code4_Relative_contribution.R), where we obtained the relative contribution of the variables used in the model. We parameterized the code with a Gaussian distribution and cross iteration of 5,000 repetitions (e.g., Friedman, 2002; kim, 2009; Hijmans and Elith, 2017). In addition, we considered selecting a validation interval of 4 random training points (Personal test). The obtained plots were the partial dependence blocks, in function of each predictor variable.

Subsequently, the correlation of the variables is run by Pearson's method (Code5_Pearson_Correlation.R) to evaluate multicollinearity between variables (Guisan & Hofer, 2003). It is recommended to consider a bivariate correlation ± 0.70 to discard highly correlated variables (e.g., Awan et al., 2021).

Once the above codes were run, we uploaded the same subgroups (i.e., presence and background groups with 75% training and 25% testing) (Code6_Presence&backgrounds.R) for the GLM method code (Code7_GLM_model.R). Here, we first ran the GLM models per variable to obtain the p-significance value of each variable (alpha ≤ 0.05); we selected the value one (i.e., presence) as the likelihood factor. The generated models are of polynomial degree to obtain linear and quadratic response (e.g., Fielding and Bell, 1997; Allouche et al., 2006). From these results, we ran ecological response curve models, where the resulting plots included the probability of occurrence and values for continuous variables or categories for discrete variables. The points of the presence and background training group are also included.

On the other hand, a global GLM was also run, from which the generalized model is evaluated by means of a 2 x 2 contingency matrix, including both observed and predicted records. A representation of this is shown in Table 1 (adapted from Allouche et al., 2006). In this process we select an arbitrary boundary of 0.5 to obtain better modeling performance and avoid high percentage of bias in type I (omission) or II (commission) errors (e.g., Carpenter et al., 1993; Fielding and Bell, 1997; Allouche et al., 2006; Kim, 2009; Hijmans and Elith, 2017).

Table 1. Example of 2 x 2 contingency matrix for calculating performance metrics for GLM models. A represents true presence records (true positives), B represents false presence records (false positives - error of commission), C represents true background points (true negatives) and D represents false backgrounds (false negatives - errors of omission).

Validation set

Model

True

False

Presence

A

B

Background

C

D

We then calculated the Overall and True Skill Statistics (TSS) metrics. The first is used to assess the proportion of correctly predicted cases, while the second metric assesses the prevalence of correctly predicted cases (Olden and Jackson, 2002). This metric also gives equal importance to the prevalence of presence prediction as to the random performance correction (Fielding and Bell, 1997; Allouche et al., 2006).

The last code (i.e., Code8_DOMAIN_SuitHab_model.R) is for species distribution modelling using the DOMAIN algorithm (Carpenter et al., 1993). Here, we loaded the variable stack and the presence and background group subdivided into 75% training and 25% test, each. We only included the presence training subset and the predictor variables stack in the calculation of the DOMAIN metric, as well as in the evaluation and validation of the model.

Regarding the model evaluation and estimation, we selected the following estimators:

1) partial ROC, which evaluates the approach between the curves of positive (i.e., correctly predicted presence) and negative (i.e., correctly predicted absence) cases. As farther apart these curves are, the model has a better prediction performance for the correct spatial distribution of the species (Manzanilla-Quiñones, 2020).

2) ROC/AUC curve for model validation, where an optimal performance threshold is estimated to have an expected confidence of 75% to 99% probability (De Long et al., 1988).

The airport catchment area is the geographic area from which an airport can reasonably expect to draw commercial air service passengers. The purpose of this interdisciplinary research is to estimate airport catchment areas using a spatial analysis method for informed airport management. In order to ensure the comprehensiveness and reliability of the research, we chose to analyze the catchment areas for five airports of different sizes and in different geographic locations in the United States. The Huff model, which is usually used in marketing, economics, and retail research, was adopted in this study. We applied this model in airport catchment analysis for the selected airports, with consideration of ground transportation distance and airport attractiveness. We identified the best distance factors and parameters for modeling purposes. The results provided reliable information for the selected five airports to estimate their catchment areas. In addition, the model can provide a good reference for other airport catchment analysis studies about the effectiveness of the Huff model in prediction.

