Spatial Quantile Regression Research Articles

Rethinking the association between green space and crime using spatial quantile regression modelling: Do vegetation type, crime type, and crime rates matter?

UN Sustainable Development Goals (e.g., Goal 16) have highlighted the importance of using policy tools (e.g., through urban planning) to prevent crimes. Existing evidence of the association between green space and crime is mixed. Some studies indicate that the inconsistencies may be due to the variance in types of vegetation and the rates of crime reported across regions and countries. This study aims to assess the conditional association between green space and crime by considering the influence of vegetation type (e.g., grassland, woodland), crime type (e.g., violence, theft) and rates of crime reported in Northern Ireland (NI), United Kingdom. Crime data were obtained from the Police Service NI and green space was determined by Land Cover Map at the Super Output Area (SOA) level provided by the UK Centre for Ecology & Hydrology. Spatial quantile regressions were used to model the adjusted association between green space and crime across areas with different rates of crime. The results showed that more grassland may be associated with lower crime rates, but only in areas with relatively low crime rates. More woodland may also be associated with lower crime rates, but only for areas with relatively high crime rates. Also, we found that associations between green space and crime varied by type of crime. In summary, policymakers and planners should consider green space as a potential crime reduction intervention, factoring in the heterogeneous effects of vegetation type, crime type and crime rate.

Spillover Effect of Urban Reconstruction from Announcement to Completion on Residential Prices: Application of Two-Stage Spatial Quantile Regression

This study analyzed four urban reconstruction projects in Taipei City (Zhufeng Tianmu, Huaku Sky Garden, Leqin Sanxi, and Yongde Yanyan) that were completed in 2016 based on data released by the Urban Regeneration R&D Foundation. The housing transaction data spanned from January 2008 to December 2017. The difference-in-difference method was used in conjunction with quantile regression to analyze the spillover effects of urban reconstruction projects on neighboring areas during the various phases of these projects. The empirical results showed that, during the planning and announcement phase, the effect on housing prices was negative and statistically significant for mid-to-low-priced units but positive and statistically significant for high-priced units. During the construction and post-completion phases, the effect was negative and statistically significant for low-priced, mid-to-low-priced, and mid-priced units, with low-priced units being the most affected group.

Social Vulnerability and COVID-19 Pandemic Outcomes: Evidence from Spatial Quantile Regression

This paper evaluates the empirical relevance of a prominent social vulnerability measure to local economic and public health outcomes at two distinct stages of the COVID-19 pandemic. In contrast to traditional least-squares regression, conditional quantile regression offers stronger evidence in support of social vulnerability for predicting the pandemic’s uneven economic devastation and death tolls across the United States. An extension of the spatial autoregressive geographically weighted regression model to conditional quantile regression reveals the varying role of social vulnerability within and across broad regions that were exposed to different levels of pandemic impacts.

THE EFFECTS OF TRANSACTION HOTSPOTS AND FLIPPING HOTSPOTS ON HOUSING PRICES

In contrast to much of the literature that focuses on the issue of spatial dependence in housing price research, this study addresses the spatial aggregation of housing transactions and analyzes the effects of transaction hotspots and short-term flipping hotspots on housing prices by using real housing transaction data in Taipei City, Taiwan. The empirical results show that after controlling for the effects of spatial dependence and individual housing attributes, the impact of transaction hotspot areas on housing prices is significantly negative, while the impact of flipping hotspot areas on housing prices is significantly positive. The results verify that the key to driving up housing prices lies in flipping activities. Furthermore, the results of the spatial quantile regression model show that low-priced residential properties are more sensitive to the spatial concentration of housing transactions and flipping transactions in the housing market. Our results have implications for the government’s policy intending to control hot trade volumes to cool skyrocketing housing prices in a booming housing market. It is suggested that the government should pay attention to restraining short-term flipping activities in the housing market rather than setting constrains on housing transactions.

Systemic Risk Spillover Effects among China’s Financial Institutions: Evidence from the Spatial Econometric Model

This study investigates the spillover and contagion effects of systemic risk among Chinese financial institutions in terms of the conditional Value-at-Risk method and spatial econometric techniques. We construct different representative spatial weight matrices to demonstrate various risk connective categories and contagion channels. The spatial autoregression model is built to reveal the different magnitudes of systemic risk contagion effects and extended as the spatial quantile regression model to measure the change in spillovers across quantiles. The results highlight that the spatial agglomeration pattern of institution-level systemic risk is highly concentrated within the same sector but highly disparate between the different sectors. The closeness of the asset price channel and the information channel would enhance the systemic risk spillover effects among institutions. The higher the single institution’s systemic risk level is, the stronger its spillovers among all of them within the same financial department, yet contrarily, the spillovers are larger at lower quantiles between different sectors due to the disparate spatial tendency of systemic risk. Besides, the spillover effects across quantiles indicate the higher tail contagion of systemic risk spatial spillovers, especially during 2015 A-share market crash and 2020 COVID-19 outbreak.

