Case03B-Area-Based Interpolations of Population in Baton Rouge

Chapter 3 Spatial Smoothing and Spatial Interpolation

This chapter covers two generic tasks in GIS-based spatial analysis: spatial smoothing and spatial interpolation. Both are useful to visualize spatial patterns and highlight spatial trends. Spatial smoothing computes the average values of a variable in a larger spatial window to smooth its variability across space. Spatial interpolation uses known (observed) values at some locations to estimate (interpolate) unknown values at any given locations.

There are three case studies. The first case study of place names in southern China illustrates some basic spatial smoothing and interpolation methods. The second illustrates how to use area-based spatial interpolation methods to transform population data between different census areal units. The third demonstrates how to use the spatio-temporal kernel density estimation (STKDE) method for detecting spatiotemporal crime hotspots.

Case Study 3B:Area-Based Interpolations of Population in Baton Rouge

Transforming data from one area unit to another or areal interpolation is a common task in spatial analysis for integrating data of different scales or resolutions. This case study illustrates two methods: areal weighting interpolation and Target-Density Weighting (TDW) Interpolation.

Data needed for the project are provided in the subfolder BR under the folder BatonRouge :

1. features BRTrt2010.zip and BRTrtUtm.zip for census tracts in the study area in 2010 and 2020, respectively, and

2. feature BRUnsd.zip for unified school districts in the study area in 2020

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