There are three layers per water quality parameter. Details of the layers and associated attributes follow:Parametername_Programs - This layer illustrates the number of monitoring programs measuring the focal parameter within each hexagon of the grid. Layer attributes are as follows.Join_Count – Field containing the number of monitoring programs with footprints inside the hexagonGRID_ID – Field containing the ID number for the hexagonAlabama – Field denoting if a hexagon from the grid falls within Alabama (1 – yes, 0 – no)Florida – Field denoting if a hexagon from the grid falls within Florida (1 – yes, 0 – no)Louisiana – Field denoting if a hexagon from the grid falls within Louisiana (1 – yes, 0 – no)Mississippi – Field denoting if a hexagon from the grid falls within Mississippi (1 – yes, 0 – no)Texas – Field denoting if a hexagon from the grid falls within Texas (1 – yes, 0 – no)Parametername_Method_Extent - This layer illustrates the extents of where each focal parameter’s identified analytical methods are found across the Gulf. In order to see each analytical method’s extent alone, the color next to the other analytical methods must be changed to “no color” by right clicking on the box next to the method name. Layer attributes are as follows. GRID_ID – Field containing the ID number for the hexagonAlabama – Field denoting if a hexagon from the grid falls within Alabama (1 – yes, 0 – no)Florida – Field denoting if a hexagon from the grid falls within Florida (1 – yes, 0 – no)Louisiana – Field denoting if a hexagon from the grid falls within Louisiana (1 – yes, 0 – no)Mississippi – Field denoting if a hexagon from the grid falls within Mississippi (1 – yes, 0 – no)Texas – Field denoting if a hexagon from the grid falls within Texas (1 – yes, 0 – no)PID – Unique identifier assigned to each monitoring program within the CMAP InventoryProgram_Name – The name of the monitoring program that occurs within that hexagonParametername_Methods_SHP – Field containing the analytical method information used to generate the shapefile and symbologyParametername_Analytical_Method_CW – Field containing information from the Analytical Method field of the crosswalk table. This field can contain “-“denoting that information was not able to be found for a particular program/parameter.Parametername_Gen_Analytical_Method_Instrument – Field containing information from the General Analytical Method (Instrumentation) field from the crosswalk table. This field can contain “-“denoting that information was not able to be found for a particular program/parameter. Information from this field was used in the Parametername_Methods_SHP field when no information was populated in the Parametername_Analytical_Method_CW field.Parametername_Method_Count - This layer illustrates the number of unique analytical methods to measure the focal parameter identified within each hexagon of the grid. A method count shapefile is not included for the cyanobacteria parameter due to no analytical methods being identified for this parameter GRID_ID – Field containing the ID number for the hexagonUNIQUE_parametername_Methods_SHP – Field containing the number of unique analytical methods occurring within the hexagonAlabama – Field denoting if a hexagon from the grid falls within Alabama (1 – yes, 0 – no)Florida – Field denoting if a hexagon from the grid falls within Florida (1 – yes, 0 – no)Louisiana – Field denoting if a hexagon from the grid falls within Louisiana (1 – yes, 0 – no)Mississippi – Field denoting if a hexagon from the grid falls within Mississippi (1 – yes, 0 – no)Texas – Field denoting if a hexagon from the grid falls within Texas (1 – yes, 0 – no)

