基于多源数据的城市功能区精细化研究——以北京为例

doi:10.11821/dlyj020200074

Abstract

Abstract:

It has been a trend for the study of urban functional areas to employ multi-source at a finer tempo and/or spatial scale to contribute a niche understanding of the structure and content of the area. Drawing on advancement of technological development of new type and new sources of geo-coded data, the paper proposes a set of indicators to understand urban functional areas, including the intensity of the use of the area, the difference in the use between day and night, and the degree of multifunctionality. The empirical research is taken in Beijing and these indicators are derived by integrating the mobile phone signaling data of 14400 grid areas in the urban area in 2017 and 380975 points of interest (POI) data from Gaode map in 2016. The main research conclusions are as follows. (1) Every standard area in this paper has a square of 250 m×250 m. The number of Beijing's daytime active areas is three times that of nighttime active areas. The nighttime use intensity of restaurant, living, and other service facilities is higher, and the daytime use intensity of financial, tourism, and public service facilities is higher. (2) Tourism (28.2%), residence (12.1%), transportation (11.4%) are the three types of functional areas with the largest proportion of Beijing area, and the smallest area proportion is financial function district (2.8%), showing that the spatial characteristics of tourism, finance, and public functional areas gather, and other functional areas' distribution presents scattered characteristics. (3) Residential, restaurants, living, and other functional services are strongly dependent on each other, while tourism and enterprise functional services show strong exclusivity to other functional types. Except for the tourism functional areas, there appears an obviously different functional mixing pattern in the central urban area of Beijing. In this paper, the area with the highest function mixing degree (greater than 0.98) is defined as the highly mixed functional area, which accounted for 24.6% of the study area. The result of the functional area divisions has a strong practical significance for Beijing urban planning, and also provides an effective method and a richer perspective for the future in-depth study of urban functional areas.

Key words: urban functional area, multi-source data, population density, mixed functional area, Beijing

YANG Zhenshan, SU Jinhua, YANG Hang, ZHAO Yonghong. ng urban functional areas based on multi-source data: A case study of Beijing[J].GEOGRAPHICAL RESEARCH, 2021, 40(2): 477-494.

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数据种类	定义	应用	相关研究
POI数据	又称兴趣点数据,是为满足电子地图等实际应用需求,由运营商逐步采集的囊括绝大多数出行目的地的具有类别信息的坐标点。	随着POI数据的逐步完善,绝大多数公共热点区域被收录,以坐标点的形式反映了产业与公共机构的空间分布情况,但无法描述产业的空间规模大小。	段亚明等使用POI聚类数据研究城市结构的多中心理论^[21]。
手机信令数据	手机网络服务运营商采集的用户地理位置信息。	由于手机需要随身携带的特点,手机信令数据客观、全面地反映了城市人口实时的空间分布情况。	钮心毅等以上海中心城为例,提出了利用手机定位数据识别城市空间结构的方法^[15]。
公交大数据	公共交通信息采集设备采集的结构化数据。	反映了城市部分人口的空间转移特征,具有代表性。	康莹莹等对公交大数据的界定、来源、分类与性质及未来用途做了详细整理和总结^[28]。
LBS数据	LBS数据是指用户使用基于位置信息的服务（location based service）时,向服务商提供位置信息所积累的数据。	实时反映部分手机应用用户的空间分布情况以及网络活动状态,但数据主体与研究总体往往存在一定差距。	毋一舟等使用LBS服务数据探索潜在兴趣点并设计了POI自动更新算法^[5]。
夜光遥感数据	卫星记录的地表可见光波段的电磁波强度照片。	夜光遥感能反映城市不同区域经济活动情况。	王海军等利用夜光遥感数据对成渝地区城市结构和城市边界变化进行深入探究^[29]。

