IntroductionChildhood stunting is a global public health concern, associated with both short and long-term consequences, including high child morbidity and mortality, poor development and learning capacity, increased vulnerability for infectious and non-infectious disease. The prevalence of stunting varies significantly throughout Ethiopian regions. Therefore, this study aimed to assess the geographical variation in predictors of stunting among children under the age of five in Ethiopia using 2019 Ethiopian Demographic and Health Survey.MethodThe current analysis was based on data from the 2019 mini Ethiopian Demographic and Health Survey (EDHS). A total of 5,490 children under the age of five were included in the weighted sample. Descriptive and inferential analysis was done using STATA 17. For the spatial analysis, ArcGIS 10.7 were used. Spatial regression was used to identify the variables associated with stunting hotspots, and adjusted R2 and Corrected Akaike Information Criteria (AICc) were used to compare the models. As the prevalence of stunting was over 10%, a multilevel robust Poisson regression was conducted. In the bivariable analysis, variables having a p-value < 0.2 were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval is presented to show the statistical significance and strength of the association.ResultThe prevalence of stunting was 33.58% (95%CI: 32.34%, 34.84%) with a clustered geographic pattern (Moran’s I = 0.40, p40 (APR = 0.74, 95%CI: 0.55, 0.99). Children whose mother had secondary (APR = 0.74, 95%CI: 0.60, 0.91) and higher (APR = 0.61, 95%CI: 0.44, 0.84) educational status, household wealth status (APR = 0.87, 95%CI: 0.76, 0.99), child aged 6–23 months (APR = 1.87, 95%CI: 1.53, 2.28) were all significantly associated with stunting.ConclusionIn Ethiopia, under-five children suffering from stunting have been found to exhibit a spatially clustered pattern. Maternal education, wealth index, birth interval and child age were determining factors of spatial variation of stunting. As a result, a detailed map of stunting hotspots and determinants among children under the age of five aid program planners and decision-makers in designing targeted public health measures.

IIt includes data that were used in the manuscript(A Geospatial and Binomial Logistic Regression Model to Prioritize Sampling for Per- and Polyfluorinated Alkyl Substances (PFAS) in Public Water Systems.) It includes layers that were created in online ArcGIS pro in manuscript and result of regression model that was done in the manuscript.

Many people assume that poor credit scores translate to higher interest rates. But is this assumption true? Follow Jonathan Blum, New York author and journalist, as he attempts to answer this question using GIS. In this lesson, you'll map variations in online loan interest rates. Then, you'll use regression analysis to build a predictive model, quantifying the relationship between interest rates and loan grade rankings.

This workflow can be used to map and measure the correlation between any two variables. It's perfect for anyone interested in regression analysis in ArcGIS Pro.

In this lesson you will build skills in these areas:

• Mapping interest rate hotspots
• Performing regression analysis
• Interpreting regression results
• Finding minimum neighbor distance
• Building the spatial regression model

Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

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 ...

Transects for historical shorline data spanning ~1880-2006 were created using the USGS Digial Shoreline Analysis software for ArcGIS 10.x. Tansects were cleaned, clipped to the shoreline change envelope (the distance between the shoreline farthest from and closest to the baseline at each transec) and merged with statisitical analyses from DSAS providing rate of chage data, confidence intervals, and other supplementary statistics. Additional desciptors were included to allow users to parse transects for omission (i.e., those that may reflect excessive development, fill, or do not meet certain quality conditions) as well as to group/associate transects by politcal town, drainage basin, or geologic zone.

Descriptive statistics for the non-standardised and standardised dependent and independent variables used as proxies for social disorganisation characteristics in Khayelitsha and Fort Lauderdale. The statistics are presented as raw variables prior to transformations. The spatial statistical techniques used to examine spatial patterns of violent crime and the associations with social disorganisation in Khayelitsha include: - exploratory spatial data analysis (ESDA) to explore the spatial distribution of violent crime in Khayelitsha; - bivariate correlation analysis using Pearson product-moment correlation; - a series of spatial regression models to examine the association between crime and a selection of structural neighbourhood characteristics in Khayelitsha.

Yearly citation counts for the publication titled "GIS-Based Analytical Tools for Transport Planning: Spatial Regression Models for Transportation Demand Forecast".

Descriptive statistics of all the variables in the regression models.

