Geographically Weighted Research Articles

Toward carbon neutrality: The impact of manufacturing agglomeration on total factor energy efficiency

Improving energy efficiency is an effective way to achieve carbon neutrality in the context of growing advocacy for a green economy in both academia and government. In this study, we analyzed the impact mechanism of the manufacturing agglomeration on total factor energy efficiency. Based on this work, we identified 30 provinces (autonomous regions and municipalities) in China using 2002 to 2017 panel data. We used the data envelopment analysis game cross-efficiency model to calculate the interprovincial current state of energy efficiency. We constructed a spatial Durbin model and used an adjacent space weight matrix, geographic distance weight matrix, and economic distance weight matrix to study the impact of manufacturing agglomeration on total factor energy efficiency. The results showed that under different spatial weights, the interprovincial total factor energy efficiency had a significant spatial dependence; under the three spatial weights, we identified a U-shaped relationship between manufacturing agglomeration and total factor energy efficiency. Industrial agglomeration had obvious spatial spillover effects on total factor energy efficiency, and the spillover effects under the weight of geographic distance were higher than other spatial weights. During the sample period, the direct, indirect, and total effects of the manufacturing industry had an impact on energy efficiency. It all had a negative number indicating that there was a crowding effect in manufacturing agglomeration, and it had an inhibitory effect on energy efficiency.

How building and street morphology affect CO2 emissions: Evidence from a spatially varying relationship analysis in Beijing

Urban morphology plays a critical role in the reduction of CO2 emissions. Despite the growing interest in the relationship between urban form and CO2 emissions in urban sustainable development, few quantitative estimates have been made of the effects of urban morphology considering the combination of three-dimensional (3D) buildings and streets at the neighbourhood scale on CO2 emissions. To explore these effects, this study first proposes a methodological system for computing urban morphology indicators including 3D buildings, streets and their relationships. These morphological indicators quantitatively measure the extension, shape complexity, spatial compactness and irregularity of buildings and streets. Then, the urban morphology indicators and CO2 emissions are calculated for each traffic analysis zone (TAZ) to obtain their characteristics of spatial distribution, using Beijing as the study area. Ultimately, the non-stationary effects of morphological indicators on CO2 are quantified by Multiscale Geographically Weighted Regression (MGWR). The results demonstrate that each morphological factor performs a distinguishable function in different regions. The adjacency ratio, meshedness and width have strongly positive effects on CO2 emissions in the northern part of Beijing within the 6th ring road, while the covered area ratio and the linearity play a crucial negative role, implying that the complexity and sparse layout of buildings and streets are the main drivers to increase CO2 emissions. In the southern part, the length, width STD and height STD pose an apparently positive impact on CO2 emissions, while the openness poses a significant adverse effect, implying that the irregularity and fragment of buildings along streets are the primary reasons for CO2 emissions augmentation. In the densely populated area, the number, floor area ratio, and neighbour distance all exserts a robust positive effect on CO2 emissions. Interestingly, the floor area ratio poses negative effects within the 5th and 6th ring roads, which is a dense industry zone, implying the expansion of buildings can reduce industrial proportion to mitigate CO2 emissions. This study provides insights into CO2 emissions related to micro-scale morphological characteristics and suggests achievable plans for low-carbon physical urban spaces in a spatially varying manner.

Exploring Determinants of HIV/AIDS Self-Testing Uptake in South Africa Using Generalised Linear Poisson and Geographically Weighted Poisson Regression.

Increased HIV/AIDS testing is of paramount importance in controlling the HIV/AIDS pandemic and subsequently saving lives. Despite progress in HIV/AIDS testing programmes, most people are still reluctant to test and thus are still unaware of their status. Understanding the factors associated with uptake levels of HIV/AIDS self-testing requires knowledge of people's perceptions and attitudes, thus informing evidence-based decision making. Using the South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey of 2017 (SABSSM V), this study assessed the efficacy of Generalised Linear Poisson Regression (GLPR) and Geographically Weighted Poisson Regression (GWPR) in modelling the spatial dependence and non-stationary relationships of HIV/AIDS self-testing uptake and covariates. The models were calibrated at the district level across South Africa. Results showed a slightly better performance of GWPR (pseudo R2 = 0.91 and AICc = 390) compared to GLPR (pseudo R2 = 0.88 and AICc = 2552). Estimates of local intercepts derived from GWPR exhibited differences in HIV/AIDS self-testing uptake. Overall, the output of this study displays interesting findings on the levels of spatial heterogeneity of factors associated with HIV/AIDS self-testing uptake across South Africa, which calls for district-specific policies to increase awareness of the need for HIV/AIDS self-testing.

