EFFECT OF SPATIAL SMOOTHING ON MAP USE: A CASE OF FACILITY CHOICE

Spatial smoothing is one of the spatial operations in GIS. It makes spatial information vague and ambiguous to some extent, in order to hide the exact location or attribute of spatial objects. Distribution of cases of an infectious disease is often visualized as a surface generated by applying spatial smoothing to the point distribution. Since spatial smoothing is a kind of conversion of spatial data, use of visualized spatial data also changes with spatial smoothing. Let us consider, for instance, a crime pattern analysis. If we look at the exact locations of crime occurrences on a map, we may find local patterns in the distribution. If the distribution is smoothed, we may focus on rather a global structure of the distribution. The degree of smoothing affects the way and result of map use. Though this has been widely recognized in cartography and GIS, few studies have been done in the literature. To fill the gap of the research, this paper analyzes the effect of spatial smoothing in map use. A focus is on a facility choice on a map choice of a hotel on a crime rate map of a tourist city. The location of hotels is indicated by point symbols on a map that shows the distribution of crime rate in a city. The crime rate is calculated from the point data of crime occurrences by spatial smoothing. Room rates of hotels are also shown outside the maps. An experiment is performed that asks subjects to choose a hotel from several options on each map. The observed data is used to build and estimate a stochastic model representing the hotel choice behavior. The model serves for not only explaining the choice behavior of a hotel but also discussing it in relation to the degree of spatial smoothing. INTRODUCTION Point is the most fundamental spatial object used in GIS. It represents various kinds of facilities, activities, and abstract concepts in the real world (Longley et al., 1999). Point data are visualized in GIS by point symbols, in which map users can see their exact locations (Figure 1a). However, in practice, it often happens that their exact locations have to be hidden from map users. For instance, though census data are collected at individual level, they are aggregated by spatial units when open to public in order to keep the confidentiality of individuals. Similarly, epidemiological maps often come in choropleth maps, where the exact location of disease cases is not precisely indicated. Even the distribution of crime occurrences is converted into surface data, because the crime occurrence reduces the land value. There are several methods to hide the exact location of points from map users (Bailey and Gatrell, 1995). A popular method is to aggregate point data into spatial units such as census tracts, administrative units, and square lattice (Figure 1b). Counting the number of points in each spatial unit, we store the number of points as an attribute of the units. Choropleth map is a typical form of visualizing the count data. Another method is to apply the spatial smoothing to point data (Figure 1c). Various methods including the moving average method and its variants, and the kernel method (Silverman 1986, Scott, 1992) are used to convert a point distribution into a surface. Spatial aggregation yields discrete surfaces while spatial smoothing gives continuous ones.