both. An addition of a ‘visibility’ metric could also benefit a future analysis. Though destination visibility was implicitly embedded in some of the accessibility measures used in this dissertation (e.g. Turns Remoteness, Parcel Type), a more explicit measure could be proposed in future research. Such a measure could for example quantify the percentage of path length at which a destination becomes visible when approaching from all surrounding origins. Future specifications of the betweenness measure could experiment with measuring betweenness more accurately between buildings, rather than street intersections. Choosing buildings as origins and destinations of the measure would allow the calculated shortest paths to be weighted by building volume, producing a more reliable representation of the spatial distribution of potential trips in the network. Though this specification is currently quite taxing on computation power even in moderate-size networks 10 , such as the one used in our study area, we are confident that it will soon become feasible as computation power grows. Finally, future extensions of this research would also benefit from adding other means of transportation than walking to the analysis. Despite the current technical barriers of computer power and memory, we think that spatial accessibility measures in future work will be able to account for all available transportation modes around each location of interest, not just walking. Adding private automobile, public transit, bicycle, and taxicab accessibility measures to the analysis could eventually produce a more holistic and realistic description of transportation opportunities in urban settings. Instead of specifying a distance radius for accessibility, the radius could instead be defined in terms of time (e.g. a ten minute access radius on all transportation modes). We hope to move towards this goal in the near future. 5.1.3 Technical improvements The increasing availability of spatial data describing both the transient activity patterns of citizens and the more permanent physical pattern of cities, coupled with continuously improving computational tools, suggests that urban spatial analysis can grow in breadth and depth in the years ahead. Historic deficiencies in fine-grain urban data collection are already being reversed by electronic databases and digital communication technology. Computer applications capable of analyzing large amounts of spatial data are growing exponentially as the scholars of the city learn about programming and programmers learn about the city. Spatial econometric methods are evolving fast as open-source software allows thousands of users to test and improve new estimation routines. Though these developments do not always overlap in the same labs or research projects, they collectively suggest that urban spatial analysis offers unprecedented opportunities to better understand the relationships between the social and spatial processes of a city. The key data requirements we have relied on consist of a) a description of activities accommodated in buildings, b) a description of building sizes, c) locations of transit stations, and d) a description of the street network. Though we have used buildings as our spatial units of analysis, it would also be possible to
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The author used a Mac Pro desktop computer with eight 2GH Intel processors, 2GB of RAM, and the OS Windows XP.
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