Weighted Regression Model Research Articles

How does cultivated land fragmentation affect soil erosion: Evidence from the Yangtze River Basin in China

Reducing soil erosion (SE) is crucial for achieving harmony between human society and the ecological environment. The cultivated land fragmentation (CLF), directly or indirectly, alters soil structure, diminishes its water-holding capacity, and escalates the risk of SE. Scientific assessment of the effect of CLF on SE can provide new insights into controlling of SE across watersheds in China. However, few studies have quantified the effect of CLF on SE. Therefore, we utilized land use change data in the Yangtze River basin from 2000 to 2020, measuring the levels of CLF and SE using Fragstats and InVEST models. The bivariate spatial autocorrelation model was employed to reveal the spatial relationship between CLF and SE. Additionally, we constructed a spatial Durbin model and introduced the geographically and temporally weighted regression model to analyze the role of CLF on SE. The south bank of the upper and middle reaches of the Yangtze River basin exhibited high CLF and SE. The bivariate spatial autocorrelation results showed a significant positive spatial correlation between CLF and SE. The spatial Durbin model results showed that CLF had a spatial spillover effect and time lag on SE, and the effect of CLF on SE had an inverted “N" curve. The study also confirmed that last SE and neighboring SE areas influenced local SE. Currently, CLF had a negative effect on SE in the Sichuan Basin, Yunnan-Guizhou Plateau, and the middle and lower Yangtze River Plain, and positively in Qinghai, Hunan, and Jiangxi provinces. These findings suggest that the government should enhance cross-regional and cross-sectoral cooperation and monitoring of cultivated land changes to prevent and control SE effectively.

MSPA-based green space morphological pattern and its spatiotemporal influence on land surface temperature

The frequent occurrence of extreme heat events has notably affected human's living environment, and a considerable number of studies have reported that green space is an efficient measure by investigating the correlation between green space and land surface temperature (LST). However, spatiotemporal effects of green space on LST still remain unclear. In this study, green space patterns (e.g., core, islet, perforation, edge, loop, bridge, and branch) were identified through morphological spatial pattern analysis (MSPA). Moreover, the effects of green space pattern on LST in three periods were investigated through three kinds of models. As indicated by the results: (1) the geographically and temporally weighted regression model exhibited the optimal performance compared with other two models. (2) in general, the core, the edge, the bridge, and the branch significantly contributed to cooling, and the islet hindered cooling. However, the perforation and the loop exerted significant dual nature effects with the similar quantity of the negative and positive coefficients, showing relatively complex impact mechanism. (3) the intensity of the effect of the respective MSPA class varied across the study area. The core had the most substantial effect, which distributed in the south and middle corners. (4) the result suggested that a neighborhood scale in China, which was 960 m in this study, served as a basic unit in green space management. The spatiotemporal non-stationarity of the effects of green space morphological patterns on LST provided important insights into urban thermal environment improvement through urban green space planning and design.

Impact of Urban Expansion on Carbon Emissions in the Urban Agglomerations of Yellow River Basin, China

Continued urban expansion (UE) has long been regarded as a huge challenge for climate change mitigation. However, much less is known about how UE affects carbon emissions (CEs), especially in the urban agglomerations of the Yellow River Basin (UAYRB), China. In this regard, this study introduced kernel density analysis, the Gini coefficient, and Markov chains to reveal the UE patterns and carbon emissions intensity (CEI) in the UAYRB at the county level, and explored the spatial heterogeneity of the impact of UE on CEI with the geographically and temporally weighted regression model. The results show that both CEI and UE in the UAYRB showed a steady growing trend during the study period. The kernel density of CEI and UE revealed that CEI in the UAYRB was weakening, while the UE rate continuously slowed down. The Gini coefficients of both CEI and UE in the UAYRB region were at high levels, indicating obvious spatial imbalance. The Markov transfer probability matrix for CEI with a time span of five years showed that CEI growth will still occur over the next five years, while that of UE was more obvious. Meanwhile, counties with a regression coefficient of UE on CEI higher than 0 covered the majority, and the distribution pattern remained quite stable. The regression coefficients of different urban landscape metrics on CEI in the UAYRB varied greatly; except for the landscape shape index, the regression coefficients of the aggregation index, interspersion and juxtaposition index, and patch density overall remained positive. These findings can advance the policy enlightenment of the high-quality development of the Yellow River Basin.

