基于位置大数据的城市居住用地效率指标构建及评价研究

doi:10.12082/dqxxkx.2022.210239

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (2): 235-248.doi: 10.12082/dqxxkx.2022.210239

• 地球信息技术在国土空间规划中的应用 • 上一篇下一篇

基于位置大数据的城市居住用地效率指标构建及评价研究

袁源¹(), 毛磊², 李洪庆¹^,^*(), 赵小风¹

1.河海大学公共管理学院,南京 211100
2.江苏省地质调查研究院,南京 210018

收稿日期:2021-04-30 修回日期:2021-06-21 出版日期:2022-02-25 发布日期:2022-04-25
通讯作者: *李洪庆（1986— ）,男,山东烟台人,博士,副教授,主要从事土地利用系统,人地耦合系统研究。E-mail: lihongqing163@126.com
作者简介:袁源（1986— ）,男,江苏常州人,博士,讲师,主要从事土地利用与规划研究。E-mail: hhuyuany@163.com
基金资助:
国家自然科学基金青年项目(42001196);国家自然科学基金项目(41871173);中央高校基本科研业务费项目(B200201073);江苏省国土资源科技计划项目(2018063)

Constructing Index for the Assessment of Urban Residential Land Efficiency Using Location-Based Big Data

YUAN Yuan¹(), MAO Lei², LI Hongqing¹^,^*(), ZHAO Xiaofeng¹

1. School of Public Administration, Hohai University, Nanjing 211100, China
2. Geological Survey of Jiangsu Province, Nanjing 210018, China

Received:2021-04-30 Revised:2021-06-21 Online:2022-02-25 Published:2022-04-25
Supported by:
Youth Program of National Natu-ral Science Foundation of China(42001196);National Natural Science Foundation of China(41871173);The Fundamental Research Funds for the Central Universities of China(B200201073);Jiangsu Provincial Land Re-sources Science and Technology Program(2018063)

摘要/Abstract

摘要：

信息化赋能已经成为新时期国土空间规划的热点,但通过大数据整合进行国土空间利用评价研究仍有待探索。本文旨在借助腾讯位置大数据开展城市居住用地效率评价实证研究,综合运用多源地理空间数据,以居民区为评价单元构建居住用地效率指标,揭示常州市新城区不同居民区用地效率差异。结果表明：① 居民区范围内小时粒度的人口规模呈周期波动,峰值一般出现在21:00,符合城市居民昼出夜归的作息规律,且不同容积率水平的居民楼人口集聚度和规模值也存在预期性的差异;② 29个居民区按建成年份划分为1980s、1990s、2000s、2010—2015年、2015年以后共5组,各组效率指标平均值分别为1.74、2.45、2.31、0.95和0.91人/百m²,2010年之前建成的居民区明显高于2010年之后新建的,2010年以后建成的居民区低于全市2.06人/百m²的平均水平（2018年标准）;③ 效率指标值低并非完全等同于集约用地水平低,常州市新城新区开发建设的成长周期、居民对提升人居环境品质的需求,都是导致不同居民区用地效率差异的原因。研究表明,位置大数据作为高精度的人口数据源,能够客观反映居民区人口聚集的时空间特征,基于位置大数据构建的城市居住用地效率指数能够为高质量国土空间利用分析提供新途径。在我国以人为本的城市化进程中,以位置大数据为代表的新型人口数据源将在国土空间规划中发挥愈加重要的作用。

关键词: 土地集约利用, 效率指标, 居民区, 时空间特征, 精细化评价, 国土空间规划, 位置大数据, 常州

Abstract:

