Location Based Services (LBS) provide a new perspective for spatiotemporally analyzing dynamic urban systems. Research has investigated urban dynamics using GSM (Global System for Mobile Communications), GPS (Global Positioning System), SNS (Social Networking Services) and Wi-Fi techniques. However, less attention has been paid to the analysis of urban structure (especially commuting pattern) using smart card data (SCD), which are widely available in most cities. Additionally, ubiquitous LBS data, although providing rich spatial and temporal information, lacks rich information on the social dimension, which limits its in-depth application. To bridge this gap, this paper combines bus SCD for a one-week period with a one-day household travel survey, as well as a parcel-level land use map to identify job-housing locations and commuting trip routes in Beijing. Two data forms (TRIP and PTD) are proposed, with PTD used for jobs-housing identification and TRIP used for commuting trip route identification. The results of the identification are aggregated in the bus stop and traffic analysis zone (TAZ) scales, respectively. Particularly, commuting trips from three typical residential communities to six main business zones are mapped and compared to analyze commuting patterns in Beijing. The identified commuting trips are validated on three levels by comparison with those from the survey in terms of commuting time and distance, and the positive validation results prove the applicability of our approach. Our experiment, as a first step toward enriching LBS data using conventional survey and urban GIS data, can obtain solid identification results based on rules extracted from existing surveys or censuses.

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The detection of clustering in a spatial phenomenon of interest is an important issue in spatial pattern analysis. While traditional methods mostly rely on the planar space assumption, many spatial phenomena defy the logic of this assumption. For instance, certain spatial phenomena related to human activities are inherently constrained by a transportation network because of our strong dependence on the transportation system. This article thus introduces an exploratory spatial data analysis method named local indicators of network‐constrained clusters (LINCS), for detecting local‐scale clustering in a spatial phenomenon that is constrained by a network space. The LINCS method presented here applies to a set of point events distributed over the network space. It is based on the network K‐function, which is designed to determine whether an event distribution has a significant clustering tendency with respect to the network space. First, an incremental K‐function is developed so as to identify cluster size more explicitly than the original K‐function does. Second, to enable identification of cluster locations, a local K‐function is derived by decomposing and modifying the original network K‐function. The local K‐function LINCS, which is referred to as KLINCS, is tested on the distribution of 1997 highway vehicle crashes in the Buffalo, NY area. Also discussed is an adjustment of the KLINCS method for the nonuniformity of the population at risk over the network. As traffic volume can be seen as a surrogate of the population exposed to a risk of vehicle crashes, the spatial distribution of vehicle crashes is examined in relation to that of traffic volumes on the network. The results of the KLINCS analysis are validated through a comparison with priority investigation locations (PILs) designated by the New York State Department of Transportation.

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Proximity is a fundamental concept in any comprehensive ontology of space (Worboys 2001). The provision of a context‐contingent translation mechanism between linguistic proximity measures (e.g. “near”, “far”) and metric distance measures is an important topic in current GIS research. After a discussion of context factors that mediate the relationship between linguistic and metric distance measures, we present a statistical approach, Ordered Logit Regression, to the context‐contingent proximity modeling. The approach can predict proximity given the corresponding metric distance and context variables. An empirical case study with human subjects is carried out using this statistical approach. Interpretation and predictive accuracy of the empirical case study are discussed.

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