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Abstract: Abstract Growth driven by innovation is of great importance to help China become a long-term economic force and gain international competitive advantage. Based on evolutionary economic geography theory, this study estimated the related variety and unrelated variety by using four-digit code classified industrial data; it analysed their spatial–temporal evolution and influence on China’s regional innovation levels using prefecture-level city data from 1998 to 2013. Results showed that average levels of related variety increased whereas unrelated variety declined. Regions with high related variety spread from the east coastal region to the central region of China, and regions with high unrelated variety moved from the northeast and north regions to the central region. Related variety promoted innovation by knowledge spillover and produced regional spillover and agglomeration effects among regions with similar levels of economic development. Conversely, unrelated variety hindered the growth of innovation and had no regional spillover among different regions. Technological innovation was the transmission mechanism of growth from related variety to economic development. Thus, it is necessary to strengthen the agglomeration effects of industries with related variety and improve each region’s innovation capacity. PubDate: 2022-06-01
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Abstract: Abstract The non-random selection of people into neighbourhoods complicates the estimation of causal neighbourhood effects on individual outcomes. Measured neighbourhood effects could be the result of characteristics of the neighbourhood context, but they could also result from people selecting into neighbourhoods based on their preferences, income, and the availability of alternative housing. This paper examines how the neighbourhood effect on individual income is altered when geographic selection correction terms are added as controls, and how these results vary across three Dutch urban regions. We use a two-step approach in which we first model neighbourhood selection, and then include neighbourhood choice correction components in a model estimating neighbourhood effects on individual income. Using longitudinal register datasets for three major Dutch cities: Amsterdam, Utrecht and Rotterdam, and multilevel models, we analysed the effects for individuals who moved during a 5-year period. We show that in all cities, the effect of average neighbourhood income on individual income becomes much smaller after controlling for explicitly modelled neighbourhood selection. This suggests that studies that do not control for neighbourhood selection most likely overestimate the size of neighbourhood effects. For all models, the effects of neighbourhood income are strongest in Rotterdam, followed by Amsterdam and Utrecht. PubDate: 2022-06-01
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Abstract: Abstract China has achieved remarkable results and made great contributions in poverty reduction alleviation. However, with the continuous advancement of poverty alleviation, the emergence of re-poverty has become a tangible problem. In this study, an analysis framework for vulnerability to re-poverty (VRP) is established. Furthermore, the spatial–temporal patterns and obstacle factors of VRP in rural China from 2000 to 2017 are explored. The results show the overall spatial pattern of VRP in rural China in the past 18 years exemplifies spatial heterogeneity. Notably, the “Hu Huanyong Line” is the boundary, and are significant differences between the east and west China. Next, VRP shows a significant global spatial positive correlation, indicating a significant spatial agglomeration characteristic. Moreover, the local spatial autocorrelation analysis shows that VRP has a certain spatial dependence. Finally, the rate of urbanization and number of rural employees have become the key obstacles to VRP in rural China. Therefore, VRP in different regions are influenced by the proportion of gross output value of agriculture to gross regional output value, average annual precipitation, elevation, relief degree of land surface, number of welfare agencies, and the Normalized Difference Vegetation Index (NDVI). Based on these findings, some policy recommendations are proposed, including promoting new urbanization, providing rural employment, strengthening infrastructure, and improving the resilience of ecological environments. PubDate: 2022-06-01
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Abstract: Abstract Labor change in China has reached a turning point, and labor shrinkage and its related issues have widely attracted attention in recent years. However, studies that examine labor shrinkage and its driving forces from a geographical perspective are still limited. This study analyzed the trends and spatial differences of labor shrinkage at national and various regional scales from 1990 to 2015, and then investigated the driving forces behind these processes by combining the variables of migration and demographic structural change. We found that labor shrinkage areas at the subnational level have increased significantly in the period from 2010 to 2015. We also found that they first emerged in the Central and Western regions, and then gradually expanded to the Coastal and Northeast regions. Although migration still matters, dramatic demographic structural changes have played an increasingly important role in explaining the recent trend of labor shrinkage. Moreover, the driving forces varied in different local contexts: while lagging economic and employment growth have been the main drivers of labor shrinkage in the Central and Western regions, many shrinkage areas in the Coastal region can be linked to the decline of the youth population on the one hand and an aging population on the other hand. These findings highlight the geographic heterogeneity of labor shrinkage as well as the driving forces at regional levels, and they recommend that local governments take differentiated measures to address labor shrinkage. PubDate: 2022-06-01
