Rian Amiton GIS – UEP 232 1/23/09 Assignment – GIS Project Example Project name: “Proximity to Trails and Retail: Effects on Urban Cycling and Walking” Source: Journal of the American Planning Association, Winter 2006, 72 (1): 33-42 Researchers: Kevin J Krizek and Pamela Jo Johnson Data source: 2000 Twin Cities Metropolitan Area Travel Behavior Inventory (TBI), a survey “administered by the regional planning agency, the Twin Cities’ Metropolitan Council”, to determine travel behaviors across the Twin Cities region Software utilized: Not given This study was conducted by two University of Minnesota professors, one from the urban and regional planning department and the other from the school of public health The purpose of the study was to determine if there is any statistical relationship between proximity to bicycle infrastructure (on-street and off-street trails) or retail establishments and the likelihood to travel by bicycle or walking, respectively If positive correlations are found, then it might suggest that policies promoting bicycle infrastructure and/or mixed use neighborhoods also reduce auto traffic and promote health The data source, TBI, consists of travel diaries taken on the same day by 1,653 residents of Minneapolis and St Paul who were 20 years of age or older The authors used the TBI information to plot out exactly where the residents lived who traveled either by foot or bicycle They then compared that information against where bicycle trails (supplied by the Minnesota Department of Transportation) and retail establishments (supplied by the Minnesota Department of Employment and Economic Development) were located They then developed four gradations of proximity to both bicycle trails and retail establishments, controlled for various demographic data (age, income, etc.) and ran the numbers through several binary logistic regression models This kind of study, which looks at behavior relative to proximity to physical features, seems perfect for a GIS component Hard data is useful when it is arranged in tables (and the authors this), and in fact the authors of this study developed their conclusions from data tables rather than maps But when the data is integrated into a map, the conclusions can be much easier to understand Unfortunately, I don’t think the authors a very good job translating the data into a very comprehensible visual format The paper includes three GIS maps Figure includes points indicating where bicyclists (according to TBI) live and vectors marking bicycle trails around the Twin Cities area In the text of the article, the authors explain that there is some statistical correlation between the two (though it is not terribly strong) However, I find it kind of hard to see this with the map alone; it seems they could have included more information For instance, I think it would have been more informative had they broken the area up into segments and added a color-coded rastor layer indicating the percentage of cyclists in each segment or distances (e.g close, moderate and far) from bicycle trails on the map The other two maps (Figure 2) are, to my eye, even less helpful than the first One is just points indicating “walker” residences spread around the region and the other is just points showing retail establishments Because the two sets of information are split into separate maps, it is extremely difficult to come to a visual conclusion as to their exact relationship with each other Again, I think a rastor layer (this one perhaps indicating the density of retail establishments within small segments) beneath points indicating residents would have been a better approach Perhaps the authors chose to forego rastor layers due to the fact that the article is printed in black and white, which has limited capacity to express subtle value differences, while a truly effective rastor layer translating sufficient information would have to take into account many gradations of behavior, requiring several colors or shades One potential issue with this study is the data source Because TBI captured the events of only one day, many factors could have easily skewed its findings (for instance, perhaps particularly inclimate weather that day influenced a disproportionate number of people to drive rather than walk or ride their bicycle); another study spread over a longer period of time might yield different results In addition, the authors themselves admit that correlation does not imply causation; it’s entirely possible that rather than bicycle trails influencing people near them to ride their bikes, bicyclists just prefer to live near bicycle infrastructure Last year (after this paper was written) a major new bicycle thoroughfare was opened through the heart of the Minneapolis; it would be really interesting to see if the travel behavior of existing (rather than newly-transplanted) residents near it has changed since its opening ... information are split into separate maps, it is extremely difficult to come to a visual conclusion as to their exact relationship with each other Again, I think a rastor layer (this one perhaps... printed in black and white, which has limited capacity to express subtle value differences, while a truly effective rastor layer translating sufficient information would have to take into account many... maps (Figure 2) are, to my eye, even less helpful than the first One is just points indicating “walker” residences spread around the region and the other is just points showing retail establishments