1991). If neither the source zones nor target zones are homogeneous but we have access to a third set of zones with a surrogate variable that has a constant density, these control zones can be used in an intermediate stage of the areal weighting technique to interpolate in two steps, first to the control zones and then to the target zones (Goodchild et al., 1993).

Imagery derived from remote sensing platforms is an increasingly viable source of socioeconomic as well as physical data. Traditional satellite-based remote sensor systems were limited to spatial resolution no higher than 10 meters. New high-resolution sensing systems can achieve spatial resolutions 1 meter higher. Spectral resolution is also improving: new hyperspectral sensor systems such as the Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) capture more than 200 very narrow bands, providing a detailed spectral signature that allows discrimination to the subpixel level (i.e., the groundcover “mix” within a pixel). Jensen and Cowen (1999) discuss minimum spatial, temporal, and spectral resolutions required in remote sensing systems to extract urban and suburban infrastructure. Mesev, et al. (1996) discuss methods for inferring urban socioeconomic data from remote sensing imagery.

Spatial-Temporal Data and Inferential Statistics

The connectedness of livability in space and time is concerned with two other issues related to inferential statistics: namely, spatial dependence and spatial heterogeneity. We often refer to spatial dependence as spatial autocorrelation when discussing this property from a statistical perspective. Spatial dependence refers to the tendency of individuals or geographical units that are proximal in space to exhibit similar characteristics. Closely related is temporal dependence or temporal autocorrelation.

Spatial heterogeneity relates to the inadequacy of overall (system-wide) parameters in describing a specific phenomenon at individual locations. Spatial heterogeneity can occur for two reasons (Fotheringham, 2000). One reason is that some relationships are intrinsically different across space; for example, people’s behavior may vary by community or administrative, political, economic, and other boundaries or contexts. This creates contextually different responses to the same stimuli. Measuring spatial heterogeneity is a precursor to more intensive study to identify these contextual effects. Another reason is that the statistical model is not specified properly; one or more variables are missing or do not have the correct functional form. This statistical model can lead to misleading conclusions from the model. A classic example is a disaggregate spatial interaction (“gravity”) model leading to the conclusion that people in Albany, New York, are “jet-setters” compared to those in Los Angeles, California (Fotheringham, 1981). In this case, we must capture the spatial heteroge

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