A Bayesian Implementation of the Multiscale Geographically Weighted Regression Model with INLA

The multiscale geographically weighted regression (MGWR) model is an important extension of the classical geographically weighted regression (GWR) model that can be used to explore the spatial nonstationarity of the regression relationship in spatial analysis, but also allows for different scales on conditional relationships between response and different predictors. A Bayesian version of the MGWR model is proposed to obtain estimates of the spatially varying coefficients and the bandwidths simultaneously. The hierarchical form of the Bayesian MGWR model has attractive features, including obtaining posterior estimates of the bandwidths and local parameters simultaneously, and their uncertainty can be easily measured. For Bayesian posterior inference, an efficient algorithm based on integrated nested Laplace approximation is introduced to provide a great alternative of the classical Markov chain Monte Carlo algorithm under the Bayesian framework. The performance of the proposed method is evaluated through simulation study, and it is shown that the proposed approach can correctly identify the differences between scales of parameter surfaces and also obtain precise posterior estimates. Finally, for illustration, this approach is used to analyze monthly housing cost data in the state of Georgia.

Integration of Moran's I, geographically weighted regression (GWR), and ordinary least square (OLS) models in spatiotemporal modeling of COVID-19 outbreak in Qom and Mazandaran Provinces, Iran.

Globally, the COVID-19 pandemic is a top-level public health concern. This paper attempts to identify the COVID-19 pandemic in Qom and Mazandaran provinces, Iran using spatial analysis approaches. This study was based on secondary data of confirmed cases and deaths from February 3, 2020, to late October 2021, in two Qom and Mazandaran provinces from hospitals and the website of the National Institute of Health. In this paper, three geographical models in ArcGIS 10.8.1 were utilized to analyze and evaluate COVID-19, including geographic weight regression (GWR), ordinary least squares (OLS), and spatial autocorrelation (Moran I). The results from this study indicate that the rate of scattering of confirmed cases for Qom province for the period was 44.25%, while the rate of dispersal of the deaths was 4.34%. Based on the GWR and OLS model, Moran's statistics demonstrated that confirmed cases, deaths, and recovered followed a clustering pattern during the study period. Moran's Z-score for all three indicators of confirmed cases, deaths, and recovered was confirmed to be greater than 2.5 (95% confidence level) for both GWR and OLS models. The spatial distribution of indicators of confirmed cases, deaths, and recovered based on the GWR model has been more scattered in the northwestern and southwestern cities of Qom province. Whereas the spatial distribution of the recoveries of the COVID-19 pandemic in Qom province was 61.7%, the central regions of this province had the highest spread of recoveries. The spatial spread of the COVID-19 pandemic from February 3, 2020, to October 2021 in Mazandaran province was 35.57%, of which 2.61% died, according to information published by the COVID-19 pandemic headquarters. Most confirmed cases and deaths are scattered in the north of this province. The ordinary least squares model results showed that the spatial dispersion of recovered people from the COVID-19 pandemic is more significant in the central and southern regions of Mazandaran province. The Z-score for the deaths Index is more significant than 14.314. The results obtained from this study and the information published by the National Headquarters for the fight against the COVID-19 pandemic showed that tourism and pilgrimages are possible factors for the spatial distribution of the COVID-19 pandemic in Qom and Mazandaran provinces. The spatial information obtained from these modeling approaches could provide general insights to authorities and researchers for further targeted investigations and policies in similar circumcises.

Spatial Accessibility of Primary Care in the Dual Public-Private Health System in Rural Areas, Malaysia.

Disparities in access to health services in rural areas represent a global health issue. Various external factors contribute to these disparities and each root requires specific remedial action to alleviate the issue. This study elucidates an approach to assessing the spatial accessibility of primary care, considering Malaysia's dual public-private system specifically in rural areas, and identifies its associated ecological factors. Spatial accessibility was calculated using the Enhance 2-Step Floating Catchment Area (E2SFCA) method, modified as per local context. Data were secondary sourced from Population and Housing Census data and administrative datasets pertaining to health facilities and road network. The spatial pattern of the E2SFCA scores were depicted using Hot spot Analysis. Hierarchical multiple linear regression and geographical weight regression were performed to identify factors that affect E2SFCA scores. Hot spot areas revolved near the urban agglomeration, largely contributed by the private sector. Distance to urban areas, road density, population density dependency ratios and ethnic composition were among the associated factors. Accurate conceptualization and comprehensive assessment of accessibility are crucial for evidence-based decision making by the policymakers and health authorities in identifying areas that need attention for a more specific and localized planning and development.