Spatiotemporal ozone concentration variations and their influencing factors in Zhejiang, China, 2018–2022: A geographically and temporally weighted regression

In this study, we applied an extended geographically and temporally weighted regression model (GTWR) to explore the spatiotemporal non-stationarity of ground-level ozone (O3) concentrations and the influencing factors in four geographic zones of Zhejiang Province from 2018 to 2022. Our results showed that meteorological conditions and emissions were both important for O3 concentration during this period, and their influences showed significant spatial heterogeneity. Relative humidity (Rh) had negative associations with O3 concentration in all the 90 counties in our study period, and there was a strong positive correlation between maximum air temperature (MaxT) and O3 concentration. The impacts of precipitation on O3 were partly positive in Hangzhou Bay and mostly negative in the other three zones due to the precipitation time. The MaxT had the most significant influence in Hangzhou Bay due to the lowest NO2/VOCs ratio, and NO2 positively influenced O3 except in Hangzhou Bay. The high ground-level O3 occurrence in 2022 resulted from meteorological factors and emissions combined.

A Bayesian spatio-temporal model of COVID-19 spread in England

Exploring the spatio-temporal variations of COVID-19 transmission and its potential determinants could provide a deeper understanding of the dynamics of disease spread. This study aimed to investigate the spatio-temporal spread of COVID-19 infections in England, and examine its associations with socioeconomic, demographic and environmental risk factors. We obtained weekly reported COVID-19 cases from 7 March 2020 to 26 March 2022 at Middle Layer Super Output Area (MSOA) level in mainland England from publicly available datasets. With these data, we conducted an ecological study to predict the COVID-19 infection risk and identify its associations with socioeconomic, demographic and environmental risk factors using a Bayesian hierarchical spatio-temporal model. The Bayesian model outperformed the ordinary least squares model and geographically weighted regression model in terms of prediction accuracy. The spread of COVID-19 infections over space and time was heterogeneous. Hotspots of infection risk exhibited inconsistent clustering patterns over time. Risk factors found to be positively associated with COVID-19 infection risk were: annual household income [relative risk (RR) = 1.0008, 95% Credible Interval (CI) 1.0005–1.0012], unemployment rate [RR = 1.0027, 95% CI 1.0024–1.0030], population density on the log scale [RR = 1.0146, 95% CI 1.0129–1.0164], percentage of Caribbean population [RR = 1.0022, 95% CI 1.0009–1.0036], percentage of adults aged 45–64 years old [RR = 1.0031, 95% CI 1.0024–1.0039], and particulate matter (PM2.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {PM}_{2.5}$$\\end{document}) concentrations [RR = 1.0126, 95% CI 1.0083–1.0167]. The study highlights the importance of considering socioeconomic, demographic, and environmental factors in analysing the spatio-temporal variations of COVID-19 infections in England. The findings could assist policymakers in developing tailored public health interventions at a localised level.

