地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (2): 235-248.doi: 10.12082/dqxxkx.2022.210239
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:
YUAN Yuan1(), MAO Lei2, LI Hongqing1,*(
), ZHAO Xiaofeng1
Received:
2021-04-30
Revised:
2021-06-21
Online:
2022-02-25
Published:
2022-04-25
Supported by:
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。
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
表1
研究采用的数据源
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 |
表2
案例地居民区用地效率评价结果
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 |
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