1. Institute of Mountain Hazards and Environment/Research Center for Mountain Development, CAS, Chengdu 610041, China 2. University of Chinese Academy of Sciences, Beijing 100049, China 3. Chengdu University of Information Technology College of Management, Chengdu 610225, China
A large floating population has entered urban areas under the rapid urbanization in China. However, their residential space pattern is strongly affected by residential self-selection, which has reconstructed the urban population distribution pattern and social space. This study examines urban floating population residential space pattern and its formation process, by using the floating population data of 2010 and 2015 in the yearbooks of Chengdu downtown block, and choosing influence variables from the perspective of behavioral decision made by the urban floating population. Therefore, the aims of this study are to analyze the influence of urban characteristic variables for residential self-selection and the influences' spatial differentiation, and to reveal process of space trade-offs in residential self-selection and its role in the formation of urban floating population residential space pattern. The results show that from 2010 to 2015, the urban floating population of Chengdu increased rapidly in the southern part of the downtown area and in the urban centers, and a significant space agglomeration situation featured by low-west and high-east is obviously reflected. Moreover, residential space pattern of urban floating population shows that the level of residential segregation is partially related to the residential space pattern of the registered population, but a notable degree of residential segregation has reduced from 2010 to 2015. More importantly, the study proves that the proportion of urban floating population in permanent residential population, residential land area, housing rent, public transportation, enterprise, hospital, drugstore, restaurant and marketplace have influences on floating population residential self-selection. In addition, the proportion of urban floating population in permanent residential population, residential land area and housing rent are the three significant variables in the spatial error model, but it is dramatically impacted by spatial autocorrelation of floating population statistic block. A major contribution of this study is that the spatial differentiation of the variables' influence on residential self-selection is verified by using geographic weighting regression (GWR), and it reveals the process of urban floating population space trade-offs on how to realize residential self-selection by weighting spatial variability of variables’ influence among different urban regions. That is to say, the spatial difference of living cost, employment opportunities, living environment and commuting costs have shaped the floating population residential space pattern, which is a complex reflection of the urban spatial perception, spatial trade-offs and spatial self-selection of floating population. It can help us to deeply understand the formation process of urban floating population residential space pattern, and provide references to promote community integration and urban management.
张少尧,
时振钦,
宋雪茜等
. 城市流动人口居住自选择中的空间权衡分析——以成都市为例[J]. 地理研究,
2018, 37(12): 2554-2566.
ZHANG Shaoyao,
SHI Zhenqin,
SONG Xueqian et al
. Space trade-offs analysis in the urban floating population residential self-selection: A case study of Chengdu[J]. GEOGRAPHICAL RESEARCH,
2018, 37(12): 2554-2566.
LitmanT.Land use impacts on transport how land use factors affect travel behavior. Land Use Planning, 2005.
[2]
CaoX, YangW.Examining the effects of the built environment and residential self-selection on commuting trips and the related CO2 emissions: An empirical study in Guangzhou, China. Transportation Research Part D: Transport and Environment, 2017, 52: 480-494.https://linkinghub.elsevier.com/retrieve/pii/S1361920915302455
Numerous studies have established the link between the built environment and travel behavior. However, fewer studies have focused on environmental costs of travel (such as CO 2 emissions) with respect to residential self-selection. Combined with the application of TIQS (Travel Intelligent Query System), this study develops a structural equations model (SEM) to examine the effects of the built environment and residential self-selection on commuting trips and their related CO 2 emissions using data from 2015 in Guangzhou, China. The results demonstrate that the effect of residential self-selection also exists in Chinese cities, influencing residents choice of living environments and ultimately affecting their commute trip CO 2 emissions. After controlling for the effect of residential self-selection, built environment variables still have significant effects on CO 2 emissions from commuting although some are indirect effects that work through mediating variables (car ownership and commuting trip distance). Specifically, CO 2 emissions are negatively affected by land-use mix, residential density, metro station density and road network density. Conversely, bus stop density, distance to city centers and parking availability near the workplace have positive effects on CO 2 emissions. To promote low carbon travel, intervention on the built environment would be effective and necessary.