The prevalence of common mental disorders across 18 ethnic groups in Britain during the COVID-19 pandemic: Evidence for Equality National Survey (EVENS)

BackgroundThe COVID-19 pandemic negatively impacted mental health in the general population in Britain. Ethnic minority people suffered disproportionately, in terms of health and economic outcomes, which may contribute to poorer mental health. We compare the prevalence of depression and anxiety across 18 ethnic groups in Britain during the COVID-19 pandemic. MethodsSecondary analysis of cross-sectional data (February–November 2021) from 12,161 participants aged 18–60 years old (N with data on outcomes = 11,540 for depression & 11,825 for anxiety), obtained from the Evidence for Equality National Survey (EVENS). Data were weighted to account for selection bias and coverage bias. Weighted regression models examined ethnic differences in depression (Centre for Epidemiologic Studies Depression Scale) and anxiety (Generalised Anxiety Disorder-7). Effect modification analyses explored whether ethnic differences in outcomes were consistent within age and sex sub-groups. ResultsCompared to White British people, greater odds of anxiety caseness (and greater anxiety symptoms) were observed for Arab (OR = 2.57; 95 % CI = 1.35–4.91), Mixed White and Black Caribbean (1.57; 1.07–2.30), any other Black (2.22, 1.28–3.87) and any other Mixed (1.58; 1.08–2.31) ethnic groups. Lower odds of depression caseness (and lower depressive symptoms) were identified for Chinese (0.63; 0.46–0.85), Black African (0.60; 0.46–0.79), and any other Asian (0.55; 0.42–0.72) ethnic groups. LimitationsCross-sectional data limits the opportunity to identify changes in ethnic inequalities in mental health over time. ConclusionsWe have identified certain ethnic groups who may require more targeted mental health support to ensure equitable recovery post-pandemic. Despite finding lower levels of depression for some ethnic groups, approximately one third of people within each ethnic group met criteria for depression.

Urban vitality and built environment from the perspective of spatiotemporal heterogeneity

ABSTRACT The spatiotemporal heterogeneity of the built environment’s effects on urban vitality is analysed through geographically and temporally weighted regression models. It is found that residents’ travel time and patterns lead to temporal heterogeneity, which is manifested by the fluctuation of the environmental effect of the peak in the morning and evening and the difference between weekdays and weekends. This is especially evident in the business district. Spatial heterogeneity is mainly reflected in the difference between the new urban area and the old city area. Single-function green spaces in areas with high functional complexity still have a weak attraction to people. Land use mix, scale and location are rather nonstationary in the dimensions of time and space.

Distribution Pattern of Species Richness of Endemic Genera in Mountainous Areas of Southwest China and Its Influencing Factors

China’s southwest mountainous region is one of the hotspots of biodiversity in the world. However, the study on the pattern of species richness distribution of endemic plants in this region and its influencing factors is still in the exploration stage. A clear regional spatial distribution pattern of plants and its influencing factors are very important for the protection and management of plant diversity in southwest China. In this study, 511 county-level regions in the southwest mountainous region were taken as examples to establish a plant species resource bank in the southwest mountainous region, and the distribution pattern of the proportion and abundance of endemic plant families and genera was analyzed based on 19 climate and environmental factors and 8 human disturbance factors in the southwest mountainous region. The geographical weighted regression model (GWR) was used to reveal the main factors affecting the spatial distribution of species. The results showed that: (1) A total of 4688 species investigated in the southwest mountain belong to 19 families and 32 genera. (2) The endemic species richness in the southwest mountainous region showed a significant uneven spatial pattern, mainly concentrated in the Heng Duan Mountain area, southwest Sichuan and northern Yunnan area, and the Ruoergai-Hongyuan area of West Sichuan Plateau. (3) The results of the GWR model showed that the diurnal mean temperature range, precipitation seasonality and distance from the main road had significant effects on species richness. The study on the distribution of endemic genera and their influencing factors in the southwest mountainous region has important implications for the exploration of the evolutionary history and species conservation of the mountain flora.

