Case06A-Function Fittings for Monocentric Models at the Census Tract Level

This chapter discusses how to identify the best fitting function to capture urban and regional population density patterns. Such an approach emphasizes the in¬ fluence of a center or multiple centers on areawide density patterns in a city or across a region. By examining the change of density function over time, one can detect the growth pattern for urban and regional structures. The methodological focus is on function fittings by regressions and related statistical issues.

Chicago has been an important study site for urban studies attributable to the legacy of so-called “Chicago school”. The study area is the core six counties (Cook, DuPage, Kane, Lake, McHenry and Will) in Chicago CMSA based on the 2000 census data. The project analyzes the density patterns at both the census tract and survey township levels to examine the possible modi¬fiable areal unit problem (MAUP) .

The following features and Python files in the subfolder ChiUrArea under the folder Chicago are provided:

1. Census tract feature trt2k.zip for the larger 10-county MSA region is used to extract census tracts in this 6-county study area ( field “ popu ” is the population data in 2000).

2. Feature polycent15.zip contains 15 centers identified as employment concentrations from a previous study (Wang, 2000), which includes the Central Business District (CBD) with eld CENT15_ = 12.

3. Feature twnshp.zip contains 115 survey townships in the study area, providing an alternative areal unit that is relatively uniform in area size.

4. Feature cnty6.zip defines the 6-county study area.

5. Three Python script snippet files, NonlinearRegression.py , WeightedOLS.py and NonlinearRegressionAssumption3.py , implement various regression models.

Case 6A: Function Fittings for Monocentric Models at the Census Tract Level

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