人口数据空间化研究进展

doi:10.3724/SP.J.1047.2016.01295

摘要/Abstract

摘要：

人口数据空间化旨在揭示人口在地理空间上的分布位置及数量信息,展现人口统计数据的地理学含义,其研究已经成为人口学、地理学、GIS领域的研究热点。人口空间数据库在各级政府部门的规划和决策、灾害评估、资源配置等方面,具有重要的应用价值和科学意义。经过近30年的发展,人口数据空间化研究水平逐渐成熟,模型丰富多样,已获得众多成果。为把握人口空间化研究的研究现状,本文首先依据研究目的、建模思想及模型原理的异同,从3个方面对人口空间化研究进行梳理：（1）格网大小（尺度）的确定;（2）3种常用建模思想及6类主要模型的对比分析;（3）提高人口空间化精度的措施及其应用背景、优点。在此基础上,依据现阶段人口数据空间化的研究内容,从格网尺度适宜性研究、高时空分辨率人口空间分布模拟、引入新型数据源及多思想多模型综合应用等方面探讨了人口数据空间化的研究方向。

关键词: 人口, 空间化, 格网, 模型, 尺度

Abstract:

The research purpose of the spatialization of population data is to capture the size and the distribution location of population in the geographical space. It plays an important role in presenting the geographical meaning of demographic data. Spatializing the statistical population data has increasingly become a research hotspot in the fields of demography, geography and GIS. Population distribution dataset is a key achievement in the spatialization study. At present, there are a few widely-used population distribution datasets and influential population spatialization projects, including GPW/GRUMP, LandScan and UNEP/GRID & China km grid population datasets. Population distribution dataset has practical application values and the scientific significance for relevant researches, such as government planning and decision making at all levels, disaster assessment and resource allocation. After nearly 30 years' development, the spatialization researches are evolving into the maturity stage. They have obtained many achievements and produce a rich variety of spatialization models of population data. Based on the purpose of spatializing census data and the differences between modeling concept and model principle, this paper reviews the spatialization methodologies in three major aspects: (1) the method and characteristic of the selection of grid size (scale); (2) 3 types of common adopted modeling ideas and a comparative analysis between 6 types of basic models; and (3) the proper strategies used for improving the simulation accuracy and their application background and advantages. Finally, according to the research contents of population data spatialization at present stage, this article discusses the further study direction through four perspectives: (1) the suitability of grid size; (2) the simulation of spatial distribution of population at high spatial and temporal resolution; (3) the adoption of new type of data source; and (4) the comprehensive application of multi-thought and multi-model. It is significant to grasp the current status of spatialization research and promote the further development of spatialization methodologies.

Key words: population, spatialization, grid, model, scale

董南, 杨小唤, 蔡红艳. 人口数据空间化研究进展[J]. 地球信息科学学报, 2016, 18(10): 1295-1304.DOI:10.3724/SP.J.1047.2016.01295

DONG Nan,YANG Xiaohuan,CAI Hongyan. Research Progress and Perspective on the Spatialization of Population Data[J]. Journal of Geo-information Science, 2016, 18(10): 1295-1304.DOI:10.3724/SP.J.1047.2016.01295