This paper examines the spatial distribution pattern and influencing factors of Martial Arts Schools (MASs) based on Baidu map data and Geographic Information System (GIS) in China. Using python to obtain the latitude and longitude data of the MASs through Baidu Map API, and with the help of ArcGIS (10.7) to coordinate information presented on the map of China. By harnessing the geographic latitude and longitude data for 492 MASs across 31 Provinces in China mainland as of May 2024, this study employs a suite of analytical tools including nearest neighbor analysis, kernel density estimation, the disequilibrium index, spatial autocorrelation, and geographically weighted regression analysis within the ArcGIS environment, to graphically delineate the spatial distribution nuances of MASs. The investigation draws upon variables such as martial arts boxings, Wushu hometowns, intangible cultural heritage boxings of Wushu, population education level, Per capita disposable income, and population density to elucidate the spatial distribution idiosyncrasies of MASs. (1) The spatial analytical endeavor unveiled a Moran’s I value of 0.172, accompanied by a Z-score of 1.75 and a P-value of 0.079, signifying an uneven and clustered distribution pattern predominantly concentrated in provinces such as Shandong, Henan, Hebei, Hunan, and Sichuan. (2) The delineation of MASs exhibited a prominent high-density core centered around Shandong, flanked by secondary high-density clusters with Hunan and Sichuan at their heart. (3) Amongst the array of variables dissected to explain the spatial distribution traits, the explicative potency of ‘martial arts boxings’, ‘Wushu hometowns’, ‘intangible cultural heritage boxings of Wushu’, ‘population education level’, ‘Per capita disposable income’, and ‘population density’ exhibited a descending trajectory, whilst ‘educational level of the populace’ inversely correlated with the geographical dispersion of MASs. (4) The entrenched regional cultural ethos significantly impacts the spatial layout of martial arts institutions, endowing them with distinct regional characteristics.

This dataset consists of long-term (less than 68 years) shoreline change rates for the exposed coast of the north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using a linear regression rate-of-change (lrr) method based on available shoreline data between 1948 and 2016. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate shoreline change rates.

Location and attributes for subbasins in the hydrologic analysis.

The S_Subbasins table contains data specific to each subbasin in the hydrologic analysis, including the relationship of the subbasin to the hydrologic network. The subbasin may be specific to a detailed hydrologic model. It may also correspond to the drainage area used in a regression analysis, or to the drainage area for a stream gage. S_Subbasins is intended to store HUC8 information related to the hydrologic model. This information is used in the Basin Characteristics tables in the FIS report, as well as for the FIRM Panel Index Map. The spatial elements representing the subbasins are polygons. In some cases the extent of these polygons may overlap, for example, cumulative subbasin areas for a regression analysis.

The regression analysis of predicting the degree of human impact by expert and non-expert groups, when the regression is split into 2 simultaneous models.

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.

Stream Management Corridors (SMCs) are the general corridors needed to allow a stream to function in a way that replicates natural processes to the extent possible. Stream Management Corridors should be considered when developing a site plan at the earliest stages of land planning or planning for a stream restoration project.This SMC dataset is based on a floodplain-based regression analysis. MHFD staff identified SMCs on both the eastern and western sides of the District’s service area where the previous shear stress-based method (released in 2019) produced unrealistic SMC widths. To improve accuracy, MHFD updated these SMCs using the revised floodplain-based approach. Last updated: 6/21/2025This is the first release of stream management corridors following the regression-based approach and should be used in place of the legacy shear stress based stream management corridors. SMCs depicted in dark green were developed using a very high level GIS desktop analysis based on a floodplain-based regression analysis. These are intended to be a starting point for planning purposes only, with refinement occurring through project design in combination with a more detailed science-backed methodology based on current practices.SMCs depicted in light green were developed using a field visit confirmed, peer reviewed, detailed analysis of the stream corridor. This may be fulfilled by a Fluvial Hazard Zone study or other scientifically defensible methods.SMCs are still under development for streams not showing an associated corridor.For more information about stream management corridors and flood hazard zones, visit the Colorado Flood Hazard Zone Mapping Program website.