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Abstract: Abstract Nighttime light (NTL) data records the nocturnal emission signals of human activities and provides an accurate and continuous basis for the study of social and economic development. A new geostatistical model using NTL data is proposed to estimate the effects of human disturbance on wildlife habitats. This paper presents an extended application of NTL data using integrated nested Laplace approximation with stochastic partial differential equation (INLA-SPDE) for modeling the spatial correlation. Several covariates, such as incision depth, road network, population, and land cover, were used to delineate the distribution of human disturbance. Our method includes an improved SPDE construction approach that allows the detection of non-stationary data in NTL datasets. Among four types of SPDE modes, the urban mode achieved the best fitting performance, revealing the aggregation effect of cities in NTL data. Comparison with previous research shows that, the estimation results of human disturbance coincided with Wilderness Quality Index, and demonstrated its potential capacity for application in ecological protection and biological conservation through an analysis of natural reserves in Yunnan Province. PubDate: 2022-06-01
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Abstract: Abstract As air pollution becomes more serious in China, it is critical to study its influencing factors for targeted environmental governance. Existing researchers have conducted various studies on the factors of PM2.5 (fine particulate matter with a diameter less than or equal to 2.5 microns) pollution. However, these studies mostly conducted analyses focused on macroscopic factors and lacked an impact analysis of specific entity distributions on PM2.5 pollution. Furthermore, most existing studies used ordinary regression models that ignored the spatial heterogeneity of influence of various factors on pollution, leading to biased results. To address these issues, focusing on air quality in heavily polluted city (Weifang City), this study aims to measure the influence of spatial differentiation of the impacts of four roughly classified POIs, namely, industry, restaurants, scenic spots, and parking lots, on PM2.5 pollution quantitatively by using a geographically weighted regression (GWR) model (spatial varying-coefficient regression model). The results indicate that the spatial distribution of effects of industry, restaurant and parking lot POIs on PM2.5 concentrations in Weifang are similar. The impact of all four POIs on PM2.5 is spatially nonstationary and have certain spatial trends, parking lots have the greatest influence on PM2.5 pollution. Based on the findings, pollution prevention and control measures are suggested to be designed based on the actual situation. For instance, some counties in Weifang should be encouraged to develop tertiary industries dominated by tourism. This research investigated the spatial impacts of the specific entity distributions on air pollution and provided targeted advice based on findings, which can contribute to policy-making aimed at air pollution mitigation. PubDate: 2022-06-01
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Abstract: Abstract This study examines the impacts of urbanization and energy-environment policies on housing prices across 30 provinces in China from 2000 to 2015. Results indicate that current urbanization policy and current housing policy do not significantly affect the housing prices, but the housing prices are determined by current energy-environmental policy. In addition, we argue that the effects of post urbanization policy and post energy-environmental policy remain highly significant, while the implementation of energy-environmental policy results in an increase in housing prices through the reduction of the emission of industrial dust. Finally, the Clean Energy Policy drives up housing prices in highly regulated provinces in China. PubDate: 2022-06-01
