Public space
Computational Methods and GIS Applications in Social Science-KNIME Lab Manual
Data | |||
Case01-Mapping and Analyzing Population Density Pattern in Baton Rouge | |||
Case02A1-Geocoding Hospitals and Estimating Euclidean and Manhattan Distances | |||
Case02A2-Estimating and Comparing Distances by Google OSRM and Road Network | |||
Case02B-Analyzing Distance Decay Behavior for Hospitalization in Florida | |||
Case03A-Mapping Place Names in Guangxi China | |||
Case03B-Area-Based Interpolations of Population in Baton Rouge | |||
Case03C-Detecting Spatiotemporal Crime Hotspots in Baton Rouge | |||
Case04A-Defining Service Areas of Acute Hospitals in Baton Rouge | |||
Case04B1- Delineating Hospital Service Areas by the Refined Dartmouth Method | |||
Case04B2-Delineating Hospital Service Areas by Spatialized Network Community Detection Methods | |||
Case05A-Measuring Accessibility of Primary Care Physicians in Baton Rouge | |||
Case05B-Implementing the 2SVCA Method for Telehealth Accessibility | |||
Case05C-Sensitive Analysis for Measuring Accessibility by Workflow Automation | |||
Case06A-Function Fittings for Monocentric Models at the Census Tract Level | |||
Case06B-Function Fittings for Polycentric Models at the Census Tract Level | |||
Case06C-Function Fittings for Monocentric Models at the Township Level | |||
Case07-PCA FA CA and application in Social Area Analysis | |||
Case08A-Spatial Distribution and Clusters of Place Names in Yunnan | |||
Case08B-Detecting Colocation between Crime Incidents and Facilities | |||
Case08C-Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago | |||
Case09A-One Level Spatial Clustering | |||
Case09B-Calibrating Clustering Indicators WTVR and Compactness | |||
Case09C-Mixed Level Regionalization | |||
Case10-Implementing the Garin-Lowry Model in a Hypothetical City | |||
Case11A-Measuring Wasteful Commuting in Columbus | |||
Case11B1-Location-Allocation Analysis of Hospitals-Part 1 Data Preparation | |||
Case11B2-Location-Allocation Analysis of Hospitals-Part 2 location-allocation | |||
Case11B3-Location-Allocation Analysis of Hospitals-Part 3 Implement MAEP | |||
Case12A Deriving Urban Population Density Functions by Monte Carlo Simulation | |||
Case12B Monte Carlo Based Traffic Simulation in Baton Rouge | |||
Case14A-Spatiotemporal Big Data Analytic-Rebuilding Taxi Trajectory | |||
Case14B-Spatiotemporal Big Data Analytic-Aggregating Taxi Trajectory between Grids |
This space contains workflows that accompany the book: Computational Methods and GIS Applications in Social Science (3 Edition) and its KNIME Lab Manual.
DOI: 10.1201/9781003304357 (KNIME Lab Manual)
DOI: 10.1201/9781003292302 (Main book)
The book details applications of quantitative methods in social science, planning, and public policy with a focus on spatial perspectives. The book integrates GIS and quantitative (computational) methods and demonstrates them in various policy-relevant socio-economic applications with step-by-step instructions and datasets. The book demonstrates the diversity of issues where GIS can be used to enhance studies related to socio-economic issues and public policy.