Case02A1-Geocoding Hospitals and Estimating Euclidean and Manhattan Distances

Chapter 2 Measuring Distance and Time and Analyzing Distance Decay Behavior

This chapter uses two case studies to illustrate how to implement two common tasks encountered most often in spatial analysis: estimating a travel distance or time matrix and modeling distance decay behaviors. Case study 2A shows how to mea?sure distances and travel times between residents at the census block group level and acute hospitals in Baton Rouge, Louisiana. Case study 2B uses hospitalization data in Florida to demonstrate how to derive the best ? tting distance decay function by the spatial interaction model or the complementary cumulative distribution curve.

Case Study 2A: Estimating Travel Times to Hospitals in Baton Rouge

This case study illustrates how to estimate various travel time matrices between cen?sus block groups and hospitals in East Baton Rouge Parish (EBRP), hereafter simply referred to as Baton Rouge, Louisiana. The census block group data in the study area is based on the 2020 Census as explained in Chapter 1. The hospitals data is extracted from the membership directory of Louisiana Hospital Association (2021). Results from this project will be used in Case Study 4A of Chapter 4 that delineates hospital service areas by the Huff model, and in Case Study 5 of Chapter 5 to measure spatial accessibility of hospitals.

The following data sets are provided under the data folder BatonRouge :

1. a comma-separated text file Hosp _ Address.csv contains addresses, geographic coordinates, and numbers of staffed beds for the five acute hos?pitals in EBRP,

2. a zipped ESRI Shapefile feature class BR_Bkg.zip for the census block group data, a feature class BR_MainRd.zip for main roads, all in EBRP,

3. two ESRI Shape?les hosp.shp and BR_BkgPt.shp generated by Step 8 in Section 2.1 for geocoding data from Hosp_Address.csv , and centroids of census block group data, respectively, and

4. a CSV file OD _ Drive _ Time.csv generated by Step 12 in Section 2.1

This part of KNIME workflow cover 2 subsections:

2.1.1 Geocoding Hospitals from Street Addresses or Geographic Coordinates

2.1.2 Estimating Euclidean and Manhattan Distances Based on the OD Coordinates

Computational Methods and GIS Applications in Social Science - KNIME Lab Manual

Lingbo Liu, Fahui Wang

External resources

• Github for Workbook Issue Report
• Github for Geospatial Analytics