Project Approach

In our project, we will implement the methodologies covered by the case study, while also learning from the BURP application as reviewed in the previous literature review section. We will also add in other functionalities not covered.

Getis and Ord’s G-Statistics

• Involves deriving adaptive proximity spatial weight matrix
• The Gi statistics will be computed and mapped for identification of the statistically significant hot spot and cold spot areas for where either high or low values (respectively) cluster spatially
• This method produced 1 map and is chosen over Local Moran’s I which requires us to compare the I value and p-value maps to come to a conclusion. Reducing the need for interpretation allows for more user-friendliness

Hansen’s Accessibility Index

• Measure accessibility of an estate area based on the number of schools, weighted by the travel time required
• More time needed means less attractive

Kernel Density Two-Step Floating Catchment Area (KD2SFCA) Method

• Gravity model of spatial interaction that uses floating catchment areas which overlap
• Displays the distance accessibility using polygons formed by contour lines, with intensity implying concentration of buildings