Good or excellent? Factors determining online hotel ratings. A spatial approach

ABSTRACT In a competitive environment, where hotel demand is lower than market supply, it is of interest to determine what factors explain how excellence differentiates certain hotels from others. Using spatial quantile regression, this research investigates the effect of locational factors as well as those related to the management of human capital investment in Spanish hotels rated on Booking.com. The study shows that human capital investment in hotels can significantly increase guest delight. As regards location, hotels in protected natural areas that are far from large cities obtain higher guest review scores. Additionally, a spatial spillover effect due to reputation transfer among nearby hotels with median scores is found. However, this effect is not observed for hotels with very high scores. These results can serve to rationalize the available resources and inform hotel owners and managers about the factors required to achieve high scores. 在酒店市场竞争激烈的环境下，其需求低于市场供给，确定哪些因素更影响酒店评级尤为重要。本文采用空间分位数回归方法，调查了Booking.com上评级的西班牙酒店的地理因素和人力资本投资管理相关的因素的影响。研究表明，酒店的人力资本投入可以显著增加客人的满意度。在地理位置方面，远离大城市的自然保护区内的酒店获得了更高的顾客评分。此外，由于得分居中的临近酒店之间有声誉转移现象，其空间溢出效应被观察到。然而，这种效应并没有在得分非常高的酒店上观察到。这些研究结果可以帮助可用资源合理化分配，并使酒店管理者知晓获得高评分所需的因素。

Does the Development of Digital Economy Affect Environmental Pollution?

The development of digital economy (Dig) promotes the development of green economy and the progress of ecological civilization. Based on the panel data of China from 2010 to 2020, this paper constructs the relevant index system and analyzes the impact of the Dig on environmental pollution (EP) by using a variety of econometric models. Subsequently, combined with the transmission effect to discuss its influence mechanism, the spatial quantile regression was used to explore spatial effects. The Dig is conducive to reducing EP, and there is an inverted U-shaped relationship between the two. Meanwhile, its influence has obvious regional heterogeneity, and the inhibition effect of Dig on EP is better in technology-intensive areas. Dig can alleviate EP by promoting technological innovation and optimizing industrial structure, but resource allocation has not played a role. In addition, the Dig has a significant impact on the distribution of EP in the middle quantile, and there is a negative spatial spillover effect on EP. Consequently, the study puts forward some suggestions to alleviate pollution from the aspects of promoting technological innovation and improving resource allocation efficiency.

Regional Differences, Dynamic Evolution and Driving Factors Analysis of PM2.5 in the Yangtze River Economic Belt

The proposal of a “dual-carbon” goal puts forward higher requirements for air pollution control. Identifying the spatial-temporal characteristics, regional differences, dynamic evolution, and driving factors of PM2.5 are the keys to formulating targeted haze reduction measures and ameliorating air quality. Therefore, adopting the Dagum Gini Coefficient and its decomposition method, the Kernel Density Estimation model, and spatial quantile regression model, this study analyzes the regional differences, dynamic evolution, and driving factors of PM2.5 concentrations (PM2.5) in the Yangtze River Economic Belt (YREB) and the upstream, midstream, and downstream (the three regions) from 2003 to 2018. The study shows that: (1) PM2.5 in the YREB was characterized by increasing first and then decreasing, with evident heterogeneity and spatial agglomeration characteristics. (2) Inter-regional differences and intensity of trans-variation were the primary sources of PM2.5 differences. (3) The density curve of PM2.5 shifted to the left in the YREB and the upstream, midstream, and midstream, suggesting that PM2.5 has declined. (4) Industrial service level (IS) and financial expenditure scale (FES) exerted a significant and negative effect on PM2.5 across the quantiles. On the contrary, population density (PD) showed a significant and positive influence. Except for the 75th quantile, the technology level (TEC) significantly inhibited PM2.5. The remaining variables had a heterogeneous impact on PM2.5 at different quantiles. The above results suggest that regional joint prevention and control mechanisms, collaborative governance mechanisms, and comprehensive policy mix mechanisms should be established to cope with PM2.5 pollution and achieve green, sustainable economic development of the YREB.

