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.

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.

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.

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.

The digital economy (DE) has become a major breakthrough in promoting industrial upgrading and an important engine for high-quality economic growth. However, most studies have neglected the important driving effect of regional economic and social (RES) development on DE. In this paper, we discuss the mechanism of RES development promoting the development of DE, and establish a demand-driven regional DE development model to express the general idea. With the help of spatial analysis toolbox in ArcGIS software, the spatial development characteristics of DE in the Yangtze River Delta City Cluster (YRDCC) is explored. We find the imbalance of spatial development is very significant in YRDCC, no matter at the provincial level or city level. Quantitative analysis reveals that less than 1% likelihood that the imbalanced or clustered pattern of DE development in YRDCC could be the result of random chance. Geographically weighted regression (GWR) analysis with publicly available dataset of YRDCC indicates RES development significantly promotes the development of DE.

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.

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 ...

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 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.

Descriptive statistics of all the variables in the regression models.

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".

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.

🇬🇧 영국 English The primary objective from this project was to acquire historical shoreline information for all of the Northern Ireland coastline. Having this detailed understanding of the coast’s shoreline position and geometry over annual to decadal time periods is essential in any management of the coast.The historical shoreline analysis was based on all available Ordnance Survey maps and aerial imagery information. Analysis looked at position and geometry over annual to decadal time periods, providing a dynamic picture of how the coastline has changed since the start of the early 1800s.Once all datasets were collated, data was interrogated using the ArcGIS package – Digital Shoreline Analysis System (DSAS). DSAS is a software package which enables a user to calculate rate-of-change statistics from multiple historical shoreline positions. Rate-of-change was collected at 25m intervals and displayed both statistically and spatially allowing for areas of retreat/accretion to be identified at any given stretch of coastline.The DSAS software will produce the following rate-of-change statistics:Net Shoreline Movement (NSM) – the distance between the oldest and the youngest shorelines.Shoreline Change Envelope (SCE) – a measure of the total change in shoreline movement considering all available shoreline positions and reporting their distances, without reference to their specific dates.End Point Rate (EPR) – derived by dividing the distance of shoreline movement by the time elapsed between the oldest and the youngest shoreline positions.Linear Regression Rate (LRR) – determines a rate of change statistic by fitting a least square regression to all shorelines at specific transects.Weighted Linear Regression Rate (WLR) - calculates a weighted linear regression of shoreline change on each transect. It considers the shoreline uncertainty giving more emphasis on shorelines with a smaller error.The end product provided by Ulster University is an invaluable tool and digital asset that has helped to visualise shoreline change and assess approximate rates of historical change at any given coastal stretch on the Northern Ireland coast.

This data contains treatment and confounder data used in the preprint "Understanding Spatial Regression Models from a Weighting Perspective in an Observational Study of Superfund Remediation" (Woodward, Dominici, Zubizarreta). The final dataset, named buffers, is at the level of the Superfund site (n = 1583). This dataset can be accessed by loading preprocessed_superfunds.RData into R. The binary treatment data, describing whether a Superfund site was remediated and removed from the National Priorities List between 2001 and 2015, is derived from publicly-available data on Superfund site status (source: EPA ArcGIS). Confounder data is derived from the 2000 Decennial Census using tidycensus. The R code used to curate this dataset directly from the publicly available data sources is provided (preprocessing.R).

Regression analysis for the model Yi = a+bXi+εi, where Yi is the degree of human impact from the control data, Xi is the degree of human impact from the participants.

This dataset consists of short-term (1970-2009) linear regression shoreline change rates for the Buzzards Bay region of Massachusetts. Rates of short-term shoreline change 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. The baseline is used as a reference line for the transects cast by the DSAS software. The transects intersect each shoreline at the measurement points, which are then used to calculate the short-term rates. Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. The Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) in cooperation with the Massachusetts Office of Coastal Zone Management, has compiled reliable historical shoreline data along open-facing sections of the Massachusetts coast under the Massachusetts Shoreline Change Mapping and Analysis Project 2013 Update. Two oceanfront shorelines for Massachusetts (approximately 1,800 km) were (1) delineated using 2008/09 color aerial orthoimagery, and (2) extracted from topographic LIDAR datasets (2007) obtained from NOAA's Ocean Service, Coastal Services Center. The new shorelines were integrated with existing Massachusetts Office of Coastal Zone Management and USGS historical shoreline data in order to compute long- and short-term rates using the latest version of the Digital Shoreline Analysis System (DSAS).

This dataset consists of short-term (~33 years) shoreline change rates for the north coast of Alaska between the Colville River and Point Barrow. 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. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 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 short-term rates.

This dataset consists of long-term (~63 years) shoreline change rates for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. 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 2010. 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.