This dataset helps to investigate the Spatial Accessibility to HIV Testing, Treatment, and Prevention Services in Illinois and Chicago, USA. The main components are: population data, healthcare data, GTFS feeds, and road network data. The core components are: 1) GTFS which contains GTFS (General Transit Feed Specification) data which is provided by Chicago Transit Authority (CTA) from Google's GTFS feeds. Documentation defines the format and structure of the files that comprise a GTFS dataset: https://developers.google.com/transit/gtfs/reference?csw=1. 2) HealthCare contains shapefiles describing HIV healthcare providers in Chicago and Illinois respectively. The services come from Locator.HIV.gov. 3) PopData contains population data for Chicago and Illinois respectively. Data come from The American Community Survey and AIDSVu. AIDSVu (https://map.aidsvu.org/map) provides data on PLWH in Chicago at the census tract level for the year 2017 and in the State of Illinois at the county level for the year 2016. The American Community Survey (ACS) provided the number of people aged 15 to 64 at the census tract level for the year 2017 and at the county level for the year 2016. The ACS provides annually updated information on demographic and socio economic characteristics of people and housing in the U.S. 4) RoadNetwork contains the road networks for Chicago and Illinois respectively from OpenStreetMap using the Python osmnx package. The abstract for our paper is: Accomplishing the goals outlined in “Ending the HIV (Human Immunodeficiency Virus) Epidemic: A Plan for America Initiative” will require properly estimating and increasing access to HIV testing, treatment, and prevention services. In this research, a computational spatial method for estimating access was applied to measure distance to services from all points of a city or state while considering the size of the population in need for services as well as both driving and public transportation. Specifically, this study employed the enhanced two-step floating catchment area (E2SFCA) method to measure spatial accessibility to HIV testing, treatment (i.e., Ryan White HIV/AIDS program), and prevention (i.e., Pre-Exposure Prophylaxis [PrEP]) services. The method considered the spatial location of MSM (Men Who have Sex with Men), PLWH (People Living with HIV), and the general adult population 15-64 depending on what HIV services the U.S. Centers for Disease Control (CDC) recommends for each group. The study delineated service- and population-specific accessibility maps, demonstrating the method’s utility by analyzing data corresponding to the city of Chicago and the state of Illinois. Findings indicated health disparities in the south and the northwest of Chicago and particular areas in Illinois, as well as unique health disparities for public transportation compared to driving. The methodology details and computer code are shared for use in research and public policy.

Context: Land use change requires measuring shifting patterns in biodiversity at various spatial scales to inform landscape management. Assessing vegetation change at different scales is challenging in urban ecosystems managed by many individuals. Thus, we do not know much about the structure and function of green spaces that support biodiversity. Objective: We aim to understand how vegetation structure and function indicators in urban community gardens vary with spatial scale, applying new and traditional methods in landscape ecology to inform future research and application. Methods: We performed two methods to assess garden vegetation structure (height) and function (species diversity, cover) at the garden- and garden plot scale. First, we used traditional field sampling to estimate garden vegetation at the garden scale (1 m 2 quadrats along transects) and at the plot scale (estimated within entire plot) to measure height, diversity and cover. Second, we used UAV aerial imagery to derive measures of garden and plot vegetation using canopy height models (CHMs). We evaluated differences in CHMs at each scale across the gardens, and compared field and UAV-derived measures. Results: Garden vegetation characteristics vary with spatial scale. Plant species richness and vegetation cover, but not height, related to UAV-derived imagery. Conclusions: New technologies paired with traditional field methods can together inform how vegetation structure and function vary with spatial scale in urban landscapes. Spatial scale is key to accurate and meaningful urban vegetation analyses. New and traditional methods in urban ecology research should develop together to improve and streamline their future application.

Avalanches represent a very high risk in residential areas, road infrastructure, environment, and economy, and can have fatal consequences if the human factors do not take any action. Advances in geospatial technology and access to spatial data have enabled spatial analysis to assist in decision-making regarding spatial planning in avalanche-prone locations. Determining locations with snow avalanche discharge potential is a crucial step in the avalanche zoning process.