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Abstract: Abstract Analyzing how the proximity to certain features or particular places in a city increases or decreases crime risk across space is a fundamental issue in quantitative criminology from both explanatory and predictive perspectives. Regarding the latter, the detection of high-risk cells is of special interest for practical reasons. There are several statistical modeling approaches that can be implemented in order to fulfil these two main objectives. The purpose of this study is to compare risk terrain modeling (RTM), a method widely used among quantitative criminologists, with a non-linear effects model that considers a non-linear function of distances to the selected places. To this end, a dataset containing crime events recorded in Valencia (Spain) along four years was used to perform the comparison. Several socio-demographic covariates and a selection of places in the city were considered for modeling crime counts with both the RTM and the non-linear approaches. The two modeling techniques were moderately coherent with regard to detecting certain types of places as responsible of higher (or lower) crime rates, but several differences arose. Furthermore, the non-linear model was more accurate than RTM to predict future crime occurrences for each of the three crime types that were considered for the analysis. In conclusion, the possibility of modeling the effect of a place on crime risk through non-linear functions appears as one competitive alternative or at least complement to RTM that may deserve further consideration. PubDate: 2022-06-01
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Abstract: Abstract With rapid urbanization, cities plays an increasingly key role in addressing CO2 emission-related issues. This study aims at analysing the spatial relation between city size and CO2 emissions in the case of Yangtze River Delta Urban Agglomeration (YRDUA) in China over the years from 2006 to 2016, making a contribution to the existing body of knowledge of the relationships between urban form and CO2 emissions. This paper identified the following main findings: (1) There is a U-shape relationship between population size and CO2 emissions in YRDUA; (2) There is a negative sublinear relationship between CO2 emissions and city density in YRDUA; (3) The ideal urban form for low CO2 performance in YRDUA is 2.716 million people living in high population density; (4) Increasing population size is an effective but not a long-term approach for CO2 emissions reduction, because for every marginal increase of city density, the marginal reduction of CO2 emission will decrease. (5) A demographic change in YRDUA from low-density cities to high-density cities would benefit CO2 emission performance. These findings confirm the important roles of population size and density for CO2 emissions reduction in urban agglomeration and so help shape current policy debates. PubDate: 2022-06-01
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Abstract: Abstract The identification of seriously infected areas across a city, region, or country can inform policies and assist in resources allocation. Concentration of coronavirus infection can be identified through applying cluster detection methods to coronavirus cases over space. To enhance the identification of seriously infected areas by relevant studies, this study focused on coronavirus infection by small area across a city during the second wave. Specifically, we firstly explored spatiotemporal patterns of new coronavirus cases. Subsequently, we detected spatial clusters of new coronavirus cases by small area. Empirically, we used the London-wide small-area coronavirus infection data aggregately collected. Methodologically, we applied a fast Bayesian model-based detection method newly developed to new coronavirus cases by small area. As empirical evidence on the association of socioeconomic factors and coronavirus spread have been found, spatial patterns of coronavirus infection are arguably associated with socioeconomic and built environmental characteristics. Therefore, we further investigated the socioeconomic and built environmental characteristics of the clusters detected. As a result, the most significant clusters of new cases during the second wave are likely to occur around the airports. And, lower income or lower healthcare accessibility is associated with concentration of coronavirus infection across London. PubDate: 2022-06-01
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Abstract: Abstract Human capital has been acknowledged as a key driver for innovation, thereby promoting regional economic development in the knowledge era. University graduates from China’s “first-class” universities—the top 42 universities, included in the “double first-class” initiative, are considered highly educated human capital. Their migration patterns will exert profound impacts on regional development in China, however, little is known about the migration of these elite university graduates and its underlying driving forces. Using data from the 2018 Graduate Employment Reports, this study reveals that the uneven distribution of “first-class” universities and regional differentials largely shaped the migration of graduates from the university to work. Graduates were found aggregating in eastern first-tier cities, even though appealing talent-orientated policies aimed at attracting human capital had been launched in recent years by second-tier cities. Employing negative binomial models, this study investigates how the characteristics of the city of university and destinations affect the intensity of flows of graduates between them. The results showed that both jobs and urban amenities in the university city and destination city exert impacts on the inflow volume of graduates; whereas talent attraction policies introduced by many second-tier cities are found not to exert positive effects on attracting “first-class” university graduates presently. The trend of human capital migration worth a follow-up investigation, particularly given ongoing policy dynamics, and would shed light on the regional development disparities in China. PubDate: 2022-06-01