图/表 2

表1

表2

人口数据空间化主要模型综合比较

模型名称	建模参数	原理	优点	局限性
负指数模型及改进模型^[66-67]	中心人口密度理论值、距市中心距离、城市特征半径等	基于城市地理学原理,人口密度从城市中心向外围递减,人口围绕城市中心成圆形分布	模型简单,是众多模型理论基础,适用于大中城市人口密度模拟	市中心确定较为主观,不适用于小城市及乡村地区
核密度估计模型^[68-69]	区域（格网）中心人口密度、带宽等	人口密度从区域中心向外围递减,基于人口加权质心将人口密度内插到格网面	引入统计分析法,模拟人口连续分布情况;模型允许部分栅格无人口,趋于实际	未考虑人口空间分布影响因素,带宽τ值确定较主观
分区密度模型^[18,59]	人口数量、土地类型、地形地貌、交通路网、不透水面等	假设面元内同一类别分区上人口分布一致,通过面插值技术实现人口空间化	模型简单,易实现,保证源区域与目标区域人口总量不变	各分区的人口分配权重确定较为困难;无法揭示同一类别分区内人口分布异同
多元回归模型^[17,62,70]	各土地类型面积,并用DEM、居民点等数据进行修正	土地面积与人口强相关性;假定某分区下同一土地类型人口密度相同,遵从“无土地则无人口”原则	适合中大尺度研究,参数少,易于建模,便于推广,结果较为可控	相同土地类型下难以揭示人口分布差异
多元回归模型^[17,62,70]	夜间灯光、NDVI、土地利用类型	夜间灯光与人口强相关性;夜间灯光蕴含人口分布信息,一定程度上反映人口分布情况	参数少,易于建模,便于推广,适合中大尺度研究	灯光像元过饱和及溢出问题使其应用受限,像元灰度值相同地区难以揭示人口分布异同
多因素融合模型^{[15,22,64,71]}	人口数量、水域因子、交通路网、第一性生产力、DEM、城市规模及点位	基于重力模型构建格点生成法;人口分布与城市规模、交通网密度、净第一性生产力成正比,与到城市中心点的距离、高程成反比	综合考虑自然、经济因素对人口分布的影响,体现了各因素的影响程度	参数设置及模型计算较为复杂
多因素融合模型^{[15,22,64,71]}	土地利用、DEM、交通路网、河流水系、居民点等	人口分布受自然地理要素、社会经济条件、历史条件等众多因素的综合影响	注重分析单因子及多因子对人口分布的影响程度,为其他研究提供清晰思路	融合权重确定较为主观,缺少因子间的相关性分析,选取指标多变,增加复杂性及信息冗余
智能化模型^[43,52,72]	交通条件、自然环境、公共服务设施、建筑物（面积、楼层、新旧程度、区位、密度）	微观个体根据自身偏好以及环境因素,来决定居住选择行为,呈现出人口空间分布;构建“自下而上”多智能体模拟系统	适合城市地区精细尺度人口分布模拟,“自下而上”的模型能够描述城市内部人口分异特性	表征多智能体行为的环境参数的选取较为主观、设置复杂
智能化模型^[43,52,72]	DEM、河流水系、交通路网、土地利用、邻近村镇	利用遗传进化计算技术的优点,构建智能算法,实现人口数据内插方法	智能化与自动化程度高,无须人工干预,模型结构灵活多样	模型精度受算法优劣、样本大小、样本类型的影响,选取合适的影响因素较为困难

表2

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研究尺度	研究时间	研究地区（数据集）及格网大小
全球	1991-2004	GPW、UNEP/5 km^[12,16];LandScan/1 km^[15]
洲或国家级	2002、2003、2005、2007;2004;2012、2013、2015;2014	中国、欧盟/1 km^[6,17,19-22];美国/250 m^[22];非洲、东南亚、塞尔维亚/100 m^[24-26]; 美国/90 m^[27]
省级	2005、2006、2007、2015;2012、2014	山东、福建、北京、河北、长江中游4省/1 km^[28-33];云贵川结合部、山东/100 m^[34-36]
市级	2009、2011;2011;2006、2013、2015	山东半岛,徐州市/500 m^[37];贵州猫跳河流域/300 m^[38-39];临沂市,江西梅江流域,延安市/100 m^[40-42]
县级	2007;2014;2009;2003;2013;2014;2010	和顺县/1 km^[43];鹤峰县/500 m^[44];张家界永定区/250 m^[45];宾夕法尼亚5县/100 m^[18];攀枝花市东区/50 m^[46];Alachua County/30 m^[47];义乌市/20 m、50 m等^[48]
居住建筑物	2009、2011、2012、2014	单个建筑物^[49-52]