Optimization Fuzzy Geographically Weighted Clustering with Gravitational Search Algorithm for Factors Analysis Associated with Stunting

Stunting is a significant threat to the quality of human resources in Indonesia because stunting does not only involve physical growth disorders but can also cause children to be vulnerable to disease and experience disorders of brain development and intelligence. Many factors cause stunting, not only malnutrition in pregnant women and toddlers. Grouping can be done to make it easier to see the characteristics of the factors causing stunting in Indonesia. The grouping is done based on the similarity of the characteristics of the factors causing stunting in each province. This study used Fuzzy Geographically Weighted Clustering (FGWC) with Gravitational Search Algorithm (GSA) to group and assess the best cluster using the Partition Coefficient validity index, Classification Entropy, Separation Index, Xie & Beni's Index, and IFV Index. Furthermore, a difference test was conducted to determine the dominant factor causing stunting in the formed cluster. The results showed that the FGWC-GSA gave the best clustering results on the fuzziness value of 2 with the number of clusters 2. Cluster 1 consisted of 16 provinces, and cluster 2 consisted of 18 provinces. Based on the T-test, the variables of infants who received exclusive breastfeeding had significant differences between clusters. Therefore, cluster 2 is a cluster that has dominant problems related to exclusive breastfeeding.

The Impact of Urban Facilities on Crime during the Pre- and Pandemic Periods: A Practical Study in Beijing.

The measures in the fight against COVID-19 have reshaped the functions of urban facilities, which might cause the associated crimes to vary with the occurrence of the pandemic. This paper aimed to study this phenomenon by conducting quantitative research. By treating the area under the jurisdiction of the police station (AJPS) as spatial units, the residential burglary and non-motor vehicle theft that occurred during the first-level response to the public health emergencies (pandemic) period in 2020 and the corresponding temporal window (pre-pandemic) in 2019 were collected and a practical study to Beijing was made. The impact of urban facilities on crimes during both periods was analyzed independently by using negative binomial regression (NBR) and geographical weight regression (GWR). The findings demonstrated that during the pandemic period, a reduction in the count and spatial concentration of both property crimes were observed, and the impact of facilities on crime changed. Some facilities lost their impact on crime during the pandemic period, while other facilities played a significant role in generating crime. Additionally, the variables that always kept a stable significant impact on crime during the pre- and pandemic periods demonstrated a heterogeneous impact in space and experienced some variations across the periods. The study proved that the strategies in the fight against COVID-19 changed the impact of urban facilities on crime occurrence, which deeply reshaped the crime patterns.

Spatial Heterogeneity and Influence Factors of Traditional Villages in the Wuling Mountain Area, Hunan Province, China Based on Multiscale Geographically Weighted Regression

Traditional villages are the spatial carriers of the excellent traditional culture of the Chinese nation, the concentrated expression of thousands of years of farming civilization, and a nonrenewable precious cultural heritage. However, in recent decades, with the rapid development of urbanization and modernization, traditional villages have rapidly declined or even disappeared. Therefore, exploring the spatial distribution characteristics and influencing factors of traditional villages and clarifying their formation and development laws, can provide a new perspective for the continuity protection of traditional villages on a cross-regional scale. This study takes six batches of 462 traditional villages in the Wuling Mountains, Hunan, China as the research object. On the basis of the analysis of the spatial distribution characteristics of overall and different ethnic minority traditional villages, this research proposes the idea of simulating the spatial distribution mechanism of traditional villages by using the multiscale geographical weighted regression (MGWR) model. Results indicate that (1) the spatial distribution of the traditional villages of the overall and different ethnic minorities shows significant agglomeration characteristics (among them, traditional Miao and Tujia villages have formed the main and sub cores of the space gathering of traditional villages in the Wuling Mountains in Hunan); and (2) compared with the least squares (OLS) method and the classic geographical (GWR) weighted regression model, the MGWR model can reflect the impact scales of different factors, and the simulation effect is better. From the perspective of influence factors, natural factors are the basis of affecting the distribution of traditional villages in the Wuling Mountain area and are generally positively correlated. Socioeconomic factors have a dual influence on the spatial distribution of traditional villages and generally have a negative correlation. Cultural factors are the key to the spatial distribution of traditional villages and are generally positively correlated. Spatial factors have no significant impact on the spatial distribution of traditional villages. On the basis of these conclusions, this study discusses the relevant strategies for the protection and development of traditional villages.