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 Heterogeneous Effects of Microscale-Built Environments on Land Surface Temperature Based on Machine Learning and Street View Images

Global climate change has exacerbated alterations in urban thermal environments, significantly impacting the daily lives and health of city residents. Measuring and understanding urban land surface temperatures (LST) and their influencing factors is important in addressing global climate change and enhancing the well-being of residents. However, due to limitations in data precision and analytical methods, existing studies often overlook the microscale examination closely related to residents’ daily lives, and lack a deep exploration of the spatial heterogeneity of the influencing factors. This leads to these results being ineffective in guiding the planning and construction of cities. Taking Shenzhen as a case study, our study investigates the effects of various microscale build environment characteristics of LST using street view images and machine learning. A convolutional neural network model adopting the SegNet architecture is used to perform semantic segmentation on street view images, extracting features of the microscale urban-built environment. The LST is inverted through the Google Earth Engine (GEE) platform. By using Multiscale Geographically Weighted Regression (MGWR) models, our study reveals the comprehensive impact of the urban-built environment on LST and its significant spatial heterogeneity. The findings indicate that the proportions of sky, roads, and buildings are positively correlated with LST, while trees have a significant cooling effect. Although earth and water can reduce LST, their overall contribution is minimal due to limitations in their area and distribution patterns. This study not only reveals the key factors affecting urban LST at the microscale but also emphasizes the necessity of considering the spatial heterogeneity of these factors’ impacts. This suggests the need for targeted strategies for different areas to effectively improve the urban thermal environment and achieve sustainable urban development.

Understanding the Spatiotemporal Dynamics and Influencing Factors of the Rice–Crayfish Field in Jianghan Plain, China

The rice–crayfish co-culture system, a representative of Agri-aqua food systems, has emerged worldwide as an effective strategy for enhancing agricultural land use efficiency and boosting sustainability, particularly in China and Southeast Asia. Despite its widespread adoption in China’s Jianghan Plain, the exact spatiotemporal dynamics and factors influencing this practice in this region are yet to be clarified. Therefore, understanding the spatiotemporal dynamics and influencing factors of the rice–crayfish fields (RCFs) is crucial for promoting the rice–crayfish co-culture system, and optimizing land use policies. In this study, we identified the spatial distribution of RCF using Sentinel-2 images and land use spatiotemporal data to analyze its spatiotemporal dynamics during the period of 2016–2020. Additionally, we used the Multiscale Geographically Weighted Regression model to explore the key factors influencing RCF’s spatiotemporal changes. Our findings reveal that (1). the RCF area in Jianghan Plain expanded from 1216.04 km2 to 2429.76 km2 between 2016 and 2020, marking a 99.81% increase. (2). RCF in Jianghan Plain evolved toward a more contiguous and clustered spatial pattern, suggesting a clear industrial agglomeration in this area. (3). The expansion of the RCFs was majorly influenced by its landscape and local agricultural conditions. Significantly, the Aggregation and Landscape Shape Indexes positively impacted this expansion, whereas proximity to rural areas and towns had a negative impact. This study provides a solid foundation for promoting the rice–crayfish co-culture system and sustainably developing related industries. To ensure the sustainable development of rice–crayfish co-culture industries in Jianghan Plain, we recommend that local governments optimize the spatial layout of rural settlements, improve transportation infrastructure, and enhance regional agricultural water sources and irrigation system construction, all in line with the national strategy of rural revitalization and village planning. Additionally, promoting the concentration and contiguity of RCF through land consolidation can achieve efficient development of these industries.

Association between oxidative balance score and resistant hypertension and arterial stiffness among US adults: A population-based study

