Data from the 7th National Population Census suggest that housing conditions in China have continued to improve in the past decade. The per capita housing area of urban residents has reached the standard of living of a comprehensive well-off society. The household residential pattern,used to be dominated by two-generation households, has now featured by a dichotomy between onegeneration households and multi-generation households, and the proportion of buying commercial houses and renting other houses has increased significantly. The housing conditions in China's western and northeastern regions have both greatly improved, and the regional disparity has narrowed. However,the urban-rural gap in housing conditions has been changing that the gap in housing facilities narrowing, while the gap in per capita housing area widening. While differentials in housing sources exist,differences by occupation in urban areas tend to narrow.

住房是流动人口融入城市、实现市民化过程中必须解决的关键问题。基于2010 年第六次人口普查数据，采用住房拥有率、租住房率、住房面积指数、住房不受干扰指数、住房质量指数和住房费用指数6 个指标考察流动人口的住房状况，并综合运用数理统计、空间自相关和系统聚类法揭示流动人口住房状况的属性特征、空间分布与集聚类型。研究发现，与城镇常住人口相比，流动人口的住房状况较差。从空间分布看，流动人口住房状况的各项指标具有显著的空间正相关，在空间分布上不仅存在集聚现象，而且有明显的集聚中心。研究结果还表明，流动人口住房条件综合状况可划分为较好、中等、中等偏下、较差4 级类型区，在全国尺度上的空间分布除个别类型外具有团块聚合的结构特征。在考虑社会公平的前提下，应分类解决不同类型区域流动人口的住房问题。

China's rapid urbanization and economic development have given rise to the fast growth of the floating population, and housing is a key issue in the process of their integration into the destination cities. This paper intends to explore this topic by analyzing the spatial patterns of housing conditions of the floating population. Based on the sixth census data, the paper selects six indicators to measure housing conditions of the floating population: the home-ownership rate, the rental-housing rate, the floor area index, the housing facilities index (constructed by summing up the situation of five variables: availability of running water, washroom, bathroom, kitchen, and the type of fuel), the index of privacy (constructed by summing up the situation of two variables: the function of the dwelling and the number of the dwelling's floors), and the housing consumption index. It uses the methods of Spatial Autocorrelation Analysis and Hierarchical Cluster to examine the spatial distribution and agglomeration patterns of the floating population's housing conditions. The results of the calculation show that compared with urban permanent residents, members of the floating population are much more likely to live in rental homes;their housing conditions are generally worse;and their rental expenses are higher. The spatial variation of the homeownership rate, the rental-housing rate, and the housing facilities index is mainly manifested as north-south differences;the floor area index, and the index of privacy show marked difference between eastern and western China. The low-value centers of the housing consumption index are located in Inner Mongolia, Shaanxi, Hubei and Anhui provinces, while the high-value centers are located in Beijing. Furthermore the results of Spatial Autocorrelation Analysis demonstrate that there is a significant positive spatial correlation in the indicators of the floating population's housing conditions on a national scale, and identify the phenomenon of their spatial clustering and the centers of such spatial clustering. The analysis of Hierarchical Clustering identifies the housing conditions of the floating population into four distinctive groups, and suggests that the housing conditions of the floating population in the inner and east parts of China are better than those in the outer and west parts, and such a spatial variation extends from the north to the south. Finally, on the basis of the above findings, the paper puts forward some policy suggestions for improving the housing conditions of the floating population.

基于2015年全国1%人口抽样调查和2014年流动人口动态监测调查（CMDS）数据,本文以房屋面积、设施条件、建筑年代和社区类型来衡量流动人口住房质量,运用空间统计工具探讨了中国310个地级及以上行政单元流动人口住房质量的空间分异特征,进而通过空间计量模型考察流动人口住房质量的影响因素。研究发现：① 流动人口住房整体水平虽不及本地居民,但差距并不悬殊;② 流动人口住房质量的空间差异明显,中部地区流动人口住房质量最高,东部、西部和东北地区在住房质量4个方面各有劣势;③ 按照行政等级和规模等级划分,中等城市流动人口住房质量最好,超大城市住房质量最差;④ 流动人口住房质量呈现出显著的空间正相关,但各指标高、低值集聚区的分布格局存在一定差异;⑤ 流动人口个体（内部特征）和流入地（外部特征）因素对流动人口住房质量具有显著影响,分别作用于不同的方面;⑥ 中小城市和大城市流动人口住房质量的决定因素及作用强度不尽相同。