ScheinerJ.Transport costs seen through the lens of residential self-selection and mobility biographies. Transport Policy, 2016: 1-12.http://www.sciencedirect.com/science/article/pii/S0967070X16305522
61The paper develops a life-course perspective to understand transport costs.61Interrelations between mobility biographies and transport costs on multiple levels.61A strong planning system may help reduce future transport user costs.61A strong planning system may reduce future transport provision and external costs.61Paper suggests a shift away from happiness and towards dignity in transport studies.
EttemaD, NieuwenhuisR.Residential self-selection and travel behaviour: What are the effects of attitudes, reasons for location choice and the built environment?. Journal of Transport Geography, 2017, 59: 146-155.https://linkinghub.elsevier.com/retrieve/pii/S0966692316301892
In studies of the effect of built environment on travel behaviour, residential self-selection is an increasingly important issue. Self-selection implies that households locate in places that provide them with conducive conditions for their preferred way of travelling. In these studies, it is assumed that attitudes toward different travel modes are an important factor in location choice, and that households are unconstrained in choosing their preferred residential location. This paper challenges these assumptions, by distinguishing between the more passive travel attitude and travel considerations as a deliberate reason to locate in a certain place. Based on a survey among 355 recently relocated households in Dutch TOD locations, we find that the association between travel attitude and residential environment is weak, and that the association between travel attitude and travel as a factor in location choice is moderate at best. Multivariate models show that both travel attitude and travel being a reason for location choice influence travel mode use, suggesting that travel attitude is insufficient to fully reflect self-selection processes. In comparison to other travel modes, train travel is most influenced by the fact whether residents deliberately chose to live in an environment conducive to using this mode.
BooneheinonenJ, GordonlarsenP, Guilkey DK, et al.Environment and physical activity dynamics: The role of residential self-selection. Psychology of Sport & Exercise, 2011, 12(1): 54-60.http://www.sciencedirect.com/science/article/pii/S1469029209001101
Within the socio-ecologic framework, diet and physical activity are influenced by individual, inter-personal, organizational, community, and public policy factors. A basic principle underlying this framework is that environments can influence an individual's behavior. However, in the vast majority of cross-sectional and even the few longitudinal studies of this relationship, the question of whether individuals select their area of residence based on physical activity-related amenities is ignored. In this paper, we address a critical methodological issue: self-selection of residential location, which is generally not accounted for, and can significantly compromise research on the relationship between environmental factors and physical activity behaviors. We define and discuss the problem of residential self-selection in the study of neighborhood influences on health and health behavior, review methods used to control for residential self-selection in the literature, and present our strategy for addressing this potentially important source of bias. Existing research has built our understanding of residential self-selection bias, but important gaps remain. Our strategy uses data from a longitudinal cohort study linked to contemporaneous environmental measures to create a multi-equation model system to simultaneously estimate residential choice, environmental influences on physical activity, and downstream health outcomes such as obesity and clinical cardiovascular disease risk factor measures.
Based on long-term residential land-use data,this paper makes a calculation on the dissimilarity of diverse residential lands,which might be used as reference in the perspective of physical changes of residential spaces.(1)This study classifies residential land use of downtown Shanghai into 6 types:garden house and villa(coded as R1),high-rise apartment before 1949and workers'community after 1949(R2),commercial residential building(R2N),li-nong residential building(R3),shanty town(R4)and rural house(E6).Then,calculations are made on the spatial differentiation,i.e.the index of dissimilarity(D),spatial-modified dissimilarity index(D(s)),multi-group dissimilarity index(D(m))and spatial-modified multi-group dissimilarity index(SD(m))of various land-use types on the spatial scale of blocks and towns.(2)The result shows that the changing of residential spatial differentiation in different time series is not affected by scale effects or whether the dissimilarity index is spatial-modified or not.(3)From 1947 to 2007,in the type of garden house and villa,the dissimilarity maintains high,while the dissimilarity of commercial residential building keeps decreasing.In other types,however,the dissimilarity has a wave change.(4)D(m)of residential land-use shows that residential segregation might be notable in 1947,and decreases obviously from 1947 to 1979,while D(m)of residential space decreases obviously,and increases significantly from 1979 to 2007.(5)The relation between the hierarchy and the dissimilarity of residential land differs in various periods.Before 1949,the dissimilarity is high within high-rank residential land,whereas the index is quite low in medium and low rank residential land.During the socialist period,the rank and the dissimilarity have a positive correlation.In the transitional period,a"Vshaped"pattern can be found,which means that the dissimilarity of high rank and low rank residential land is high,and low dissimilarity can be seen in medium rank residential land.This indicates that the residential space of Shanghai has been polarized in terms of physical environment.
