Against the paucity of information on urban parcels in China, we propose a method to automatically identify and characterize parcels using OpenStreetMap (OSM) and points of interest (POI) data. Parcels are the basic spatial units for fine-scale urban modeling, urban studies, and spatial planning. Conventional methods for identification and characterization of parcels rely on remote sensing and field surveys, which are labor intensive and resource consuming. Poorly developed digital infrastructure, limited resources, and institutional barriers have all hampered the gathering and application of parcel data in China. Against this backdrop, we employ OSM road networks to identify parcel geometries and POI data to infer parcel characteristics. A vector-based cellular automata model is adopted to select urban parcels. The method is applied to the entire state of China and identifies 82 645 urban parcels in 297 cities. Notwithstanding all the caveats of open and/or crowd-sourced data, our approach can produce a reasonably good approximation of parcels identified using conventional methods, thus it has the potential to become a useful tool.
There is a long-running debate in the planning literature about the effects of the built environment on travel behavior and the degree to which apparent effects are due to the tendency of households to self-select into neighborhoods that reinforce their travel preferences. Those who want to walk will choose walkable neighborhoods, and those who want to use transit will choose transit-served neighborhoods. These households might have walked or used transit more than their neighbors wherever they lived. Most previous studies have shown that individual attitudes attenuate the relationship between the residential environment and travel choices, and so the effect of the built environment on travel may be overestimated. But there are other researchers who argue the reverse, claiming that residential preferences reinforce built environmental influences. This study assesses the relative importance of the built environment and residential preferences/travel attitudes for a sample of 962 households in the Greater Salt Lake region using structural equation modeling. For the sake of simplicity, we extracted two factors using principal component analysis, one representing the built environment and the other representing residential preferences/attitudes. Our findings are consistent with the view that the neighborhood built environment and residential preferences both influence household's travel, that the built environment is the stronger influence, and that the built environment affects travel through two causal pathways, one direct and the other indirect, through attitudes.
Understanding individual daily activity patterns is essential for travel demand management and urban planning. This research introduces a new method to infer individuals' activities from their mobile phone traces. Using Metro Boston as an example, we develop an activity detection model with travel diary surveys to reveal the common laws governing individuals' activity participation, and apply the modeling results to mobile phone traces to extract the embedded activity information. The proposed approach enables us to spatially and temporally quantify, visualize, and examine urban activity landscapes in a metropolitan area and provides real-time decision support for the city. This study also demonstrates the potential value of combining new big data such as mobile phone traces and traditional travel surveys to improve transportation planning and urban planning and management.
One of the important bottlenecks to the wider adoption of planning support systems is a lack of evidence about whether they improve planning processes. In addition, existing research does not include field studies with high context validity. This paper fills this gap in the literature by reporting the results from a field study evaluating the performance of a planning support systems used for sketch planning as part of a land use planning project in metropolitan Austin, Texas, USA. Participants reported high levels of learning and dialog quality, the two chosen planning support systems performance measures. A regression analysis finds both are related to the ability of planning support systems to change participants’ perceptions. In addition, as suggested by learning theory, the planning support systems performance outcomes are significantly related to participants’ identity as a planner and meeting attendance, but are not related to gender or educational attainment. Finally, for the planning support systems studied here, participation in the planning support systems creation is less important than other factors in explaining planning support systems performance. The paper contributes to efforts to develop and implement robust measures of planning support systems performance and links concept of planning support systems performance to broader theories of learning.
