地理背景的不确定性对时空行为模式分析的影响——基于西宁市的实证研究

doi:10.11821/dlxb201704008

Abstract

Abstract:

The emerging technologies for collection and analysis of massive spatiotemporal databases have largely benefitted the innovation of urban studies, and enabled us to revisit the traditional constructs in terms of space-time behavior. On specific case is the uncertain geographic context problem (UGCoP) was proposed by Kwan (2012), which refers to the spatial uncertainty in the actual areas that exert contextual influences on the individuals being studied.
This research makes an attempt to address the multivariable analysis as well as the temporal variations in the research of UGCoP. Two inter-related questions are explored in this research: First, how the contextual effects on different aspects of individual behaviors (and their inter-relationships) may be affected by the delineation of geographic context. Second, how the uncertainty of geographic context is evident in terms of the day-to-day variations.
This exploration is based on an empirical study of activity-travel behavior in Xining, China. An activity-diary dataset collected in 2013 and including 1098 valid samples is applied. The research first delineates the contextual unit of the built environment variables in two different ways: one is merely based on the residential neighborhoods, and the other considers both residential neighborhoods and activity spaces. Then, the research testifies the hypothesis about the potential impact of uncertain geographic context on the space-time behavior analysis. Also, the results on the weekday and weekend are compared in order to testify the day-to-day variation. Based on ANOVA test, geo-visualization, and the structural equation modelling, the study yields several findings. First of all, the geographic context based on residential neighborhood may lead to misunderstanding results, such as overestimating the contextual effects of neighborhood areas, and underestimating those of other activity places. Secondly, the effects of geographic context on individual behaviors show significant differences between the weekday and weekend.
These results imply that expanding our analytical focus to human mobility will considerably enrich our understanding on geographic context. The findings reveal trends similar to those observed in Western cities. Overall, addressing themes like these calls for further application of the space-time behavior perspective in revisiting and re-conceptualizing the traditional constructs.

Key words: uncertain geographic context problem, space-time behavior, activity space, structural equation modelling, Xining

Yiming TAN, Yanwei CHAI, Mei-Po KWAN. The impact of the uncertain geographic context on the space-time behavior analysis: A case study of Xining, China[J].Acta Geographica Sinica, 2017, 72(4): 657-670.

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References 53

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变量		样本数(个)	百分比(%)
性别	男	578	52.6
性别	女	520	47.4
年龄(岁)	<30	102	9.3
	30~50	670	61.0
	>50	326	29.7
收入(元)	<2000	395	36.0
	2000~5000	601	54.7
	>5000	102	9.3
教育程度	初中及以下	395	36.0
	高中或大专	427	38.9
	本科及以上	276	25.1
户籍	西宁户籍	928	84.5
户籍	非西宁户籍	170	15.5
驾照	有	405	36.9
驾照	无	693	63.1
汽车所有权	有小汽车	345	31.5
汽车所有权	无小汽车	753	68.5

		工作日			休息日		F值
		平均值(min)		标准差		平均值(min)	标准差
强制性活动	家内	13.61	69.100		10.77	59.727	5.767^***
强制性活动	家外	293.30	259.980		156.55	249.284	3.258^***
维护性活动	家内	763.25	183.876		798.23	184.300	2.663^***
维护性活动	家外	71.60	117.623		103.68		144.324	4.523^***
可支配活动	家内	179.76	158.602		201.70	161.339	5.327^***
可支配活动	家外	76.61	142.051	126.10		170.492	6.472^***
出行		41.87	38.472	42.98	40.593	7.953^***

		平均值	标准差	差异显著性
零售商业	居住地设施密度	89.67	96.64	-
	工作日活动地设施密度	82.00	88.02	0.00
	休息日活动地设施密度	96.17	92.78	0.00
餐饮娱乐	居住地设施密度	68.67	72.20	-
	工作日活动地设施密度	65.16	66.08	0.00
	休息日活动地设施密度	75.57	68.02	0.00
公交站	居住地设施密度	5.82	2.72	-
	工作日活动地设施密度	4.43	3.04	0.00
	休息日活动地设施密度	4.75	2.86	0.00

	CMIN/DF	RMSEA	CFI
模型1	2.17	0.033	1.000
模型2	2.33	0.035	0.999
模型3	2.93	0.042	0.999
模型4	3.09	0.044	0.998
参考范围	2.00~5.00	<0.100	>0.900

	家外强制性活动时长		家外维护性活动时长		家外可支配活动时长		出行频次		出行时长		活动空间
	居住地	居住地+活动地	居住地	居住地+活动地	居住地	居住地+活动地	居住地	居住地+活动地	居住地	居住地+活动地	居住地	居住地+活动地
年龄	-0.33^***	-0.34^***	-0.11^***	-0.12^***	0.14^***	0.13^***					-0.05^*
性别					-0.08^**	-0.08^**	-0.05^*	-0.05^*	-0.07^**	-0.07^**
收入	0.29^***	0.30^***	0.06^*	0.07^**			0.13^***	0.14^***
驾照			-0.06^*
市中心距离					-0.08^**			0.06^*			0.11^**	0.06^*
居住地餐饮娱乐设施密度	0.31^***	0.28^**			0.26^**		-0.20^**	-0.23^**	-0.16^*		-0.21^**
居住地商业零售设施密度	-0.21^**	-0.17^*			-0.38^***	-0.25^**	0.17^*	0.16^*			0.23^**
居住地公交车站设施密度					0.06^*						-0.08^**
活动地餐饮娱乐设施密度				-0.26^**						-0.17^*
活动地商业零售设施密度				0.32^**		-0.18^*				0.29^**
活动地公交车站设施密度		0.09^**				0.13^***						-0.27^***
家外强制性活动时长			-0.34^***	-0.35^***	-0.43^***	-0.44^***	0.25^***	0.23^***			0.17^***	0.20^***
家外维护性活动时长					-0.15^***	-0.15^***	0.29^***	0.28^***			0.08^**	0.10^***
家外可支配活动时长							0.38^***	0.37^***				0.11^***
出行频次									0.39^***	0.38^***		0.06^*
出行时长											0.31^***	0.32^***

The impact of the uncertain geographic context on the space-time behavior analysis: A case study of Xining, China

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