Background and aimsPrior studies have established the correlation between oxidative balance score (OBS) and hypertension (HTN). While the association between OBS and resistant hypertension (RHT) as well as arterial stiffness among individuals with hypertension remains undisclosed. Methods and resultsIn this study, total of 15,910 adults diagnosed with hypertension were enrolled from NHANES 2001–2018. OBS was calculated and categorized into quartiles. Weighted regression model, stratified analyses and restricted cubic spline (RCS) were employed to evaluate the association between OBS and RHT, major adverse cardiovascular events (MACEs) and arterial stiffness in individuals with hypertension. Among enrolled participants, high OBS quartiles consistently demonstrated a negative association with resistant hypertension across all models (all p < 0.05), indicating robust stability. Compared with the lowest OBS quartile, the risk of resistant hypertension in the highest OBS quartile was decreased by 30.8% (95%CI 0.471–0.995, p = 0.049). After dividing OBS into dietary OBS and lifestyle OBS, a significant inverse association with lifestyle OBS and RHT was observed. With regard to MACEs, the inverse association was also found in participants with high OBS. Besides, the potential relation between OBS and arterial stiffness was explored and we found OBS was significantly associated with decreased arterial stiffness (β for ePWV, −0.014; 95%CI -0.026 to −0.001; p = 0.032). RCS analysis confirmed a nonlinear association between OBS and RHT, MACEs, cardiovascular death and nonfatal MI among participants with hypertension. ConclusionElevated OBS was negatively associated with the risk of RHT and arterial stiffness among US adults with hypertension.

Sex ratio shift after frozen single blastocyst transfer in relation to blastocyst morphology parameters

The sex ratio shift was observed in peoples who underwent ART treatment. Moreover, there is limited evidence on differences in sex ratio between single frozen-thawed blastocyst morphology, insemination type and transfer days. So further research is needed in this area with regard to factors possibly affecting the sex ratio. Retrospective study based on multicenter including two large assisted reproduction centers in Shanghai and Wuhan in China. A total of 6361 singleton delivery offspring after frozen-thawed blastocyst transfer. Propensity score weighting and logistic regression models were used to estimate the associations between blastocyst morphology grading and child sex ratio. The main outcome measures is singleton sex ratio. In our study, the primary outcome measure was sex ratio which was calculated as the proportion of male newborns among all live births. Higher quality blastocysts resulted in a higher sex ratio than single poor-quality frozen-thawed blastocyst transfer. Among the three blastocyst morphological parameters of trophectoderm (TE), Grade A and B were significantly associated with a higher sex ratio than Grade C. The similar trend was observed in both IVF and ICSI treated subgroups. As compared with expansion (4 + 3), expansion degree 6 achieved a higher sex ratio in overall populations and IVF treated subgroup. Transferring blastocysts of day 6 had the highest sex ratio both in IVF group and ICSI group. A 6.95% higher sex ratio in transferring blastocysts of day 5 in IVF group than those in ICSI group. No significant association between inner cell mass degree and sex ratio was observed. However, as compared with IVF treatment, all morphology parameters achieved the similar or the biased sex ratio favoring female in ICSI treated subgroup. Quality of blastocysts was positively associated with sex ratio. TE score and expansion degree rather than ICM were significantly associated with sex ratio at birth. ICSI treatment promotes the biased sex ratio favoring female.

Digitalization era: Investigating the spatial interplay between cyber human activity and economy with a hierarchical framework

The development of Information and Communication Technology has shifted human activity from offline to online, and promoted digital economy. These changes challenge traditional methodologies relying on physical human activity as a micro-level reflection of the macro-level economy. To address this, a hierarchical framework is proposed to characterize cyber human activity, incorporating activity diversity, size, and preference. Then, Ordinary Least Squares and Geographically Weighted Regression models are used to examine the spatial interplay between cyber human activity and township-level economy. K-Medoids clustering is further applied to coefficients in cyber GWR model to reveal mechanisms influencing local economies. Taking diverse geospatial data in Jilin Province, China as an example, the result indicates that the significant extreme values of cyber human activity have divided Jilin Province into four subregions, closely aligning with local economic segmentation. Moreover, the local economy can be better reflected by cyber human activity rather than physical ones, especially in highly digitized regions. Furthermore, the regions with similar influence mechanisms cluster geographically. These clusters are categorized into three types, namely, dynamics, robust, and balanced, representing different mechanisms influencing the local economy. Practical and theoretical implications are discussed, including using cyber human activity in assessing the economy and implementing adaptable economic policies at township level.