Based on data from the 1% National Population Sample Survey 2015 and the 2014 China Migrant Population Dynamic Survey (CMDS), the paper selects four indicators to measure housing quality of the migrant population including floor area, housing facilities, construction period and living communities (urban or rural) and discusses the phenomenon about spatial differentiation of migrant populations' housing quality in 310 prefecture- and provincial-level cities in China, using GIS spatial analysis methods such as Moran's I coefficient and Getis-Ord Gi*. Besides, we investigate the influencing factors of migrant populations' housing quality. Some conclusions can be drawn as follows: (1) Compared with the local residents, the housing quality of the migrants is not that worse. (2) The spatial distribution of housing quality of migrant population presents marked spatial differentiation on cities of different levels and scales, population sizes and geographical divisions. (3) The results of spatial autocorrelation analysis demonstrate that there is a significant positive spatial correlation in the indicators of the migrant population's housing quality on a national scale, and identify the phenomenon of their spatial clustering and the centers of such spatial clustering. (4) The housing quality is influenced by both internal factors of migrant population and external factors of in-flow cities. (5) Population, economic development and the housing market play different roles in the housing quality of migrant population.

Malaysian National Housing Policy has reinforced the aspect of quality as one of the aspects that utterly needs consideration in new housing development. There are problems in defining the housing quality regarding the expected criteria and standards due to subjective and contextual determined in controlling the quality of housing development. It focuses on what constitutes a good housing quality in the perspectives of housing actors in Malaysia? This paper employs in-depth interview with the actors as well as focus group interviews with residents as the main instruments towards understanding the meaning of housing quality in developing countries such as Malaysia.eISSN: 2398-4287 © 2018. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.DOI: https://doi.org/10.21834/e-bpj.v3i8.1414

近年来“家庭式迁移”日益成为流动人口迁移的主要趋势,并对城市居住的独立性、权属和质量提出了现实需求。运用2009年环渤海、长三角、珠三角、成渝四区域12市的2394份抽样调查问卷,采用Logistic回归分析等计量方法,探究家庭式迁移的流动人口住房特征及影响因素。研究发现,“独住型”“夫妻同住型”“两代同住型”和“三代同住型”的流动家庭住房特征存在显著差异,其购房比例和住房质量依次提高。流动家庭的住房权属和质量受到家庭社会经济特征、家庭类型、地理因素以及流动家庭与老家联系和在流入地融入程度的影响。研究发现,如果纳入城市归属感、留城意愿及与老家的联系等变量,将会显著弱化户口对住房的作用。由于不同类型家庭所处的社会经济状况和应对策略不同,住房特征产生了家庭分异。因此,政府应当制定梯度化的住房管理政策,以此推动流动家庭逐步实现“固化”到城市。

Nowadays family migration has become the main migration pattern of floating population in China, which may raise new requirements for residential independence, tenure and quality in the cities. For some researchers, the migrant households were generally considered as passive recipients of housing inequality, and the institutional barrier of Hukou were no doubt still the decisive factors for migrants' housing outcomes. However, other researchers start to challenge this perspective, and argue that migrants should be considered as enabling agents with coping strategies in the housing market since the influence of Hukou system in migrants' housing outcomes is declining. Under this context, this paper tries to explore the role of family strategy in family migrants' housing outcomes, and whether this conclusion differentiates among various households. Based on a questionnaire survey which covered 2394 migrants and their households in 12 cities of the Bohai Rim Region, the Yangtze River Delta Region, the Pearl River Delta Region, and the Chengdu-Chongqing Region, this paper uses logistic regression model to explore family migrants' urban housing outcome in terms of housing tenure and quality and the mechanism for this. The result shows, different household arrangements lead to diversification of housing outcome, which means that the ownership and housing quality differs significantly among sole migrants, couple migrants, two-generation migrants, and three-generation migrants. The three-generation migrant households, as expected, have the highest ownership percentage and residential quality among all types of migrant households, while the sole and couple migrants suffer from the poor residential conditions. This housing outcome should be explained by factors such as socio-economic characteristics, household arrangements, geographic environment, migrants' connection with the original hometown and adaptation to current destination. Especially, when considering the effect of migrants' sense of belonging, plan to settle down in the destination and connection with hometown, the effect of Hukou is weakened. Different household arrangements have different barriers and strategies, which leads to the divergence of housing outcomes. The implication from this research is that the governments should make gradient regulation policy for the diverse family migrants, and lead them to settle stably step by step.