[LiaoBanggu, XuJiangang, MeiAnxin.Evolution of residential differentiation in central Shanghai city (1947-2007): A view of residential land-use types. Geographical Research, 2012, 31(6): 1089-1102.]
[7]
Vos JD, WitloxF.Do people live in urban neighbourhoods because they do not like to travel? Analysing an alternative residential self-selection hypothesis. Travel Behaviour & Society, 2016, 4: 29-39.http://www.sciencedirect.com/science/article/pii/S2214367X15000435
Previous research has indicated that mode-specific attitudes can affect travel mode choice through the residential location choice. According to the principle of residential self-selection, people will try to choose a residential neighbourhood that enables them to travel with as high a share as possible of their amount of travel with their preferred mode. In this study, however, we will analyse whether differences in travel distance, travel time and travel satisfaction in urban versus suburban neighbourhoods are due to travel-liking attitudes, the residential location or a combination of both. Results of this study 61 analysing leisure trips within the city of Ghent (Belgium) 61 indicate that suburban respondents are, compared to urban respondents, more satisfied with their trips, which are also longer in time and distance. Suburban respondents also have a more positive stance towards travelling, suggesting a possible residential self-selection process. Travel lovers might prefer a residential neighbourhood where travel distances and travel time are relatively high, while people who do not like to travel might prefer to live in a neighbourhood that enables more short-distance and less travel-time intensive trips. This study suggests that especially people who do not like to travel self-select themselves in urban neighbourhoods in order to limit travel distance and travel time. In contrast, respondents with a more positive stance towards travelling are equally distributed in urban and suburban neighbourhoods. Results also indicate that travel distance and travel time are mainly affected by respondents’ residential neighbourhood, while travel satisfaction is mainly affected by travel-liking attitudes.
SchwanenT, Mokhtarian PL.What affects commute mode choice: Neighborhood physical structure or preferences toward neighborhoods?. Journal of Transport Geography, 2005, 13(1): 83-99.http://linkinghub.elsevier.com/retrieve/pii/S0966692304000894
The academic literature on the impact of urban form on travel behavior has increasingly recognized that residential location choice and travel choices may be interconnected. We contribute to the understanding of this interrelation by studying to what extent commute mode choice differs by residential neighborhood and by neighborhood type dissonance—the mismatch between a commuter’s current neighborhood type and her preferences regarding physical attributes of the residential neighborhood. Using data from the San Francisco Bay Area, we find that neighborhood type dissonance is statistically significantly associated with commute mode choice: dissonant urban residents are more likely to commute by private vehicle than consonant urbanites but not quite as likely as true suburbanites. However, differences between neighborhoods tend to be larger than between consonant and dissonant residents within a neighborhood. Physical neighborhood structure thus appears to have an autonomous impact on commute mode choice. The analysis also shows that the impact of neighborhood type dissonance interacts with that of commuters’ beliefs about automobile use, suggesting that these are to be reckoned with when studying the joint choices of residential location and commute mode.
