基于居住空间属性的人口数据空间化方法研究

doi:10.18306/dlkxjz.2016.11.002

Abstract

Abstract:

Fine spatial scale population distribution has increasingly become a research hotspot yet a difficult question in the field of population geography. It has practical values in application and scientific significance for relevant research, such as disaster risk and impact assessment, resource allocation, and construction of smart cities. Residential building scale is considered an important part of fine spatial scales for population distribution. Research on the spatialization of population data at this scale has increasingly attracted academic attention. In this study, a population distribution vector data set at the residential building scale was established for six residential committees in Xuanzhou District, Anhui Province in 2015 based on residential space attribute data. Data used in the study include residential building patch area, percentage of housing area within residential building patches, building floor number, and public area rate. The method takes residential space attributes as variables for spatializing population data and treats residential building patches as population distribution location in geographical space with town boundary and town-level demographic data as controls. The spatialization method used in this study reveals detailed information about population distribution in urban areas. The results show that: (1) The population distribution data, obtained by using residential space attributes, are proved to be of high accuracy and reliability. The mean absolute relative error for 29 communities (villages) is less than 7%. The absolute relative error of 25 out of 29 communities (villages) is less than 10%. The proportion of patches whose estimated number of people is in reasonable range is higher than 74% in a total of 1102 residential building patches. The proportion of patches whose relative error is in slightly underestimated area (-10%, 0) and overestimated area (0, 10%) is higher than 9%. (2) Building volume , defined by residential building patch area and building floor number, is a key factor to estimate accurately the number of people within a residential building. The percentage of housing area can further improve model accuracy. Public area rate plays an important role to increase estimated number of people in underestimated area and decrease that in overestimated area, but is too weak to adjust the estimated number of people to reasonable range. In conclusion, spatialization based on residential space attributes can be an important method for population spatialization research at the residential building scale.

Key words: population, spatialization, residential space, residential building, patch, Xuanzhou District of Xuancheng City

Nan DONG, Xiaohuan YANG, Hongyan CAI. A method for demographic data spatialization based on residential space attributes[J].PROGRESS IN GEOGRAPHY, 2016, 35(11): 1317-1328.

Figures/Tables 15

Fig.1

Fig.2

Tab.1

Tab.2

Tab.3

Models for demographic data spatialization based on residential space attributes

模型名称	回归模型
类型1	$P^= a × Area + b$
类型2	$P^= a × Area × PHA + b$
类型3	$P^= a × Area × BFN + b$
类型4	$P^= a × Area × PHA × BFN + b$
类型5	$P^= a × Area × PHA × BFN × (1 - k 1) + b$
类型6	$P^= a × Area × PHA × BFN × (1 - k 2) + b$

Tab.3

Tab.4

Tab.5

Fig.3

Fig.4

Fig.5

Tab.6

Tab.8

Fig.6

Tab.7

Tab.8

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类型	解译标志描述
类型	颜色	形状	相关布局	地域分布
1~2层居住建筑	屋顶以暗红、灰色为主;阴影不明显	房屋朝向不一致;房屋大小不一,几何形状规则	房屋排列无序,房屋间距不等,周围为绿色植被、耕地等	城乡结合处,城中村
3层及以上居住建筑	屋顶以暗红、暗灰、灰白色为主;阴影显著,并随楼房的变高而变长	方向特征显著,并且一致;几何形状规则且明显	房屋排列有序,与其他居住建筑呈近似等间距关系,周围绿化带及道路明显	城市市区

公摊率(k)	层数
公摊率(k)	1~3	4~6	7~18	19~
方案k¹	0.0	0.10	0.15	0.20
方案k²	0.0	0.08	0.18	0.22

模型名称	相关系数	决定系数	校正决定系数	估计值误差标准差
类型1	0.173	0.030	-0.006	8097.17
类型2	0.438	0.192	0.162	7388.19
类型3	0.956	0.914	0.911	2405.26
类型4	0.985	0.970	0.969	1430.66
类型5	0.982	0.965	0.963	1548.80
类型6	0.983	0.965	0.964	1530.76

模型名称	变差来源	平方和	自由度	均方	F统计量	显著性水平(Sig.)
类型4	回归	1.769E+09	1	1.769E+09	864.431	0.000
	残差	55263490	27	2046795.9	—	—
	总离差	1.825E+09	28	—	—	—
类型5	回归	1.760E+09	1	1.760E+09	733.626	0.000
	残差	64767166	27	2398783.9	—	—
	总离差	1.825E+09	28	—	—	—
类型6	回归	1.761E+09	1	1.761E+09	751.662	0.000
	残差	63266949	27	2343220.3	—	—
	总离差	1.825E+09	28	—	—	—

类别	计算值减真实值	斑块个数比重/%	相对误差平均值/%	描述
人均居住面积	<0	25.9	-5.8	人均居住面积计算值偏小
人均居住面积	>0	74.1	15.5	人均居住面积计算值偏大
建筑面积	<0	22.2	-8.2	矢量化提取的建筑面积偏小
建筑面积	>0	77.8	13.1	矢量化提取的建筑面积偏大

A method for demographic data spatialization based on residential space attributes

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层数/层	合理区内斑块个数比重/%
层数/层	类型4	类型5	类型6
1~3	18.9	16.9	16.9
4~6	49.5	49.3	49.4
7~18	6.3	6.8	7.3
19~33	0.2	1.1	1.2

层数/层	类型5与类型4			类型6与类型4
层数/层	估算人数之差	人数之差平均值	斑块个数比重/%	估算人数之差	人数之差平均值	斑块个数比重/%
1~3	>0	6.8	18.7	>0	6.5	18.1
	=0	0.0	6.4	=0	0.0	7.2
	<0	-1.0	0.1	<0	—	0.0
4~6	>0	1.1	8.2	>0	1.8	40.9
	=0	0.0	32.3	=0	0.0	20.5
	<0	-1.1	21.2	<0	-1.0	0.3
7~18	>0	—	0.0	>0	—	0.0
	=0	—	0.0	=0	—	0.0
	<0	-13.1	11.6	<0	-21.9	11.6
19~33	>0	—	0.0	>0	—	0.0
	=0	—	0.0	=0	—	0.0
	<0	-44.1	1.5	<0	-54.0	1.5

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