In the Industrialized West, ageing populations and cultural diversity—combined with rising property prices and extensive years spent in education—have been recognized as diverse factors driving increases in extended family living. At the same time, there is growing awareness that household size is inversely related to per capita resource consumption patterns, and that urgent problems of environmental sustainability are negotiated, on a day-to-day basis (and often unconsciously), at the household level. This paper explores the sustainability implications of everyday decisions to fashion, consume, and share resources around the home, through the lens of extended family households. Through interviews with extended family households in Australia, we explore the potential for these living arrangements to reduce resource use, and thus improve sustainability outcomes. In these households, a desire to care for and support family members in hard times (rather than an overt sustainability agenda) has promoted particular modes of extended family living, including unique forms of sharing and pooling material goods. But cultural values of privacy, space, and independence—and the sanctity of the nuclear family—have led to duplication (and even multiplication) of household spaces, appliances, and resources, under one roof. The potential environmental and economic benefits of resource sharing within larger households are thus mediated by deep cultural values and exigencies of everyday life.

研究新城市贫困空间居住满意度,对深入认识城市贫困群体生活状况,提升居住质量,完善居住区理论具有重要意义。论文以西安市主城区典型老旧企业社区为例,基于1010份问卷数据,构建居住满意度结构方程模型,探究居民居住满意度及其影响因素;引入重要性—绩效理论(importance-performance analysis, IPA),甄别企业社区改造重点与次序,指出老旧小区提升建议。结果表明：① 企业社区居住满意度整体“一般”,在李克特五分制量表下均值为3.21;② 居住满意度受居住环境、住房条件及配套设施3个方面影响,与居民搬离意愿呈显著负相关;③ 不同社会经济属性个体的居住满意度在年龄、文化程度、户籍、职业、个人收入、居住时长、住宅产权以及企业运营状况等变量作用下差异显著;④ 企业社区改造亟需优先关注社区休憩活动场所、卫生清洁、适老化医疗护理设施、物业服务、公共服务设施等要素,进而改善居住条件,增强居民的社区归属感与凝聚力。文章提出从创新管理模式、改善居住环境、改造住房条件以及建设配套设施等4个方面实施未来老旧小区改造建议。

Studying on residential satisfaction of the new urban poverty space is of great significance for understanding the living condition of the urban poverty group, improving the life quality, and enriching the research of residential area. Taking the typical old enterprise communities in the central city of Xi'an as an example, this study explored residential satisfaction of enterprise communities and its influence factors based on survey data and structural equation modeling (SEM) method. Importance-performance analysis (IPA) theory was introduced to identify the priorities and sequence of enterprise community renewal, and we propose methods for the renewal of the old urban communities. The results show that: 1) Residential satisfaction of enterprise communities is at an average level, and the mean value of the 5-point Likert scale is 3.21. 2) Residential satisfaction is successively affected by the living environment, housing conditions, and supporting facilities, and has a significant negative impact on residents' willingness to move out. 3) There were significant differences in residential satisfaction among individuals with different socioeconomic attributes, such as age, educational level, household registration, occupation, monthly income, length of residence, housing property rights, and enterprise operation. 4) In order to improve the living conditions, it is necessary to give priority attention to the factors of community recreation and activity, sanitation, medical and nursing facilities suitable for the aged population, property services, public service facilities, and so on, and strengthen the sense of community belonging and cohesion of residents. It is recommended that the renewal of the old urban community in the future should be carried out from four aspects: management mode, living environment, housing, and supporting facilities.