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Abstract: Abstract Tourism plays a vital role in many rural areas and has been proven a highly resilient sector following an unforeseen shock. Recent evidence points out its capacity to transfer resilient proprieties to the economic landscape of destinations. Yet, little is known about the way structural features of a destination impacts the tourism-induced resilience. Our study builds a mediation model for tourism-based economic resilience of rural destinations in relation to the accessibility towards urban areas. The results suggest that the accessibility towards the larger cities does not have a measurable effect upon the tourism-induced resilience. However, when the accessibility index took into consideration the medium cities and towns, a clear, distinguishable, effect was observed but only for time-distances up to 76 min. Therefore, we were able to map all rural areas that could benefit in a recovery period from their proximity from a city. The study increases our understanding of cone-like relationship model in tourism studies and completed previous approaches which established a relation between tourism growth and economic growth. Moreover, it confirms the role that accessibility plays during the recovery period and the contributions of tourism activities to strengthening the urban–rural synergies. Several policy recommendations regarding an integrated and efficient destination management are addressed at the end of the paper. PubDate: 2022-04-30
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Abstract: Abstract The scarcity of urban land resources requires a well-organized spatial layout of land use to better accommodate human activities, however, as a widely accepted concept, the integration of land use and transport is not given due consideration in land use spatial optimization (LUSO). This paper aims to integrate land use and transport in LUSO to support urban land use planning. Maximizing accessibility fitness, which follows the underlying logic between land use types and transport characteristics, is introduced into multi-objective land use spatial optimization (MOLUSO) modelling to address transport considerations, together with widely-used objectives such as maximizing compactness, compatibility, and suitability. The transport characteristics, in this study, are identified by driving accessibility, cycling accessibility, and walking accessibility. Accessibility maps, which quantify and visualize the spatial variances in accessibility fitness for different land use types, are developed based on the empirical results of the relationship between land use types and transport characteristics for LUSO and addressing policy issues. The 4-objective LUSO model and a corresponding non-dominated sorting genetic algorithm (NSGA-II) based optimization method constitute a prototype decision support system (DSS) for urban land use planning. Decision-makers (e.g., planning departments) can choose an ideal solution to accommodate urban development needs from a set of Pareto-optimal alternatives generated by the DSS. The approaches to creating accessibility maps and MOLUSO modelling are demonstrated by the case study of Eindhoven, the Netherlands. This study advocates limited changes to the current land use pattern in urban planning, and the LUSO emphasizes urban renewal and upgrading rather than new town planning. PubDate: 2022-04-27
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Abstract: Abstract This study offers a literature review and bibliometric analysis aiming to enhance our understanding of the actual contribution of resilience approaches to spatial and territorial development and planning studies. Using citation link-based clustering and statistical text-mining techniques (in terms of prevalence of topics, over time, extraction of relevant terms, keywords frequencies), our study maps scientific domains that include the spatial dimension of resilience thinking. It offers a systematic assessment of modern approaches by connecting profoundly theoretical views to more instrumental and policy-oriented approaches. Firstly, the theoretical background of spatial resilience used in numerous studies in various fields is analysed from the viewpoint of the type of embedded resilience (engineering, ecological, social-ecological, economic, social etc.). Secondly, we review and discuss the significance of three main and consistent research directions in terms of different scales and political/institutional contexts that matter from the viewpoint of spatial and territorial planning. Our findings show that spatial resilience debates are far from being settled, as according to many scientists, resilience measurements are often based on technical-reductionist frameworks that cannot comprehensively reflect the complex systems and issues they address. Our conclusions highlight the necessity of a harmonized framework and integrated perspective on resilience in sustainable territorial planning and development, in both theoretical and empirical contexts. PubDate: 2022-04-23
