Introduction

The project intends to use traditional statistics (regression and sampling) to tackle the relationship between the change in low-density residential land use and the change in individual households' tax burden. However, dealing with the spatial aspects of the project helps understand the complexity of the data in order to increase the significance of the result of future regression models. 

In fact, the special nature of spatial data, due to the fact that the location of the observations provides important information, requires appropriate methodologies for their analysis, developed in the fields of spatial statistics and spatial econometrics. The basic difference between traditional statistics and spatial statistics is the traditional assumption of independent units of analysis, which in fact are spatially autocorrelated in some ways in the case of the project where MCDs are units of analysis.

ESDA (Exploratory spatial data analysis) is concerned with the detection, analysis, and interpretation of spatial patterns in the data, such as spatial clusters, outliers, and hot spots. Several measures of spatial autocorrelation can be treated as well. Spatial regression analysis deals with the effects of the special nature of geographic data on the properties of regression models. One aspect of this is the detection of spatial autocorrelation as a specification error in regression models. 

At the current stage of the project, bi-variate thematic mapping and spatial autocorrelation mapping is used to observe if there is any spatial pattern within the dataset. Mapping as well as animation are used to observe spatial intervening factors that can not be taken into account in traditional regression models such as the effect of highways and sewer zones on the location of new land uses and the location of a MCD within a regional context. This will facilitate the ESDA and spatial regression analysis later on in SpaceStat.