Population urbanization is crucial to establishing a harmonious society. However, the phenomenon of population semi-urbanization is becoming an issue of ever-increasing concern in China. More and more immigrants from rural areas work and live in the city, but their roots remain in the rural area. This paper aims to analyze the influence mechanism of government competition on population semi-urbanization through land supply structure. The study’s theoretical analysis and empirical analysis results are based on the panel data of 105 key prefecture-level cities in China from 2007 to 2017. The results demonstrate that: (1) land finance and land-motivated investment engendered by government competition lead to an imbalance in the land price structure, further increasing the rate of population semi-urbanization; (2) land finance does not lead to population semi-urbanization through the land area structure; and (3) land-motivated investment aggravates the imbalance in the land area structure, further leading to population semi-urbanization. It is found that government competition in terms of achieving performance indicators affects population semi-urbanization by adjusting the land supply structure. Efforts should be made to achieve the coordinated development of urbanization, given that the increasing rate of population semi-urbanization will almost certainly aggravate social instability.

住房支付能力事关广大百姓生活质量与福祉，已成为世界各国政府共同关心的民生话题。2020年度住建部城市体检工作对中国城市住房支付能力给予了高度重视，并将其纳入到城市体检评价指标体系。基于全国337个地级以上城市房价/房租和家庭可支配收入数据，从购房群体和租赁群体视角客观评估了中国城市住房支付能力及其变化，并划分了中国城市住房支付能力空间类型区，最后分析总结了中国城市住房支付能力的影响机制与分类调控对策。研究结果表明：① 中国城市房价收入比略微偏高且有所提升，2015年和2019年的平均房价收入比分别为7.01和7.76；中国城市房租收入比更加合理且有所下降，2015年和2019年的平均房租收入比分别为25.04%和22.01%。② 中国城市住房支付能力空间分异明显，房价收入比呈现出明显的东部高、中西部低的特征，房租收入比却呈现明显的南高北低的特点。③ 根据房价收入比和房租收入比联合空间分布特点，可将中国城市住房支付能力划分为租购支付能力双弱型、租购支付能力偏弱型、购房支付能力偏弱型、租购支付能力双强型和租房支付能力偏弱型等5种空间类型区，不同空间类型区的住房支付能力调控策略有所区别。④ 中国城市住房支付能力的影响机制包括城市住房供给和需求、城市生活质量、社会预期和住房偏好、金融与房地产政策以及家庭收入水平等方面。

Based on 337 prefecture-level above cities’ housing prices/rent and household disposable income data, this article objectively evaluates housing affordability and its changes for Chinese cities from perspectives of both homeowners and tenants, then identifies spatial types of housing affordability, and finally summarizes the influence mechanism and control measures of housing affordability. The results show that price-to-income ratio for Chinese cities is slightly higher and has increased, with an average being 7.01 and 7.76 in 2015 and 2019 respectively, but rent-to-income ratio for Chinese cities is more reasonable and has declined, with an average of 25.04% in 2015 and 22.01% in 2019. Moreover, there is distinct spatial differentiation of housing affordability in urban China. Price-to-income ratio is witnessed with higher value in the eastern region, while lower value in the central and western regions and rent-to-income ratio with higher value in the southern region and lower value in the northern region. Following the joint distribution of price-to-income ratio and rent-to-income ratio, housing affordability in China can be divided into five spatial types: type of weak affordability for both renters and purchasers, type of slight weak affordability for both renters and purchasers, type of weak affordability for only purchasers, type of strong affordability for both renters and purchasers, and type of weak affordability for renters. Distinguished control strategies have been raised for different spatial types of housing affordability. Last, the influencing mechanism of housing affordability in urban China includes housing supply and demand, urban quality of life, social expectation and housing preference, financial and real estate policies, and household income level.

基于2016年中国流动人口动态监测数据，运用多尺度地理加权回归模型对流动人口流入地购房意愿影响因素的空间异质性进行分析。研究发现：① 中国流动人口在流入地城市的购房意愿整体偏低，尤其在流动人口聚集的东南沿海地区，流动人口购房意愿最低。② 多尺度地理加权回归（multi-scale geographically weighted regression，MGWR）模型能识别出不同因素对购房意愿的影响具有空间尺度差异，其中户口类型、流动范围、流动次数等显著变量对不同区域流动人口购房意愿的影响存在明显的区域差异。③ 各影响因素呈现显著的空间分异格局，其中婚姻状况、户口类型、职业类型、房价、公共服务等因素对东南地区购房意愿的影响较大，收入、流动次数、已购住房、随迁子女等因素对东北地区购房意愿的影响更大，西北地区平均受教育年限和拥有住房公积金对流动人口购房意愿的正向促进作用显著，而跨省流动的负向影响由西北向中部地区梯度递减。