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Abstract: Abstract We focus on a poor region and study the nexuses between health interventions undertaken by a regional authority (RA) and this region’s Holling resilience in the presence of a pandemic such as Covid-19. First, we show how a health intervention by the RA probabilistically affects an appropriately defined health indicator. Second, we compute the chance that the health status of this region’s population falls below a minimum acceptable level in the presence of the health intervention. Third, we solve an optimization problem in which the RA maximizes the likelihood that the health status of this region’s population stays above a minimum acceptable level at a given economic cost. Our analysis demonstrates that there is a connection between a health intervention, a region’s health status, and its Holling resilience by presenting two applications. Our analysis reveals that this paper’s methodology can be used to compute a region’s Holling resilience with a particular health intervention. The main policy implications of our analysis concern the need for a RA to pay attention to (i) a region’s health infrastructure and financing, (ii) sufficient engagement with the region’s population, (iii) regional heterogeneity, (iv) data collection, and (v) the likelihood that sicker regions are likely to require more health interventions at a higher cost. PubDate: 2022-04-14
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Abstract: Abstract On March 23, 2020, a national lockdown was imposed in the UK to limit interpersonal contact and the spread of COVID-19. Human mobility patterns were drastically adjusted as individuals complied with stay-at-home orders, changed their working patterns, and moved increasingly in the proximity of their home. Such behavioural changes brought about many spillover impacts, among which the sharp and immediate reduction in the concentration of nitrogen-based pollutants throughout the country. This work explores the extent to which urban Nitrogen Dioxide (NO2) concentration responds to changes in human behaviour, in particular human mobility patterns and commuting. We model the dynamic and responsive change in NO2 concentration in the period directly following national lockdown and respective opening orders. Using the national urban air quality monitoring network we generate a synthetic NO2 concentration series built from a time series of historic data to compare expected modelled trends to the actual observed patterns in 2020. A series of pre- and post-estimators are modelled to understand the scale of concentration responsiveness to human activity and varying ability of areas across the UK to comply with the lockdown closing and response to openings. Specifically, these are linked to workday commuting times and observed patterns of human mobility change obtained from Google mobility reports. We find a strong and robust co-movement of air pollution concentration and work-related mobility – concentrations of NO2 during typical weekday commuting hours saw a higher relative drop, moving in tandem with patterns of human mobility around workplaces over the course of lockdowns and openings. While NO2 concentrations remained relatively low around the time of reopening, particularly during commuting hours, there is a relatively fast responsiveness rate to concentrations increasing quickly in line with human activity. With one of the key Government advice for workers to take staggered transportation into work and lessen the burden of rush hours and adopting more flexible work-home arrangements, our results would suggest that reductions in NO2 in urban areas are particularly responsive to broader human patterns and dynamics over time as we transitioned towards new working routines. PubDate: 2022-04-12
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Abstract: Abstract Corruption levels are clustered in space and time simultaneously, and they differ not only between but also within countries. This study adds to the literature by shedding new light on the cause of corruption. Specifically, it investigates a further question that arises whether there is an effect on the corruption levels of neighboring sub-national regions within a country over time. Using the Dynamic Spatial Durbin models with provincial data during 2006–2017, this paper finds that the corruption levels of a specific province are influenced by both corruption level and its lags of neighboring provinces, and this effect decreases with more considerable geographic distance between provinces. Estimated results also show that the spatio-temporal dependence of corruption levels is explained by spatial externalities, including immigration, provincial governance and policy, but not economic development. These results provide theoretical applications for studying corruption and shed light on anti-corruption policy design in transitional countries where corruption is rampant. PubDate: 2022-04-09