A neural network model to optimize the measure of spatial proximity in geographically weighted regression approach: a case study on house price in Wuhan

The estimation of spatial heterogeneity within real estate markets holds significant importance in house price modelling. However, employing a single or straightforward distance to measure spatial proximity is probably insufficient in complex urban areas, thereby resulting in an inadequate modelling of spatial heterogeneity. To address this issue, this paper incorporates multiple distance measures within a neural network framework to achieve an optimized measure of spatial proximity (OSP). Consequently, a geographically neural network weighted regression model with optimized measure of spatial proximity (osp-GNNWR) is devised for the purpose of spatially heterogeneous modeling. Trained as a unified model, osp-GNNWR obviates the need for separate pretraining of OSP. This enables OSP to delineate the modeled spatial process through a post hoc calculated value. Through simulation experiments and a real-world case study on house prices, the proposed model reaches more accurate descriptions of diverse spatial processes and exhibits better overall performance. The interpretable results of the case study in Wuhan demonstrate the efficacy of the osp-GNNWR model in addressing spatial heterogeneity within real estate markets, suggesting its potential for modelling and predicting complex geographical phenomena.

Temporal Variation and Spatial Heterogeneity in Boarding/Alighting Patterns at Urban Railway Stations: Implications for Estimating Optimal Construction Scale—A Case Study in Seoul, South Korea

Urban railways have become a prominent mode of public transportation within cities owing to their connectivity with other modes of transport and environmental friendliness. Various policies, such as the expansion of metropolitan areas and the development of megacities, have further emphasized the pivotal role of urban railways. Consequently, more railway stations are expected to be constructed in developed cities. However, the temporal variation in boarding and alighting patterns at each railway station is often overlooked. Failing to account for this variation, specifically the differences in peak-hour concentration rates, in railway station design may cause increased conflicts among users owing to concentrated demands during specific time periods, exacerbating congestion and diminishing the appeal of the urban railway systems. Therefore, this study investigated the correlation between the temporal variation in boarding and alighting patterns and the attributes (location) of railway stations in Seoul, South Korea, and analyzed the spatial heterogeneity of this correlation. Initially, the factors influencing the peak-hour concentration rates in railway stations were identified using a linear regression model. Peak hours were defined as morning and afternoon peaks and boarding and alighting were differentiated to account for the directional aspects of temporal variations in boarding and alighting patterns. The correlation between boarding and alighting patterns and the attributes of railway station influence zones was determined, and a geographically weighted regression model was estimated to analyze the spatial heterogeneity of this correlation based on railway station location. The analysis results revealed that railway stations in the southeastern and downtown areas of Seoul exhibited varying impacts of station attributes on boarding and alighting patterns even when the station attribute influence zones were identical. The contribution of this study is to evaluate the priorities of railway projects and its corresponding transportation policies. Regarding the policy goal recently announced by the Korean government, “Achieving Commute Times in 30-min range,” our finding will provide a good measure of accessibility whether it succeeds or not.

Unravelling the influencing hydrogeological factors contributing to land subsidence in the Tianjin Plain of China using a multi-scale geographically weighted regression model and monitoring data