Against the backdrop of new urbanization in China, the home purchase intention of floating population in the destination city is closely related to the process of citizenization. However, existing studies have paid insufficient attention to the spatial heterogeneity of floating population’s home purchase intention and its influencing factors. Based on China Migrants Dynamic Survey in 2016, this study investigates the determinants of floating population’s home purchase intention and their spatial heterogeneity using the multi-scale geographically weighted regression (MGWR). The results show that the overall willingness of China’s urban floating population to purchase a home in the city of their inflow is on the low side, especially in the southeastern coastal region where the floating population congregates, where the floating population has the lowest willingness to purchase a home. The results of the MGWR model indicate that different factors exerting significant impacts on floating population’ home purchase intention have spatial scale differences. Among them, significant variables such as hukou status, inter-provincial migration have obvious regional differences in the influence of the floating population’s home purchase intention in different regions. Specifically, marital status, hukou status and occupation at the individual level and housing price, public financial expenditure and medical resources at the city level have greater impacts on the willingness of floating population to purchase a dwelling in southeast China than other regions. The impacts of income, frequency of migration, owning a dwelling and migration with children are more significant in northeast China. The educational attainment and housing provident fund have more significant positive effects on floating population’s home purchase intention in northwest China, while the negative impact of inter-provincial migration decreases from northwest to middle central China. This study provides theoretical basis and policy recommendations for alleviating the housing problem of the floating population and advancing the process of urbanization.

时间地理核密度估计是经典核密度估计(KDE)基于时间地理的一种扩展,主要是将标准核函数的定义域扩展至时间地理的时空可达域,以通过增强定义域在时空方面的物理意义来避免非零密度被分配到可达域之外的问题。时空可达域包括时空碟和由时空碟复合而成的潜在路径区域(PPA)。这2类可达域用作核函数的定义域能解决上述问题,但也带来了新的问题。基于时空碟构建的核函数能叠加成PPA上的概率密度,但敏感于时空碟的时间点。而基于PPA构建的核函数相较于理想布朗桥模型缺乏双峰特性,且也不能生成时空碟上的概率密度函数。因此,时间地理与KDE相结合的研究还处于应用前的理论探索阶段,论文的目标就是对这一进程进行梳理并引出未来的发展趋势。论文围绕时空轨迹不确定性量化这一目标,首先回顾了时间地理与核密度估计的不同功用,然后对两者相融合的意义、框架和模式进行了阐述。最后,认为时间地理的可达域代替核密度估计的定义域是改进时空轨迹不确定性测度的重要手段,但距离目标的落地还有一定的距离。

Time geographic kernel density estimation is an extension of classical kernel density estimation (KDE) based on time geography. It mainly extends the definition domain of the standard kernel function to the space-time reachable domain of time geography, so as to avoid the problem of non-zero density being assigned outside the reachable domain by enhancing the physical meaning of the definition domain in space-time. The space-time reachable area includes the space-time disc and the potential path area (PPA) compounded by the space-time disc. These two types of reachable domains can be used as the domain of the kernel function to solve the above problems, but they also bring new problems. The kernel function constructed based on the space-time disc can be superimposed into the probability density on the PPA, but it is sensitive to the time point of the disc. Compared with the ideal Brown Bridge model, the kernel function constructed based on PPA lacks bimodal characteristics, and cannot generate the probability density function on the space-time disc. Therefore, the research on the combination of time geography and KDE is still in the stage of theoretical exploration before application. The goal of this article is to sort out this process and elicit future development trends. Focusing on the goal of quantifying the uncertainty of space-time trajectory, this article first reviews the different functions of time geography and KDE, and then elaborates the meaning, framework and mode of the integration of the two. Finally, this article believes that the time-geographic reachable domain instead of the domain of KDE is an important means to improve the uncertainty measurement of space-time trajectory, but there is still a certain distance from the landing of the target.

The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the “spatial” aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the Gi and G*i statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify “outliers,” as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.