The determinants of Airbnb prices in New York City: a spatial quantile regression approach

AbstractIn this paper, we study the price determinants of Airbnb rentals, for the case of New York City, by developing a new dataset, which combines attributes of the property and of the related service, with other information available as open data. This dataset is employed within a spatial quantile semiparametric regression model, able to handle the intrinsic heterogeneity of house prices. The results confirm that property and service attributes play a significant role in determining rental prices, while some variables exert a different impact on prices in magnitude and sign, depending on the quantile considered.

Spatial Market Inefficiency in Housing Market: A Spatial Quantile Regression Approach

Spatial Market Inefficiency in Housing Market: A Spatial Quantile Regression Approach

The distribution of wage inequality across municipalities in Mexico: a spatial quantile regres-sion approach

Research background: According to classical labor economics, wage differences among regions of a country that has free-factor mobility should eventually vanish. However, the level of wage inequality among Mexican territories is increasing. The nature and causes of this discrepancy are worth identifying. Purpose of the article: To identify the spatial relationship of wage inequality that existed in the Mexican metropolitan system during the years 2010 and 2015. Methods: We develop a model of wages that considers the interaction between spatial units within a region. Then, we specify a spatial autoregressive model with the average wage per municipality as a dependent variable. This variable is spatially lagged along with other controls such as productivity, schooling, and migration. We combine data from population and economic censuses. Then, we perform a quantile regression to estimate the spatial effect of wage in a region upon quartiles of the wage distribution. Findings & value added: Wage inequality increases within a given region when the average wage increases in one of said region?s municipalities. This phenomenon occurs because in municipalities that are neighbors of the one that enjoys a wage increase, the average wage tends to decrease. The impact is larger in those municipalities whose average wage is in the lower range of the regional wage distribution. Wage inequality is also increased by internal migration and increased productivity. These latter findings are some of the first for Mexico at this aggregation level. A novel aspect of our study is its use of territory as an observation unit for which statistics from population and economic censuses are combined to draw inferences about spatial inequality.

Earthquake parametric insurance with Bayesian spatial quantile regression

With its transparent and fast claims payment, parametric insurance has been widely used to insure nature-related risks such as earthquakes, floods and hurricanes. In 2014, earthquake parametric insurance was introduced to provide coverage for earthquake losses occurred in Yunnan Province of China. However, as a main limitation of parametric insurance, basis risk is inevitable. In this paper, a Bayesian spatial quantile regression model is proposed to reduce the basis risk of earthquake parametric insurance. The effect of earthquake hazard, risk exposure, and vulnerability on economic loss are analyzed and considered in the quantile regression model. Since risk exposure and vulnerability at the epicenter cannot be observed, they will be treated as latent variables in the quantile regression model. Bayesian approaches are applied, and spatial correlation is considered to construct the prior distributions for the latent variables. Earthquake losses in Yunnan Province from 1992 to 2019 are collected and analyzed by the proposed model and methods. The payment mechanism and the corresponding premiums of 16 regions in Yunnan Province are then calculated. The results show that the loss ratio is more reasonable than the current earthquake insurance, and the basis risk is then reduced.

A spatial quantile regression model for driving mechanism of urban heat island by considering the spatial dependence and heterogeneity: An example of Beijing, China

• TAZ unit is applied to analyzing the relationships between urban features and LST. • SQR model explores heterogeneity of urban features on conditional low- or high-LST. • Spatial dependence bears a downward trend across the whole quantiles of LST. • SQR model is a promising method on estimation of LST. To seek mitigation strategies of urban heat island (UHI), many statistical methods have been applied to quantitatively explore the impact of driving factors on UHI. Nonetheless, the commonly used statistical models, such as ordinary least-squares (OLS) model and spatial autoregression model (SAM), were limited to explore the homogeneity relationship of driving factors on UHI. Although the spatial dependence of spatial data was considered, SAM failed to address the heterogeneity of relationships. In this study, a spatial quantile regression (SQR) model is innovatively introduced to investigate the relationships between driving factors and land surface temperature (LST) at different quantiles (heterogeneity) while considering spatial dependence. Substantial variations in SQR are found, compared with OLS and the spatial lag model (SLM). The coefficients of all driving factors in SQR are not constant and the spatial dependence shows an obvious downward trend throughout the whole quantiles of LST. The results of root mean square deviation (RMSD), coefficient of determination (R 2 ) and concordance correlation coefficient (CCC) show that SQR has better performance than SLM in LST prediction. These indicate that the characteristics of both spatial dependence and heterogeneity are concurrence. Therefore, joint prevention strategies with regional governance according to local conditions are suggested to mitigate LST.