BackgroundUtilization of modern contraceptives is a common healthcare challenge in Ethiopia. Prevalence of modern contraception utilization is varying across different regions. Therefore, this study aimed to investigate Geographic weighted regression analysis of hotspots of modern contraceptive utilization and its associated factors in Ethiopia, using Ethiopian Demographic and Health Survey 2016 data.MethodsBased on the 2016 Ethiopian Demographic Health Survey data, a total weighted sample of 8,673 women was included in this study. For the Geographic Weighted Regression analysis, Arc-GIS version 10.7 and SaTScan version 9.6, statistical software was used. Spatial regression was done to identify factors associated with the hotspots of modern contraceptive utilization and model comparison was carried out using adjusted R2 and AICc. Variables with a p-value < 0.25 in the bi-variable analysis were considered for the multivariable analysis. Multilevel robust Poisson regression analysis was fitted for associated factors since the prevalence of modern contraceptive was >10%. In the multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval was reported to declare the statistical significance and strength of association.ResultThe prevalence of modern contraceptive utilization in Ethiopia was 37.25% (95% CI: 36.23%, 38.27%). Most of the hotspot areas were located in Oromia and Amhara regions, followed by the SNNPR region and Addis Ababa City administration. Single Women, poor Women, and more fertility preference were significant predictors of hotspots areas of modern contraceptive utilization. In the multivariable multilevel robust Poisson regression analysis, Women aged 25–34 years (APR = 0.88, 95% CI: 0.79, 0.98), 35–49 years (APR = 0.71, 95% CI: 0.61, 0.83), married marital status (APR = 2.59, 95% CI: 2.18, 3.08), Others religions (APR = 0.76, 95% CI: 0.65, 0.89), number of children 1–4 (APR = 1.18, 95% CI: 1.02, 1.37), no more fertility preference (APR = 1.21, 95% CI: 1.11, 1.32), Afar, Somali, Harari, and Dire Dawa: (APR = 0.42, 95% CI: 0.27, 0.67), (APR = 0.06, 95% CI: 0.03, 0.12), (APR = 0.78, 95% CI: 0.62, 0.98), and (APR = 0.75, 95% CI: 0.58, 0.98), respectively. Amhara region (APR = 1.34, 95% CI: 1.13, 1.57), rural residence (APR = 0.80, 95% CI: 0.67, 0.95) High community wealth index (APR = 0.78, 95% CI: 0.67, 0.91) were significantly associated with modern contraceptive utilization.Conclusion and recommendationThere were significant spatial variations of factors affecting modern contraceptive use across regions in Ethiopia. Therefore, public health interventions targeting areas with low modern contraceptive utilization will help to increase modern contraception use considering significant factors at individual and community levels.The detailed map of modern contraceptive use cold spots among reproductive age group and its predictors could assist program planners and decision-makers to design targeted public health interventions.Government of Ethiopia must develop more geographic targeted strategies for improving socioeconomic status of women and availability & accessibility of health facilities in rural areas of the countries.

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Catalina Mountains by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

ObjectivesGout, a common form of inflammatory arthritis, is often managed with urate-lowering therapy, but many patients only adhere to treatment during gout attacks, resulting in poor follow-up and suboptimal management. This study aimed to identify characteristics associated with loss to follow-up (LTFU) and develop strategies for better patient management.MethodsData were analyzed from the Urate Lowering TheRApy in gout (ULTRA) registry, a prospective cohort of Korean gout patients recruited since September 2021. Patients aged 18 or older who met the 2015 ACR/EULAR classification criteria were included. Demographic data, clinical characteristics, lifestyle habits, comorbidities, and quality of life assessments using the Korean Gout Impact Scale (K-GIS) and EuroQol 5-Dimension (EQ-5D) were collected at baseline, six months, and annually. LTFU was defined as missing a clinic visit for more than a year. Logistic regression was used to determine factors associated with LTFU.ResultsAmong 269 patients, 125 (46.5%) were classified as LTFU. Patients not lost to follow-up experienced more frequent gout attacks (P = 0.020) and expressed greater concerns about future flares (P = 0.034). In contrast, LTFU patients had higher levels of anxiety (P = 0.049), depressive symptoms (P = 0.009), impaired mobility (P = 0.002), and a higher EQ-5D score (P = 0.002). Logistic regression identified frequent gout attacks, concerns about attacks, anxiety, impaired mobility, and EQ-5D scores as significant predictors of LTFU.ConclusionFewer gout attacks, heightened anxiety and depression, and lower quality of life were key factors associated with LTFU. Providing emotional support and comprehensive education may enhance follow-up and improve gout management.

This dataset consists of long-term (less than 68 years) shoreline change rates for the sheltered north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using a linear regression rate-of-change (lrr) method based on available shoreline data between 1948 and 2016. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate rates of change.

This dataset consists of long-term (~65 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2012. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate long-term rates.

This dataset includes: 1. The browser version of stream ecological enhancement tool; 2. JMP files including stream ecological enhancement tool metadata, multiple regression analysis, and logistic regression analysis; 3. R files and script for conducting structural equational modeling; 4. GIS package file for geospatial analysis.