A recent paper expands the well‐known geographically weighted regression (GWR) framework significantly by allowing the bandwidth or smoothing factor in GWR to be derived separately for each covariate in the model—a framework referred to as multiscale GWR (MGWR). However, one limitation of the MGWR framework is that, until now, no inference about the local parameter estimates was possible. Formally, the so‐called “hat matrix,” which projects the observed response vector into the predicted response vector, was available in GWR but not in MGWR. This paper addresses this limitation by reframing GWR as a Generalized Additive Model, extending this framework to MGWR and then deriving standard errors for the local parameters in MGWR. In addition, we also demonstrate how the effective number of parameters can be obtained for the overall fit of an MGWR model and for each of the covariates within the model. This statistic is essential for comparing model fit between MGWR, GWR, and traditional global models, as well as for adjusting multiple hypothesis tests. We demonstrate these advances to the MGWR framework with both a simulated data set and a real‐world data set and provide a link to new software for MGWR (MGWR1.0) which includes the novel inferential framework for MGWR described here.

第六次全国人口普查数据面世以后,基于因子生态分析的中国城市社会空间结构研究虽取得较多成果,但方法创新还有待推进。本文以常州作为实证研究地区,将人口普查数据与大数据（城市POI数据）相结合,借助网格化处理和公共服务设施可达性计算方法,从居住内部环境（住房条件）和居住外部环境（设施可达性）两方面综合衡量城市居民生活质量,进而探讨城市居民生活质量空间结构及其与居民社会经济属性之间的关系。结果表明,常州城市居民生活质量空间结构呈现出较为明显的圈层结构与扇形结构叠加的模式,城市生活质量空间结构与居民社会经济属性在空间上存在一定的耦合关系,即不同类型居民属性区的生活质量存在较为明显的差异,各类居民属性区均有与之重叠度较高的相对应的生活质量区,这种空间关系的揭示对城市规划和管理具有一定的借鉴意义,同时也从居民社会空间与实体空间互动关系的角度对社会—空间辩证法进行了诠释。

在跨地区生计及其变迁的视角下,将流动人口在流入地和流出地城镇的住房状况有机结合,借助2016年中国流动人口动态监测调查数据和相匹配的城市特征数据,运用描述统计和多层次回归模型,探究流动人口城镇住房分层的特征及其影响因素。结果发现,流动人口的城镇住房已形成了一个从低到高依次由无城镇产权房、有城镇产权房（流出地或流入地城镇产权房）和多区域城镇产权房构成的“三阶四级式”的“金字塔型”结构。模型结果显示,流动人口个体及家庭的社会经济条件和流出地的区位禀赋是其实现城镇住房自有的重要基础和财务支撑,流入城市的经济发展水平、房价和城市规模仅对流入地和多区域城镇产权房有显著影响。因此,本文挑战了流动人口住房条件差的刻板印象,并吸引人们关注流动人口跨地区生计及其变迁对其城镇住房的影响,拓展了当代中国城镇住房分层研究的视角。

Using data from the 2016 China Migrant Dynamic Survey and the corresponding data on the characteristic of relevant cities, this paper first provides an updated and comprehensive study on the urban housing stratification of the floating population, and then uses multilevel multinomial logit model to estimate its determinants, based on the perspective of multi-locational household arrangements and their changes. The results show that the floating population′s urban housing stratification is characterized by the “three ranks” and “four levels”: the “three ranks” are composed of those who do not own any housing unit at the lowest rank, those owning one housing unit in an urban place of either origin or destination, and those owning more than one housing units in multiple urban locations at the highest rank, and the “four levels” consist of those who do not own any housing unit at the first level, those owning one housing unit in an urban place of origin at the second level, those owning one housing unit in an urban place of destination at the third level, and those owning more than one housing units in multiple urban locations at the highest level. Our findings also demonstrate that with the advancement of China′s urban and mobility transitions, some of the floating population in China has already had their owner-occupied housing in the destination cities and/or cities or towns of their places of origin, suggesting a significant change in the floating population′s housing conditions and their spatial arrangements. The results of the MMLM show that while the socio-economic conditions of the floating population at both the individual and family levels and the location endowment of their places of origin are important basis and financial foundation for the realization of their urban housing ownership, the per capita GDP, housing price and city size of the destination cities only affect the floating population′s housing ownership in their destination cities and in multiple urban locations. Our conclusions challenge the traditional stereotype of poor housing conditions of the floating population, provide a new perspective for urban housing stratification research in contemporary China, and call for more attention to the effects of multi-locational household arrangements and their changes of the floating population on their urban housing stratification.