功能区分类	编号	POI原始分类	城市用地小类	日夜活跃情况	城市用地大类
交通设施及服务	0	通行设施	道路用地	日、夜	道路与交通设施用地
		汽车服务	服务业用地
		停车场	停车厂库用地
		公共交通站点	交通广场用地
		交通执法设施	其他交通设施用地
		交通设施	交通设施用地
居住及相关服务	1	住宿服务	旅馆业用地	夜	居住用地
		住宅	居住用地
		地名地址信息	街道区划信息
文体与科教服务	2	体育休闲服务	体育用地	日、夜	公共管理与公共服务设施用地
		教育设施	教育科研用地
		文化传媒	文化用地
政府及公共设施	3	公共设施	公共设施用地	日	公共管理与公共服务设施用地
政府及公共设施	3	政府办公	行政办公用地	日	公共管理与公共服务设施用地
企业	4	公司企业	商务办公\工业用地	日、夜	工业用地
医疗	5	医疗保健	医疗卫生用地	日	公共管理与公共服务设施用地
生活服务	6	购物服务物流服务生活服务	商业用地	日、夜	商业服务业设施用地
			服务业用地
			服务业用地
餐饮服务	7	餐饮服务酒店服务	服务业用地	日、夜	商业服务业设施用地
餐饮服务	7	餐饮服务酒店服务	服务业用地	日、夜	商业服务业设施用地
旅游	8	风景名胜	绿地、公园	日	绿地与广场用地
金融服务	9	金融保险	金融保险业用地	日	商业服务业设施用地

分类	编号	POI数量(个)	POI个数占比(%)
交通设施及服务	0	44444	11.7
居住及相关服务	1	20002	5.3
文体休闲娱乐	2	47074	12.4
政府及公共设施	3	33965	8.9
企业	4	10865	2.9
医疗	5	12655	3.3
生活	6	92372	24.2
餐饮	7	100696	26.4
旅游	8	2401	0.6
金融	9	16501	4.3

编号	0	1	2	3	4	5	6	7	8	9
功能类型	交通	居住	文体	公共	企业	医疗	生活	餐饮	旅游	金融
日间活跃区域	11.8	5.2	13.0	9.3	2.9	3.2	23.0	26.0	0.8	4.8
夜间活跃区域	11.1	5.2	10.9	8.2	2.3	3.6	28.2	27.3	0.2	3.0

案例	指标	交通	居住	文体	公共	企业	医疗	生活	餐饮	旅游	金融
SOHO 现代城	POI数量	17	6	127	24	52	6	214^*	142	0	48
SOHO 现代城	主导功能	1.0	0.4	6.8^*	2.1	4.7	0.9	6.1	2.9	-0.1	3.3
朝外MEN 大厦	POI数量	25	39	57	8	28	9	180^*	127	0	74
朝外MEN 大厦	主导功能	5.0	13.5	8.2	1.9	6.7	4.8	14.1	7.4	-0.1	14.7^*
三里屯南区	POI数量	14	9	53	8	12	1	175	179^*	0	19
三里屯南区	主导功能	0.7	1.1	2.8	0.4	1.0	-0.1	5.2^*	4.0	-0.1	1.2
金隅大成	POI数量	24	4	31	7	45	1	97	136^*	0	23
金隅大成	主导功能	1.5	0.3	1.4	0.2	4.0^*	-0.1	2.6	2.8	-0.1	1.3

ng urban functional areas based on multi-source data: A case study of Beijing

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References 41

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功能类别	交通	居住	文体	公共	企业	医疗	生活	餐饮	旅游	金融	总计
计数	829	877	523	825	471	663	418	415	2048	205	7274
均值	0.94	0.92	0.94	0.89	0.66	0.87	0.94	0.94	0.35	0.85	0.74
标准差	0.14	0.19	0.12	0.22	0.43	0.24	0.17	0.16	0.45	0.27	0.40
最小值	0.01	0.00	0.01	0.00	0.00	0.00	0.01	0.01	0.01	0.01	0.00
最大值	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00

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