Spatial Smoothing and Spatial Interpolation

• Published in: Computational Methods and GIS Applications in Social Science - Lab Manual pp. 63 - 86
• Main Authors: Liu, Lingbo, Wang, Fahui
• Format: Book Chapter
• Language: English
• Published: United Kingdom CRC Press 2024; Taylor & Francis Group
• Edition: 1
• Subjects: Floating Catchment Area Method; Case Study 3C; Spatial Smoothing; Study Area Size; Column Selection; Joiner Node; Case Study 3A; Bottom Input; Areal Weighting Method; Time Window Size; CSV File; Aggregation Method; Input Ports; Kernel Density Estimation; Population Change Rates; Ancillary Variable; Spatial Interpolation; Origin Id; Top Input; Destination Id; Group Column; Census Tracts; Groups Tab; Id Column
• ISBN: 9781032302430; 1032302437
• DOI: 10.1201/9781003304357-3

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.