This research deals with areas with snow avalanche potential disjunction, based mainly on topographic factors followed by meteorological ones. Topographic factors were mainly determined according to morphometric techniques, which are achieved through geographic information systems (GIS), as well as meteorological ones from statistical data and various processing of spatial and non-spatial data. Spatial analysis are also supported by geostatistical methods Fuzzy Logic and AHP, which in interaction with GIS have enabled the achievement of the purpose of this paper. The results from the spatial analysis have been verified based on comparison methods, such as the ROC method which was used during this final phase, in which the analysis has shown that the methods used in this research have given satisfactory results. As the main result, we obtained maps of areas with snow avalanche potential discharge in the study area relating to two geostatistical methods.

As GIS and computing technologies advanced rapidly, many indoor space studies began to adopt GIS technology, data models, and analysis methods. However, even with a considerable amount of research on indoor GIS and various indoor systems developed for different applications, there has not been much attention devoted to adopting indoor GIS for the evaluation space usage. Applying indoor GIS for space usage assessment can not only provide a map-based interface for data collection, but also brings spatial analysis and reporting capabilities for this purpose. This study aims to explore best practice of using an indoor GIS platform to assess space usage and design a complete indoor GIS solution to facilitate and streamline the data collection, a management and reporting workflow. The design has a user-friendly interface for data collectors and an automated mechanism to aggregate and visualize the space usage statistics. A case study was carried out at the Purdue University Libraries to assess study space usage. The system is efficient and effective in collecting student counts and activities and generating reports to interested parties in a timely manner. The analysis results of the collected data provide insights into the user preferences in terms of space usage. This study demonstrates the advantages of applying an indoor GIS solution to evaluate space usage as well as providing a framework to design and implement such a system. The system can be easily extended and applied to other buildings for space usage assessment purposes with minimal development efforts.

Geostatistics analyzes and predicts the values associated with spatial or spatial-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This archive contains results of geostatistical analysis of COVID-19 case counts for all available US counties. Test results were obtained with ArcGIS Pro (ESRI). Sources are state health departments, which are scraped and aggregated by the Johns Hopkins Coronavirus Resource Center and then pre-processed by MappingSupport.com.

This update of the Zenodo dataset (version 6) consists of three compressed archives containing geostatistical analyses of SARS-CoV-2 testing data. This dataset utilizes many of the geostatistical techniques used in previous versions of this Zenodo archive, but has been significantly expanded to include analyses of up-to-date U.S. COVID-19 case data (from March 24th to September 8^th, 2020):

Archive #1: “1.Geostat. Space-Time analysis of SARS-CoV-2 in the US (Mar24-Sept6).zip” – results of a geostatistical analysis of COVID-19 cases incorporating spatially-weighted hotspots that are conserved over one-week timespans. Results are reported starting from when U.S. COVID-19 case data first became available (March 24^th, 2020) for 25 consecutive 1-week intervals (March 24th through to September 6th, 2020). Hotspots, where found, are reported in each individual state, rather than the entire continental United States.

Archive #2: "2.Geostat. Spatial analysis of SARS-CoV-2 in the US (Mar24-Sept8).zip" – the results from geostatistical spatial analyses only of corrected COVID-19 case data for the continental United States, spanning the period from March 24^th through September 8th, 2020. The geostatistical techniques utilized in this archive includes ‘Hot Spot’ analysis and ‘Cluster and Outlier’ analysis.

Archive #3: "3.Kriging and Densification of SARS-CoV-2 in LA and MA.zip" – this dataset provides preliminary kriging and densification analysis of COVID-19 case data for certain dates within the U.S. states of Louisiana and Massachusetts.

These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*) [‘Archive #1’: consecutive weeklong Space-Time Hot Spot analysis; ‘Archive #2’: daily Hot Spot Analysis], Cluster and Outlier analysis (Anselin Local Moran's I) [‘Archive #2’], Spatial Autocorrelation (Global Moran's I) [‘Archive #2’], and point-to-point comparisons with Kriging and Densification analysis [‘Archive #3’].