Pinjari AR, Bhat CR, Hensher DA.Residential self-selection effects in an activity time use behavior model. Transportation Research Part B Methodological, 2009, 43(7): 729-748.https://linkinghub.elsevier.com/retrieve/pii/S0191261509000198
This study presents a joint model system of residential location and activity time-use choices that considers a comprehensive set of activity-travel environment (ATE) variables, as well as socio-demographic variables, as determinants of individual weekday activity time-use choices. The model system takes the form of a joint mixed Multinomial Logit ultiple Discrete-Continuous Extreme Value (MNL DCEV) structure that (a) accommodates differential sensitivity to the ATE attributes due to both observed and unobserved individual-related attributes, and (b) controls for the self-selection of individuals into neighborhoods due to both observed and unobserved individual-related factors. The joint model system is estimated on a sample of 2793 households and individuals residing in Alameda County in the San Francisco Bay Area.The model results indicate the significant presence of residential self-selection effects due to both observed and unobserved individual-related factors. For instance, individuals from households with more bicycles are associated with a higher preference for out-of-home physically active pure recreational travel pursuits (such as bicycling around in the neighborhood). These same individuals locate into neighborhoods with good bicycling facilities. This leads to a non-causal association between individuals time investment in out-of-home physically active pure recreational travel and bicycling facilities in their residential neighborhoods. Thus, ignoring the effect of bicycle ownership in the time-use model, would lead to an inflated estimate of the effect of bicycling facility density on the time invested in physically active pure recreational travel. Similarly, there are significant unobserved individual factors that lead to a high preference for physically active recreational activities and also make individuals locate in areas with good bicycling facilities. When such unobserved factors were controlled by the proposed joint residential location and time-use model, the impact of bicycling facility density on out-of-home physically active recreational activities ceased to be statistically significant (from being statistically significant in the independent time-use model). These results highlight the need to control for residential self-selection effects when estimating the effects of the activity-travel environment on activity time-use choices.
人类社会的永续发展极大地依赖于生态系统服务的持续供给。生态系统服务是人类从自然、半自然或人工生态系统中获得的各种惠宜,包括丰富多样的产品和服务类型,如干净的空气和安全的食物等。在自然因素和人为因素的共同影响下,生态系统服务间存在复杂的相互作用关系,通常概括为此消彼长的权衡(trade-offs)和相互促进的协同(synergies)。然而,人类目前对这种相互作用关系的认识仍然非常有限,给生态系统服务管理带来了极大的挑战。生态系统服务空间权衡与协同关系已成为当前地理学和生态学研究中亟待解决的关键科学问题。山地是地球陆地表面一种重要的地貌类型,山地生态系统为人类社会提供了许多重要的生态系统服务,并且山地生态系统服务间相互作用关系的复杂性极高。在典型山地开展生态系统服务的空间权衡与协同关系研究,可为国际学术界提供新的典型案例,丰富现有理论与方法。同时,对我国山地可持续发展和生态文明建设也具有重要指导意义。本研究以“三江并流”区,这一世界性生物多样性热点地区为例,选取对研究区当地及其周围地区十分重要的4类共8种典型生态系统服务,包括供给服务(粮食供给服务、畜牧养殖服务和水源供给服务)、调节服务(碳存储服务、固碳服务、土壤保持服务)、支持服务(生境支持服务)和文化服务(自然游憩服务)。在地理信息系统技术支持下,结合社会经济统计资料与相关生物物理模型,在乡镇尺度上模拟和估算以上8种服务物质量的空间分布。使用斯皮尔曼相关系数并结合冷、热点分析法,探究不同生态系统服务间的相互作用关系。使用主成分分析法,揭示影响不同生态系统服务空间分异及其相互作用关系的主要因素。运用K-均值聚类法识别出不同的生态系统服务簇,并提出针对各类生态系统服务簇的管理建议。主要结论如下：(1)8种生态系统服务在水平和垂直方向上具有各自的分布特征,地域差异明显,生态系统服务间的空间分布规律相似性和差异性并存。除粮食供给服务和畜牧养殖服务外,其他6种服务对维护区域生态安全有重要意义,突出了保护本地区生态系统服务的必要性。(2)在28组可能的生态系统服务组合中有15组相关性表现为中等以上相关(|Spearnan's r |≥0.30,p0.01)。供给类服务与其他3类服务之间的相互作用关系以负相关居多,调节服务与其他3类服务间的相互作用关系以正相关为主,生境支持服务与自然游憩服务之间为显著正相关关系(Spearman's r=0.564,p0.01),同类型的生态系统服务间权衡与协同关系并存。生态系统服务的相互作用关系为生态系统服务管理提供了重要支持,如加强某种特定的调节服务,可能会同时提高其他3类服务。生态系统服务间相互作用关系具有明显的尺度效应。(3)选取在研究区提供某种生态系统服务能力在前或后30个乡镇为该服务的热或冷点乡镇(约占总乡镇数的20%)。研究发现,当两种服务间的相互作用关系表现为权衡关系时,一种服务的热点可能正好是另一种服务的冷点。当两种服务的关系表现为协同关系时,两种服务的热点空间分布十分相似。79.7%的乡镇可以提供至少1种以上较高或较低的生态系统服务。仅有7.2%的乡镇可以同时产生至少4种以上的高值生态系统服务,这些乡镇是急需优先保护的地区。5.9%的乡镇存在4种以上的低值服务,这些乡镇需要在经过实地调研后决定是否需要采取生态恢复措施。没有乡镇能同时提供8种高值或低值生态系统服务。(4)通过对比不同服务的负荷量及不同乡镇前3个主成分因子得分的空间分布特征并结合专家知识,认为“海拔-人类活动”交互作用在空间上的梯度变化是解释本地区生态系统服务空间分异形成的主要因素。由本地区特殊的地表格局造成的气候区域差异是解释生态系统服务空间分异形成的其他重要因素。(5)结合影响生态系统服务空间分异的主要因素,在研究区共识别出4类生态系统服务簇。研究比较了每类服务簇内部及相互间生态系统服务供给能力差异。研究发现一些特殊的生态系统服务簇会因相似的社会经济和自然环境背景在不同地区反复出现,如第2类服务簇；而随着研究的深入,不断有新的生态系统服务簇被发现,如第1类服务簇。结合不同生态系统服务簇当前的社会经济发展和自然环境状况,文章最后提出了相应的生态系统管理建议,以期为本地区可持续发展和生态文明建设提供科学支持。
