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Explore Distance-decay Relationship with Python
Modern geographers and urban planners frequently utilise spatial analysis to understand how human activities and land uses are dispersed across the landscape. One of the fundamental concepts at the heart of these studies is the distance-decay relationship. Distance-decay models describe how the interaction or influence between two points decreases as the distance between them increases. These models have a wide range of applications, and they are invaluable tools in understanding and predicting spatial patterns and interactions in the geospatial industry.
Python is a powerful tool to do distance-decay analyses. In this article, we will be using Python to analyse the relationship between Queensland’s residential areas and other land uses, in order to uncover intriguing spatial patterns and insights.
TABLE OF CONTENTS
- Programming Environment and Libraries
- Example Dataset Overview
- Step 1: Extract Data from ArcGIS Online and Convert to GeoPandas DataFrame for Analysis
- Step 2: Filter Data for the Suburb
- Step 3: Compute the Distance Between Residential Areas and the Land Use
- Step 4: Visualise the Distance-decay Relationship
- Analysis Example
Programming Environment and Libraries
To do distance-decay analysis, we will be using Python in the Jupyter Notebook environment. GeoPandas, Shapely, and the ArcGIS.GIS modules will be imported and utilised.
Example Dataset Overview
In this article, we take Queensland, Australia for example and analyse the distance-decay relationship between the residential areas and various land uses across the suburbs. There are two keydataset used for this analysis.
1. Queensland Locality Boundaries
This dataset encompasses all 3,305 suburbs within Queensland.
(Data Source: Data.gov.au)