The Word document provided ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 6).docx") details the contents of each file and folder within these three archives and gives general interpretations of these results.

INTRODUCTION: In order to meet the demands of the patient population with hearing impairment, the Hearing Health Care Network was created, consisting of primary care actions of medium and high complexity. Spatial analysis through geoprocessing is a way to understand the organization of such services. OBJECTIVE: To analyze the organization of the Hearing Health Care Network of the State of Minas Gerais. METHODS: Cross-sectional analytical study using geoprocessing techniques. The absolute frequency and the frequency per 1000 inhabitants of the following variables were analyzed: assessment and diagnosis, selection and adaptation of hearing aids, follow-up, and speech therapy. The spatial analysis unit was the health micro-region. RESULTS: The assessment and diagnosis, selection, and adaptation of hearing aids and follow-up had a higher absolute number in the micro-regions with hearing health services. The follow-up procedure showed the lowest occurrence. Speech therapy showed higher occurrence in the state, both in absolute numbers, as well as per population. CONCLUSION: The use of geoprocessing techniques allowed the identification of the care flow as a function of the procedure performance frequency, population concentration, and territory distribution. All procedures offered by the Hearing Health Care Network are performed for users of all micro-regions of the state.

Animal mortality on roads is one of the main concerns on wildlife conservation. Due to their habitat requirements, amphibians became one of the most commonly road-killed group and this may affect their population viability. Implementation of mitigation measures may overcome the problem. However, due to the extensive road network, their application is very expensive and required a better understanding in where they should be implemented. Mortality hotspots can be identified as clusters of road-killed records) using GIS (Geographic Information Systems). Although there are several statistical methods available, it is lacking a comparison analysis of them in order to understand their pros and contras. The aim of this study was to analyse possible differences between global, multi-scale and local spatial analysis methods in defining hotspots using amphibian road fatality data collected in northern Portugal country roads. We calculated the Nearest neighbor index, Morans I and Getis-ord General in order to compare the global clustering of points in seven sampled roads, and three were identified as clustered. We used Ripley K-function, Ripley L-function and F function to calculate the best scale for Malo's equation and Kernel density analysis in detecting hotspots and we compared their detection performance with Local Indicators of Association (LISA) (i.e Local Moran's I and Getis-ord Gi). Three different GIS software applications were used: ArcGis, Quantum GIS with R (opensource) and GeoDa (opensource). Results showed the importance of using multidistance spatial cluster analysis to define the best scale for hotspot detection with Malo´s equation and Kernel density analysis. Here we also suggest the advantages of Local Indicators of Association (LISA) for detecting clusters with the contribution of each individual observation (Local Morans I and Getis-ord Gi).

One cell map V8 svg version.R. File of the script to analyse retinas with thesvg format using R software program. The file should be copied in the same directory than the svg file. (R)

BackgroundSpatial transcriptomics (STs) simultaneously obtains the location and amount of gene expression within a tissue section. However, current methods like FindMarkers calculated the differentially expressed genes (DEGs) based on the classical statistics, which should abolish the spatial information.Materials and methodsA new method named spatial analysis of spatial transcriptomics (saSpatial) was developed for both the location and the amount of gene expression. Then saSpatial was applied to detect DEGs in both inter- and intra-cross sections. DEGs detected by saSpatial were compared with those detected by FindMarkers.ResultsSpatial analysis of spatial transcriptomics was founded on the basis of spatial statistics. It was able to detect DEGs in different regions in the normal brain section. As for the brain with ischemic stroke, saSpatial revealed the DEGs for the ischemic core and penumbra. In addition, saSpatial characterized the genetic heterogeneity in the normal and ischemic cortex. Compared to FindMarkers, a larger number of valuable DEGs were found by saSpatial.ConclusionSpatial analysis of spatial transcriptomics was able to effectively detect DEGs in STs data. It was a simple and valuable tool that could help potential researchers to find more valuable genes in the future research.