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Abstract: Abstract The main purpose of this paper is to develop a systematic, spatially explicit approach to the analysis of the ecosystem services provided by the metropolitan landscape that can act as a support for green infrastructure planning. To achieve this, we have proposed a set of indicators to assess and map nine ecosystem services—including regulating, provisioning, habitat and cultural services. This methodology has been applied to three case studies in the south of Spain: the metropolitan areas of Seville, Malaga-Marbella and Cordoba. Despite the geographical proximity of these areas to one another, the indicators show that there are significant differences in their potentialities and available resources to form a multipurpose green space system. The results suggest that further reflection is needed on how the concept of green infrastructure can be applied to metropolitan areas, especially in the Mediterranean region and other similar geographical contexts. Instead of understanding green infrastructure strictly in terms of a network of interconnected green spaces and natural areas, planning initiatives should assign a more important role to the landscape matrix and, in particular, to the multifunctional cultivated space on the urban fringe. In addition, more thought needs to be given to how to create functional green corridors in the metropolitan landscape for public use and habitat conservation. From the perspective of spatial planning, the methodology proposed has been demonstrated to be a useful tool to identify key spaces for the provision of ecosystem services. PubDate: 2022-04-06
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Abstract: Abstract In the past decade, China's rapid development has affected the higher education population's formation mechanism and spatial distribution. Therefore, this study aims to analyze the spatial distribution pattern and formation mechanism of the spatial agglomeration of the higher education population of 30 provinces in China from 1990 to 2017 through Exploratory Spatial Data Analysis (ESDA) and spatial econometric analysis. The study's main results are as follows: (1) The overall spatial distribution of China's higher education population is uneven, indicating that the population is distributed in the West and dense in the East. (2) The agglomeration of the higher education population is spatially dependent. The agglomeration of the higher education population is mainly high-high and low-low types. Moreover, due to social, economic, and environmental factors, the higher education population's formation mechanism in the study area and neighboring areas is uneven. Explanatory factors of the higher education population, namely gross domestic product (GDP), industrial structure (IS), urbanization (UP), number of universities (NU), and technological innovation (TI), have a positive and significant impact on the spatial agglomeration of the higher education population and living consumption expenditure (CON) and elevation (EL) has a negative and significant effect on the population of higher education. Outstanding economic factors such as industrial structure has driven the spatial agglomeration of the higher education population. In contrast, environmental factors such as EL have played a unique role in promoting the higher education population's outward diffusion. Therefore, the government should encourage the transfer of tertiary industry in the central and western regions to accelerate the infrastructure, social and economic development of these regions, which may reduce the uneven distribution of the higher education population in various areas. PubDate: 2022-03-05
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Abstract: Abstract The present study tests if the spatial variability in endogenous and exogenous components of population growth (natural balance and migration balance) reflects the transition from a mono-centric (compact-dense) settlement structure towards a more polycentric agglomeration based on sub-centers. The spatial distribution of population growth rates across municipal units in Barcelona province (Spain) was analyzed over a sufficiently long time period (44 years between 1975 and 2018, partitioned into four intervals of equal length, 1975–1985, 1986–1996, 1997–2007, 2008–2018) at five concentric rings around Barcelona. Natural balance and migration rates were investigated vis à vis selected territorial indicators using descriptive, inferential and multivariate statistical techniques. The contribution of natural balance to overall population growth decreased with the distance from downtown Barcelona; the contribution of migration balance increased with population density. In the first period (1975–1985), natural balance was higher in peri-urban locations and sub-central municipalities. In the following two periods (1986–1996 and 1997–2007), the contribution of natural balance to total population growth decreased, showing a greater spatial concordance with migration rate. In the last period (2008–2018), natural population growth decelerated further, and the impact of migration balance was extremely variable across space. The empirical results of this study shed light on the (apparent or latent) demographic processes underlying Barcelona's long-term growth, and provide evidence in favor of a relationship between polycentric development and the shift from natural growth to immigration as the main engine of urban expansion in recent times. PubDate: 2022-03-01 DOI: 10.1007/s12061-021-09395-2
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