Assessment of Influence Mechanisms of Built Environment on Street Vitality Using Multisource Spatial Data: A Case Study in Qingdao, China

Street vitality is a significant indicator of a city’s capacity for sustainable development. Significant progress has been made on the basis of measurements of a single indicator of street vitality, but few studies have used multisource data to measure street vitality in a comprehensive way. In this study, in order to explore the multidimensional vitality characteristics of streets, streets were taken as the analysis unit, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) evaluation model with combined weights was used to identify the spatial pattern of streets vitality from social, economic, and cultural dimensions using multisource spatial data such as Baidu heat map, Meituan store rating, and cultural facilities points of interest in the main urban area of Qingdao City, China. Using a Multiscale Geographically Weighted Regression (MGWR) model, the spatial correlations and differences between street built environment components and multidimensional street vitality were examined, to reveal the influence mechanism of street vitality creation in each street. The study found that the comprehensive vitality of the streets in the main urban area of Qingdao City exhibits the spatial differentiation features of “weak east–west, strong central, multicenter, cluster type”. Furthermore, although commercial and public services are essential for enhancing street vitality and attracting crowds, a very high degree of functional mix has not resulted in a high degree of street vitality. Lastly, high spatial heterogeneity between built environment factors and street vitality necessitates considering the functional positioning and development basis of the street, tailoring to local conditions and policies, considering the street’s vitality development status and development needs, complementing strengths, promoting coordinated development, and releasing and enhancing the street’s vitality. Therefore, it is essential to explore street vitality and its influencing mechanisms to improve people’s quality of life and promote sustainable urban development.

Modeling of COVID-19 Cases in Indonesia with the Method of Geographically Weighted Regression

The COVID-19 pandemic has spread to all corners of the world, including Indonesia. Various factors affect the spread of COVID-19 cases in an area so that the government and the community can make prevention and control efforts so that this pandemic does not spread. This study aims to model the number of COVID-19 cases in Indonesia using the Geographically Weighted Regression (GWR) method, which develops a linear regression model. The GWR model uses weights based on the location of each observation so that the model is obtained for that location. Determine the weighting on the bandwidth. Optimum bandwidth selection is obtained by minimizing the value of Cross-Validation (CV). The GWR model using a fixed bisquare kernel weighting function has an optimum bandwidth of 0.999948 with a minimum CV value of 397.076.128 with a coefficient of determination R2 of 85.1 %. The results show that the number of positive cases positively correlates with the number of patients who died from COVID-19. In contrast, the number of recovered patients negatively correlates with the number of patients who died from COVID-19.

Determination of the best knot and bandwidth in geographically weighted truncated spline nonparametric regression using generalized cross validation

This study proposes the development of nonparametric regression for data containing spatial heterogeneity with local parameter estimates for each observation location. GWTSNR combines Truncated Spline Nonparametric Regression (TSNR) and Geographically Weighted Regression (GWR). So it is necessary to determine the optimum knot point from TSNR and determine the best geographic weighting (bandwidth) from GWR by deciding the best knot point and bandwidth using Generalized Cross Validation (GCV). The case study analyzed the Morbidity Rate in North Sumatra in 2020. This study will estimate the model using knot points 1, 2, and 3 and geographic weighting of the Kernel Function, Gaussian, Bisquare, Tricube, and Exponential. Based on data analysis, we obtained that the best model for Morbidity Rate data in North Sumatra 2020 based on the minimum GCV value is the model using knots 1 and the Kernel Function of Bisquare. Based on the GWTSNR model, the significant predictors in each district/city were grouped into eight groups. Furthermore, the GWTSNR is better at modeling morbidity rates in North Sumatra 2020 by obtaining adjusted R-square = 96.235 than the TSNR by obtaining adjusted R-squared = 70.159. Some of the highlights of the proposed approach are:•The method combines nonparametric and spatial regression in determining morbidity rate modeling.•There were three-knot points tested in the truncated spline nonparametric regression and four geographic weightings in the spatial regression and then to determine the best knot and bandwidth using Generalized Cross Validation.•This paper will determine regional groupings in North Sumatra 2020 based on significant predictors in modeling morbidity rates.