Impact of public water supply unreliability on residential property prices in Marion County, West Virginia

AbstractWater supply unreliability in many public water systems stems from aging infrastructure. We measure unreliability by the issuance of boil water notices (BWNs) within one year prior to single-family residential sale observations. Using a spatial quantile regression framework on transactions between 2012 and 2017, we find statistically significant, negative relationships between BWNs and residential properties. The estimated impacts of unreliability on residential housing prices, however, are not uniform across the distribution of prices. Specifically, we find that BWNs have a larger impact on medium- to low-priced houses (at or below the 60 percent quantile) compared with high-priced houses. An aggregate marginal willingness-to-pay value of $4.2 million was computed for a one-day reduction in annual BWN throughout Marion County.

Evaluating the Impact of Urban Blue Space Accessibility on Housing Price: A Spatial Quantile Regression Approach Applied in Changsha, China

Urban nature spaces are increasingly recognized as essential urban features providing crucial amenities to the residents’ health and well-being. While many studies have been conducted focusing on the influence of green spaces on house prices, very few have explored the impact of urban blue spaces. In this study, we analyzed the proximity effects of different types and sizes of urban blue spaces on property value in Changsha metropolis, China, and examined the spatial quantile effect across different housing prices. A two-stage instrumental method (2SLS) hedonic model was employed to evaluate the impact of different types of urban blue space: river (mainstream and tributary), wetland park, and lakes (large, medium, and small). Spatial quantile regression (SQR) was then used to measure the spatial effect of accessibility on various house price ranges. The 2SLS results show that, except for small-sized lakes, proximity to blue spaces significantly increases property value. Analysis of the SQR model reveals that proximity to major blue spaces increases the marginal willingness-to-pay among homebuyers of high-priced properties, while ordinary blue spaces are more attractive to buyers of low- and medium-priced houses. This may be broadly related to the level of education, utility, and sensitivity to ecosystem services across income groups. Based on these findings, we recommend that urban planners adopt different strategies to promote space utilization efficiency. This is one of the few studies that analyze the capitalization of blue space accessibility in house prices across different quantiles. By categorizing blue spaces and employing the SQR model, this study found the effect of blue spaces on housing prices to be heterogeneous, providing new perspectives to the existing literature.

Identifying meteorological drivers of PM2.5 levels via a Bayesian spatial quantile regression

AbstractRecently, due to accelerations in urban and industrial development, the health impact of air pollution has become a topic of key concern. Of the various forms of air pollution, fine atmospheric particulate matter (PM2.5; particles less than 2.5 micrometers in diameter) appears to pose the greatest risk to human health. While even moderate levels of PM2.5 can be detrimental to health, spikes in PM2.5 to atypically high levels are even more dangerous. These spikes are believed to be associated with regionally specific meteorological factors. To quantify these associations, we develop a Bayesian spatiotemporal quantile regression model to estimate the spatially varying effects of meteorological variables purported to be related to PM2.5 levels. By adopting a quantile regression model, we are able to examine the entire distribution of PM2.5 levels; for example, we are able to identify which meteorological drivers are related to abnormally high PM2.5 levels. Our approach uses penalized splines to model the spatially varying meteorological effects and to account for spatiotemporal dependence. The performance of the methodology is evaluated through extensive numerical studies. We apply our modeling techniques to 5 years of daily PM2.5 data collected throughout the eastern United States to reveal the effects of various meteorological drivers.

Heterogeneous Effects of ICT across Multiple Economic Development in Chinese Cities: A Spatial Quantile Regression Model

Most previous articles estimate the effects of information communication technologies (ICTs) on economic growth average using national data without consideration of heterogeneity of ICT effects on cities across multiple economic development. The heterogeneity of ICT effects is confirmed to promote both the sustainability and equitableness of the whole cities. In order to investigate the heterogeneous effects of ICT between developed and less developed cities, a quantile spatial autoregressive (QSAR) model is applied to estimate coefficients at different quantiles while accounting for the spatial dependence of urban economy. We find significantly positive effects of ICT in local and neighboring cities after controlling the spatial dependence of urban economy. We have further found larger coefficients of ICT-related variables in cities with lower gross domestic product (GDP) per capital suggesting that digital dividend from ICT prefer the less developed cities over developed cities. Our conclusions indicate there would be “double dividend” from ICT, namely an improvement of both overall economic growth and balanced economic development among cities.