影响高新技术企业分布的因素往往具有空间非平稳性,然而既有研究对其关注尚少。基于2017—2019年间认定的215791家高新技术企业数据,运用多尺度地理加权回归模型（MGWR）,刻画了中国高新技术企业的空间分布格局,识别了其影响因素的空间异质性与尺度效应,结果表明：① 2019年中国高新技术企业的空间分布呈现出在以“北上广深”为首位中心,以成渝与区域性中心城市为次位中心的高度集聚特征。② 企业内部因素、城市知识创造水平、技术创新水平、创新环境水平和外部连通水平共同影响了高新技术企业的空间分布。③ 影响高新技术企业分布的因素存在空间异质性,公司年龄、高校学生数量、互联网的影响呈现出“东-西”向空间分异格局,专利申请数、高新区、生活设施的正向影响呈现出“南-北”向空间分异格局,高铁的正向影响呈现出“东南-西北”向空间分异格局,研发费用投入对东北地区影响最大,市场化水平对京津冀和珠三角城市群地区影响最大。④ 影响因素存在尺度效应,靠近创新末端的变量具有更大的作用空间尺度。最后,本研究提出相关的政策建议,以期为高新技术产业的发展提供借鉴与参考。

The main driving forces of China’s economic growth have gradually shifted from production factors and investments to innovations since the 2010s. The high-tech industry is knowledge- and technology-intensive and is one of the key intermediaries in regional innovation systems. Therefore, the development of high-tech industry or high-tech enterprises contributes to regional development, especially in the context of the “new normal” of China’s economy. However, researchers have paid little attention to factors affecting the distribution of high-tech enterprises with spatially varying effects. With the help of the data of 215,791 high-tech enterprises and multiscale geographically weighted regression, this paper described the spatial distribution of China’s high-tech enterprises in 2019 and explored the spatial and scale variation of its determinants. The following conclusions were drawn. First, in 2019, the spatial distribution of China’s high-tech enterprises showed a high concentration, with Beijing, Shanghai, Guangzhou and Shenzhen as the primary cores, and Chengdu-Chongqing region and several regional centers as the secondary cores. Second, the attributes of high-tech enterprises, the capabilities of urban knowledge creation and technological innovation, innovation environment and extra-regional linkages are the main factors affecting the distribution of high-tech enterprises. Third, the determinants showed spatially varying effects. Specifically, the positive impacts of new companies, college students and the Internet presented an “East-West” spatial differentiation pattern; the positive impacts of the number of patent applications, high-tech zones and amenities showed a “South-North” spatial differentiation pattern. The positive effects of high-speed rail presented a “Southeast-Northwest” spatial differentiation pattern; R&D expenditure is a strong driver in Northeast China; marketization has the most significant impact on the Beijing-Tianjin-Hebei and Pearl River Delta urban agglomerations. Fourth, the determinants have different working scales, and the variables indirectly related to the profits of high-tech enterprises that need a transformation process (e.g., R&D investment, extra-regional linkages) have less significant spatial variation than other factors (e.g., high-tech zones, the number of college students, the number of patent applications). Finally, justified on territorial equity criteria, this research provided several suggestions for improving spatially unbalanced innovation (high-tech enterprises), helping developing regions jump faster (develop high-tech industry). Current findings broaden our understanding of the associations of geography and scale with high-tech industry in an emerging large-scale economy.

This study attempts to estimate the impact of population ageing on house prices. There is considerable debate about whether population ageing puts downwards or upwards pressure on house prices. The empirical approach differs from earlier studies of this relationship, which are mainly regression analyses of macro time‐series data. A micro‐simulation methodology is adopted that combines a macro‐level house price model with a micro‐level household formation model. The case study is Scotland, a country that is expected to age rapidly in the future. The parameters of the household formation model are estimated with panel data from the British Household Panel Survey covering the period 1999–2008. The estimates are then used to carry out a set of simulations. The simulations are based on a set of population projections that represent a considerable range in the rate of population ageing. The main finding from the simulations is that population ageing – or more generally changes in age structure – is not likely a main determinant of house prices, at least in Scotland.