Case06C-Function Fittings for Monocentric Models at the Township Level

0 ×

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 6C: Function Fittings for Monocentric Models at the Township Level


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

Lingbo Liu, Fahui Wang


URL: Github for Geospatial Analytic https://github.com/spatial-data-lab/knime-geospatial-extension
URL: Github for Workbook Issue Report https://github.com/UrbanGISer/CGA-KNIME-Workbook/tree/main

Nodes

• Area7 ×
• Linear Regression Learner6 ×
• Math Formula5 ×
• Joiner2 ×
• Python Script2 ×
• GroupBy1 ×
• Row Filter1 ×

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Math Expression (JEP)
• FeatureKNIME Python Integration

Links

• No links available