The capitalization of spatial healthcare accessibility into house prices in Taiwan: an application of spatial quantile regression

Purpose Population ageing is fast becoming a major social concern across the globe. This ageing trend unavoidably fuels elders’ demand for healthcare services. As the main users of health care service, whether the healthcare is geographically approachable in local areas is more imperative to senior residents with restricted mobility. This paper proposes to examine the effect of elders’ healthcare accessibility on property prices of Taipei Metropolis, Taiwan. Design/methodology/approach Luo and Qi’s (2009) enhanced two-step floating catchment area method – taking both healthcare demand and supply into account – was used to measure three types of healthcare services: “physician-to-elder ratio”, “hospital bed-to-elder ratio” and “ambulance-to-elder ratio”. Spatial quantile regression (SQR) model was then used to examine the spatial effect of healthcare accessibility on different property price ranges. Findings The “physician-to-elder ratio” and “hospital bed-to-elder ratio” demonstrated expected consistent positive effects across all quantiles of property prices (p < 0.01) in SQR, and its effects aggravated as the quantiles of property prices rose. The “ambulance-to-elder ratio” demonstrated a non-linear influence on property prices (i.e. a negative effect on lowest quantile prices but a positive on higher quantile prices) possibly due to the semi-obnoxious characteristic of the ambulance. That is, residents living in lower priced neighbourhoods may dislike ambulances’ annoying sound of sirens (i.e. ambulances’ disamenity), while residents living in higher valued neighbourhoods may on the contrary appreciate ambulances’ healthcare services (i.e. amenity). Practical implications These findings are expected to offer some insights for government’s policies in providing elders in their later years with good residential quality and easy access to healthcare resource. Originality/value This paper is one of the few studies that consider the capitalization of the spatial healthcare accessibility to elders into property prices. In this ageing trend across the globe, although all the accessibility to medical resources should be equally critical, the application of spatial quantile regression revealed residents’ inconsistent tendency against semi-obnoxious ambulances. It provides a different perspective in defining the importance of healthcare accessibility in neighbourhoods.

The Effects of Government-Announced Soil Liquefaction Potential on Housing Prices in Reported Areas: A Two-Stage Spatial Quantile Regression Analysis

This study examines the effects of a government announcement of soil liquefaction potential on housing prices in the reported areas, and explores the rate at which these prices changed after the announcement. This investigation utilizes published real estate price registration data from Taipei City, Taiwan covering the period January 1, 2015 to December 31, 2017, and resolves the issue of data heterogeneity by applying nearest-neighbor matching (an aspect of propensity score matching), and employ the difference-in-difference method in conjunction with two-stage spatial quantile regression. The empirical results indicate that although low, moderate, and high housing prices in potential soil liquefaction areas were all negatively affected by the announcement initially, after a period of one and a half years, the negative effect decreased for low housing prices and was no longer significant for moderate and high housing prices.

Latent relationships between environmental impacts of cultivation practices and land market: Evidences from a spatial quantile regression analysis in Italy

Several economic approaches can be carried out for managing the environmental impacts in agriculture, i.e. property and bargaining rights, economic incentives, ecological fees, etc. These approaches can be mainly applied to the cultivation phase or to the markets of the agricultural commodities. However, a further ambit in which the regulatory systems could be useful to trigger sustainable cultivation practices is the farmland market. Hence, this study contributes to the setting of market mechanisms based on incentives or fees related to the environmental impacts of farming practices for reducing the pressures of the production processes on the environmental components.The study, through a hedonic pricing method based on a spatial quantile regression and integrated by an environmental analysis, highlights different trends of land value determinants along the quantiles of the selling prices as the intensity of the cultivation system varies. The results show that the most important value determinants of the vineyards cultivated through the semi-extensive production system are related to the quality of grapes. Conversely, in presence of the intensive production system, the most important value determinants are related to the high yields, which also generate high greenhouse gas emissions, in contrast with the “polluter pays” principle.The results allow the assessment of the implicit marginal prices concerning the impact of the environmental indicator on land value per cultivation system, so as to favour the setting of regulatory monetary strategies able to foster farmers towards cleaner agricultural practices.