Information empowerment to the territorial spatial planning has become a hot research field in the new era. However, research on territorial utilization evaluation using big data integration remains to be explored. The purpose of this paper is to carry out an empirical study on the efficiency assessment of residential land use in new urban area employing Tencent location-based big data. Assessment index of residential land use efficiency in each residential area have been proposed, supported by integration of multi-source geospatial data, to reveal the differences in land use efficiency among different residential areas in Changzhou city. The results show that, firstly, population size of hourly particle statistics within the residential area fluctuates periodically, reaching peak value at 21:00 generally, which is in line with the routine of daily going out and returning home for urban residents. There are also expected differences in population agglomeration degree and population size among residential buildings with different capacity rates. Secondly, the 29 residential areas are divided into five groups by year of construction, 1980s, 1990s, 2000s, 2010—2015, and post-2015. The average population size of efficiency index of group 1980s, 1990s, 2000s, 2010—2015, and post-2015 are 1.74, 2.45, 2.31, 0.95, and 0.91 per 100 m², respectively. Index values of residential areas built before 2010 are significantly higher than those built after 2010. Furthermore, residential areas built after 2010 are lower than the average level (population size of 2.06 per 100 m²in year 2018) of the entire urban residential areas. Thirdly, it is suggested that lower results of efficiency index is not fully equal to poor level of intensive land use. The main reasons of diverse land use efficiency of residential areas constructed in different periods include the growth periodicity of new urban area development in Changzhou city, and urban residents' desire for better living environment to enhance their quality of habitation. Research shows that location-based big data, as a source of population data with high solution, could reflect the temporal and spatial characteristics of resident aggregations objectively. Index constructed to assess urban residential land efficiency using location-based big data is both innovative and scientific, which could provide a new way for the analysis of high-quality land space utilization. In conclusion, regularity recognition of behavior characteristics from urban residents can provide support for spatial policy formulation during the urbanization process based on "putting people first" policy in China. What's more, new data sources, represented by location-based big data in this paper, will play an important role in decision-making mechanism of territorial spatial planning.

Key words: land intensive use, efficiency index, residential area, spatial-temporal characteristics, refined assessment, territorial spatial planning, location-based big data, Changzhou

袁源, 毛磊, 李洪庆, 赵小风. 基于位置大数据的城市居住用地效率指标构建及评价研究[J]. 地球信息科学学报, 2022, 24(2): 235-248.DOI:10.12082/dqxxkx.2022.210239

YUAN Yuan, MAO Lei, LI Hongqing, ZHAO Xiaofeng. Constructing Index for the Assessment of Urban Residential Land Efficiency Using Location-Based Big Data[J]. Journal of Geo-information Science, 2022, 24(2): 235-248.DOI:10.12082/dqxxkx.2022.210239

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数据名称	年份	格式	来源	空间分辨率	属性与作用
遥感影像	2019	TIF	天地图影像	1 m	居民区位置校核、空间配准
建筑物数据	2018	SHAPEFILE	业务主管部门（数据脱敏后提供）	1:500	包括基底面积、层高等字段,用于计算居民楼建筑面积、居民区建筑密度和容积率等
百度街景地图数据	2019	JPG	百度地图	-	判读沿街建筑物用途并分类
腾讯位置大数据	2018	JSON	腾讯位置大数据平台	5 m	包括区域ID、区域人数（pop）、起止时间（以小时为时间粒度）、所有点数据坐标（经纬度）及其权重值（weight）等字段,用于分析不同时刻居民区的人口规模及其空间分布

居民区编号	建成年份	用地效率/（人/百m²）	建筑密度	建筑容积率	居民楼类型
1	1980s	2.53	0.53	0.96	L
2	1986	1.09	0.33	0.64	L
3	1988	2.31	0.42	1.57	L、D
4	1990s	2.12	0.44	0.86	L
5	1990s	1.82	0.38	0.67	L
6	1990	1.97	0.61	1.44	L
7	1997	3.16	0.23	1.12	D
8	2001	2.43	0.21	1.13	D、X
9	2003	3.86	0.18	1.00	D
10	2008	1.54	0.15	2.37	X、G
11	2009	2.04	0.14	1.98	D、X
12	2009	2.03	0.17	2.57	X、G
13	2010	1.97	0.11	2.07	X、G
14	2012	1.71	0.19	2.00	L、D、X、G
15	2013	0.60	0.32	1.46	L、G
16	2014	1.03	0.09	2.72	X、G
17	2014	1.01	0.11	2.40	G
18	2015	0.80	0.14	4.28	G
19	2015	1.10	0.16	5.03	G
20	2015	0.42	0.18	2.77	L、G、C
21	2016	1.51	0.11	3.03	G
22	2016	1.02	0.14	3.27	X、G
23	2016	0.93	0.09	2.08	G
24	2016	0.91	0.16	2.68	L、D、G
25	2016	0.88	0.12	2.99	X、G
26	2016	0.75	0.07	1.96	G
27	2017	0.79	0.12	2.82	L、G
28	2017	0.65	0.12	2.05	L、G
29	2018	0.76	0.12	3.20	G

基于位置大数据的城市居住用地效率指标构建及评价研究

Constructing Index for the Assessment of Urban Residential Land Efficiency Using Location-Based Big Data

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