The establishment of a quantitative relationship between land subsidence and its influencing factors is a crucial task in developing prevention strategies of land subsidence in specific areas. In this study we examined the dynamic patterns of land subsidence before and after establishing the South-to-North Water Division Project (SNWDP) in the Tianjin Plain (TJP) that aims to reduce groundwater extractions in certain areas. Statistical analyses were used to determine the key attributing factors contributing to land subsidence that were subsequently used to derive the so-called ‘critical water level of land subsidence’(CWLS), which were used to manage groundwater extractions with the objective of minimizing further land subsidence. The main results obtained were as follows: under the influence of the SNWDP, the groundwater level in the TJP stopped declining and the water level rose in most areas. The area of ‘strong’ subsidence, defined as the area with a subsidence rate greater than 50 mm a −1 , decreased from 16.0% before the SNWDP to 2.5% after. The results of the multi-scale geographically weighted regression (MGWR) model revealed that groundwater drawdown in the second and third confined aquifer was the main contributor to the areas classified as ‘strong’ and ‘medium’ land subsidence areas. Following the reduction in groundwater extractions, the behaviour of surface deformation can be grouped into two categories: first, a rebound in surface elevation; and, second, continuous compression with a lower subsidence rate. The first mode occurs mainly in the area of ‘medium’ and ‘weak’ subsidence, and the second mode occurs mainly in the ‘strong’ subsidence area. Supplementary material : The result of the MGWR model is available at https://doi.org/10.6084/m9.figshare.c.7103611

Exploring spatio-temporal impact of COVID-19 on citywide taxi demand: A case study of New York City.

Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents' urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study. City-wide taxi trip data from August 1st, 2020 to July 31st, 2021 in New York City was collected as model's dependent variables, and COVID-19 case rate, population density, road density, station density, points of interest (POI) were selected as the independent variables. By comparing GTWR model with traditional ordinary least square (OLS) model, temporally weighted regression model (TWR) and geographically weighted regression (GWR) model, a significantly better goodness of fit on spatial-temporal taxi data was observed for GTWR. Furthermore, temporal analysis, spatial analysis and the epidemic marginal effect were developed on the GTWR model results. The conclusions of this research are shown as follows: (1) The virus and health care become the major restraining and stimulative factors of taxi demand in post epidemic era. (2) The restraining level of COVID-19 on taxi demand is higher in cold weather. (3) The restraining level of COVID-19 on taxi demand is severely influenced by the curfew policy. (4) Although this virus decreases taxi demand in most of time and places, it can still increase taxi demand in some specific time and places. (5) Along with COVID-19, sports facilities and tourism become obstacles on increasing taxi demand in most of places and time in post epidemic era. The findings can provide useful insights for policymakers and stakeholders to improve the taxi operational efficiency during the remainder of the COVID-19 pandemic.

Factors affecting the housing prices in the metropolis of Tehran

Purpose This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable). Design/methodology/approach The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map. Findings Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities. Originality/value Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

Spatial Relationship of Inter-City Population Movement and Socio-Economic Determinants: A Case Study in China Using Multiscale Geographically Weighted Regression

In the current field of regional studies, there is a growing focus on regional spatial relationships from the perspective of functional linkages between cities. Inter-city population movement serves as an embodiment of the integrated functionality of cities within a region, and this is closely tied to the socio-economic development of urban areas. This study utilized Location-Based Services (LBSs) to collect the scale of inter-city population movement across 355 cities in China. Additionally, socio-economic data published by local governments were incorporated. By establishing a Multiscale Geographically Weighted Regression (MGWR) model, this research explores the spatial relationships between inter-city population movement and socio-economic influencing factors in China. This study aims to elucidate the spatial scales of the relationships between various variables. Our research findings indicate that the relationship between inter-city population movement and potential socio-economic determinants exhibits spatial non-stationarity. It is better to explore this spatial relationship through the MGWR model as there are different determinants operating on inter-city population movement at different spatial scales. The spatial distribution of the coefficient estimates shows significant regional differences and numerical variations. In China’s economically developed coastal regions, there is relatively balanced development among cities, with advanced manufacturing and producer service industries acting as significant drivers of mobility. In inland regions of China, city size is the most influential variable, directing a substantial flow of human and economic resources towards regional socio-economic hubs such as provincial capitals. The main contribution of this study is the re-examination of the relationship between inter-city population movement and socio-economic factors from the perspective of spatial scales. This approach will help China to consider the heterogeneity of different regions more extensively when formulating regional development policies, thereby facilitating the targeted promotion of regional element flow.