Slides for Big Models, CLICK HERE
Comments from Miss Xiaohui Yuan @ Tsinghua University, http://citiesheart.com/2014/05/big-model/
Aiming at the paucity of urban parcels in developing countries in general and China in particular, this paper proposes a method to automatically identify and characterize parcels (AICP) with ubiquitous available OpenStreetMap (OSM) and Points of Interest (POIs). Parcels are the basic spatial units for fine-scale urban modeling, urban studies, as well as spatial planning. Conventional ways of 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 developing countries. Against this backdrop, we employ OSM road networks to identify parcel geometries and POI data to infer parcel characteristics. A vector-based CA 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.
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.
For more, see BCL Working Paper 31
As a vital indicator for measuring urban development, urban areas are expected to be identified explicitly and conveniently with widely available dataset thereby benefiting the planning decisions and relevant urban studies. Existing approaches to identify urban areas normally based on mid-resolution sensing dataset, socioeconomic information (e.g. population density) generally associate with low-resolution in space, e.g. cells with several square kilometers or even larger towns/wards. Yet, few of them pay attention to defining urban areas with micro data in a fine-scaled manner with large extend scale by incorporating the morphological and functional characteristics. This paper investigates an automated framework to delineate urban areas in the parcel level, using increasingly available ordnance surveys for generating all parcels (or geo-units) and ubiquitous points of interest (POIs) for inferring density of each parcel. A vector cellular automata model was adopted for identifying urban parcels from all generated parcels, taking into account density, neighborhood condition, and other spatial variables of each parcel. We applied this approach for mapping urban areas of all 654 Chinese cities and compared them with those interpreted from mid-resolution remote sensing images and inferred by population density and road intersections. Our proposed framework is proved to be more straight-forward, time-saving and fine-scaled, compared with other existing ones, and reclaim the need for consistency, efficiency and availability in defining urban areas with well-consideration of omnipresent spatial and functional factors across cities.
For more, see BCL Working Paper 34
As of January 2016
在经历了30多年的高速增长和快速扩张后，中国经济步入了“新常态”，并确立了“新型城镇化”战略。特别是，习近平2013年12月12日在中央城镇化工作会议上的讲话中指出，“…，城市建成区越摊越大，就会摊出不可治愈的城市病，甚至将来会出现一些‘空城’、‘鬼城’。”，以及“城市规划要由扩张性规划逐步转向限定城市边界、优化空间结构的规划”。近期的中央城市工作会议又对此进行了特别强调，体现了我国新区“中高强度建筑开发与低密度人类活动之间的悖论”。龙瀛所开展的一项研究（Long, 2016, Redefining Chinese city system with emerging new data, 工作论文）也显示，全国2009-2014扩张的城镇建设用地（新区）相比2009年以前的城镇建设用地（老区），道路交叉口密度占73%，城市功能（使用兴趣点points of interest数据）占25.3%，而人类活动（使用大众点评数据）占5.5%。即新区相比老区，对应着更大地块的物质空间开发，更低的城市功能承载，以及极低的人类活动强度。此外，广为讨论的“千城一面”也多体现于各个城市的新区。这在一定程度上也凸显了中国新区建设在城市形态、功能发育以及城市活力等方面的不足。