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This paper presents a modeling methodology capable of accounting for spatial correlation across choice alternatives in discrete choice modeling applications. Many location choice (e.g., residential location, workplace location, destination location) modeling contexts involve choice sets where alternatives are spatially correlated with one another due to unobserved factors. In the presence of such spatial correlation, traditional discrete choice modeling methods that are often based on the assumption of independence among choice alternatives are not appropriate. In this paper, a Generalized Spatially Correlated Logit (GSCL) model that allows one to represent the degree of spatial correlation as a function of a multi-dimensional vector of attributes characterizing each pair of location choice alternatives is formulated and presented. The formulation of the GSCL model allows one to accommodate alternative correlation mechanisms rather than pre-imposing restrictive correlation assumptions on the location choice alternatives. The model is applied to the analysis of residential location choice behavior using a sample of households drawn from the 2000 San Francisco Bay Area Travel Survey (BATS) data set. Model estimation results obtained from the GSCL are compared against those obtained using the standard multinomial logit (MNL) model and the spatially correlated logit (SCL) model where only correlations across neighboring (or adjacent) alternatives are accommodated. Model findings suggest that there is significant spatial correlation across alternatives that do not share a common boundary, and that the GSCL offers the ability to more accurately capture spatial location choice behavior.
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Based on a large-scale household survey conducted in Nanjing in 2005, this study examines housing differentiation between and within groups defined by different socioeconomic characteristics and analyzes institutional and market determinants of housing differentiation under market transition. It is worth noting that, although the degree of housing differentiation between different socioeconomic groups is high, the differentiation within each group is even more significant. This suggests an intensified housing differentiation in the Chinese city. Institutions inherited from the socialist period and the emerging market mechanisms are intertwined to contribute to housing differentiation after the introduction of housing reform. In the postreform era, whereas some institutional factors were weakened, other institutional factors such as the hukou system and the work unit system continue to be significant. Furthermore, similar to other postsocialist countries, the pattern of housing inequality in prereform China remains and even consolidates after economic reforms; that is, vested groups continue to enjoy better housing conditions under a market economy, and the disadvantaged groups are entrapped in a housing predicament. Nevertheless, market factors have also become decisive, which is mainly reflected in the significant housing differentiation between groups categorized by educational attainment and household income.
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2018,
Vol. 37
),时振钦1,2,宋雪茜3,邓伟1,2(
