The use of micro-models as supplements for macro-models has become an accepted approach into the investigation of urban dynamics. However, the widespread application of micro-models has been hindered by a dearth of individual data, due to privacy and cost constraints. A number of studies have been conducted to generate synthetic individual data by reweighting large-scale surveys. The present study focused on individual disaggregation without micro-data from any large-scale surveys. Specifically, a series of steps termed Agenter (a portmanteau of “agent producer”) is proposed to disaggregate heterogeneous agent attributes and locations from aggregate data, small-scale surveys, and empirical studies. The distribution of and relationships among attributes can be inferred from three types of existing materials to disaggregate agent attributes. Two approaches to determining agent locations are proposed here to meet various data availability conditions. Agenter was initially tested in a synthetic space, then verified using the acquired individual data, which were compared to results generated using a null model. Agenter generated significantly better disaggregation results than the null model, as indicated by the proposed similarity index (SI). Agenter was then used in the Beijing Metropolitan Area to infer the attributes and location of over 10 million residential agents using a census report, a household travel survey, an empirical study, and an urban GIS database. Agenter was validated using micro-samples from the survey, with an average SI of 72.6%. These findings indicate the developed model may be suitable for using in the reproduction of individual data for feeding micro-models.
We have finished the tool which is now a module of our fresh new model BUDEM2.
For more details, please see our BUDEM2 paper.
Population individuals together with their locations & attributes are essential for feeding micro-level applied urban models (like spatial micro-simulation and agent-based modeling) for policy evaluation. Existing research on population spatialization and population synthesis is generally separated. In developing countries like China, population distribution in a fine scale, as the input for population synthesis, is not universally available. With the open-government initiatives in China and the emerging Web 2.0 techniques, more and more open data are becoming achievable. In this chapter, we propose an automatic process using open data for population spatialization and synthesis. Specifically, road network in OpenStreetMap is used to identify and delineate parcel geometries, while crowd-sourced POIs are gathered to infer urban parcels with a vector cellular automata model. Housing-related online Check-in records are then applied for selecting residential parcels from all identified urban parcels. Finally the published sub-district level population census, in which distribution of attributes and relationships between attributes are available, is used for synthesizing population attributes supported by a previous developed tool Agenter (Long and Shen, 2013). The results have been validated with ground truth manually-prepared dataset by planners in Beijing Institute of City Planning.
This paper appears in the Springer book "Geospatial Analysis for Supporting Urban Planning in Beijing" as a chapter.