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Ying Long

Publications and source records attributed to Ying Long.

20 records · Page 2Linked to original sources

Simulating urban expansion in the parcel level for all Chinese cities

Large-scale models are generally associated with big modelling units in space, like counties or super grids (several to dozens km2). Few applied urban models can pursue large-scale extent with fine-level units simultaneously due to data availability and computation load. The framework of automatic identification and characterization parcels developed by Long and Liu (2013) makes such an ideal model possible by establishing existing urban parcels using road networks and points of interest for a super large area (like a country or a continent). In this study, a mega-vector-parcels cellular automata model (MVP-CA) is developed for simulating urban expansion in the parcel level for all 654 Chinese cities. Existing urban parcels in 2012, for initiating MVP-CA, are generated using multi-levelled road networks and ubiquitous points of interest, followed by simulating parcel-based urban expansion of all cities during 2012-2017. Reflecting national spatial development strategies discussed extensively by academics and decision makers, the baseline scenario and other two simulated urban expansion scenarios have been tested and compared horizontally. As the first fine-scale urban expansion model from the national scope, its academic contributions, practical applications, and potential biases are discussed in this paper as well.

cs.MA↗

Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing

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.

cs.SI↗