The effect of sanitation risk on toddler stunting incident with geographically weighted regression approach in Malang Regency, Indonesia

Stunting is a fundamental problem in Indonesia's human development. The risk factors for stunting are very complex, including inadequate environmental sanitation. This study aims to analyze and map the influence of spatially correlated sanitation risk on the incidence of stunting toddlers in Malang Regency, Indonesia. Data collection was carried out by interviewing respondents using questionnaires, counting stunting toddlers, and observing environmental sanitation objects. Determination of respondents was done purposively with the following stages. First, at the village level, 75 sites were selected based on the following criteria: population density; poverty level; the presence of streams, drainage or irrigation canals; and flood vulnerability. Furthermore, in each village, 40 respondents were selected based on the following criteria: women aged between 18 to 65 years, came from poor families and had a child (or children). When the number of respondents who were willing to participate is less than 40 people in each village, they were replaced by other respondents based on recommendations from village officials. A total of 3000 respondents have participated in this study. The selected respondents include wives, dependent married women or widows. The spatial analysis approach was carried out using the Geographically Weighted Regression (GWR) method. Based on the results of the analysis of the spatial effect of the GWR model, the Sanitation Risk Factors that significantly affected the incidence of stunting in Malang Regency were; Environmental Drainage (greywater), Solid Waste, and Domestic Wastewater (black water). Clean and Healthy Lifestyles and Water Sources have less effect on sanitation risk. This study recommends government and public interventions to improve environmental sanitation including integrated waste management, improvement of drainage infrastructure, construction of standardized toilets, and carrying out health promotion efforts with education to increase public awareness.

Regional PM2.5 Estimation for Southern Ontario through Geographically Weighted Regression

In this study, a geographically weighted regression (GWR) approach was adopted to forecast regional concentration of particulate matter 2.5 (PM2.5) for the southern Ontario based on both in situ meteorological measurement and Satellite retrievals of aerosol optical depth (AOD). The correlation between monitored concentration of PM2.5 and Satellite-retrieved AOD would be quantified. The ground-level PM2.5 for South Ontario area was then predicted using GWR with AOD and meteorological variables considered as inputs. The results indicated that performance of GWR was slightly better than the ordinary least squares (OLS) model, indicating spatial variations between independent and dependent variables. Consequently, the GWR model can help us to predict the PM2.5 concentration in terms of time or region with satellite data, and also help improve satellite data inversion.

Geographically weighted generalized poisson regression model with the best kernel function in the case of the number of postpartum maternal mortality in east java

This study proposes a Geographically Weighted Generalized Poisson Regression (GWGPR) model with the best kernel function to obtain a model of the number of postpartum maternal mortality in East Java Province in 2020 and determine the factors that affect the number of maternal postpartum mortality in East Java in 2020. The kernel functions used in this study are fixed bisquare kernel, fixed tricube kernel, and adaptive bisquare kernel. Optimum bandwidth selection using the Cross-Validation (CV) method. The results obtained the best model is the GWGPR model with a fixed bisquare kernel because it produces the smallest AIC value of 194.92. Variables significantly affecting the number of maternal postpartum mortality in East Java in 2020 vary in each district/city where there are three regional groups. The percentage of pregnant women who had a pregnancy visit K1, the percentage of pregnant women who had a pregnancy visit K4, the percentage of households receiving cash assistance, and the ratio of hospitals and health centres have a significant effect on Kabupaten Blitar, Mojokerto, Gresik, Bangkalan, Blitar City, Mojokerto City, Surabaya City. While the five predictor variables together significantly affect districts/cities included in group 3, such as Ponorogo, Trenggalek, Tulungagung, Kediri, Malang, Lumajang, Jember, Banyuwangi, Bondowoso and so on. Some of the highlights of the proposed approach are:•Generalized Poisson regression model using the maximum likelihood estimation (MLE) method.•The kernel functions used in the Geographically Weighted Generalized Poisson Regression (GWGPR) model to determine bandwidth are fixed bisquare, fixed tricube, and adaptive bisquare kernel functions selected using the Cross Validation (CV) method.•The computation procedure is easy to implement.

Spatio-Temporal Spread of Covid-19 in Children Aged 0-11 Years at Central Java Province Indonesia Using Geographical Weight Regression

During the COVID-19 pandemic, children under the age of 12 are the most vulnerable age group to health concerns. The goal of this study was to conduct a spatiotemporal analysis of the distribution of COVID-19 cases in Central Java children using the GWR (Geographically Weighted Regression) approach. The data source is the Central Java Provincial Health Office, and the study objects are 35 cities and districts in Central Java province. The data obtained are the number of COVID-19 cases in children aged 0-11 years, the total number of Covid-19 cases, the number of PCR tests per day, the number of vaccinations and the number of health care facilities per city and district per month from March 2020 to November 2021. Hotspot analysis and the GWR approach were used to examine data in semesters 1–4 (S1–S4). From S1 to S4, the number of COVID-19 cases in children increased. Areas that became hotspots for more than two semesters were Semarang City, Semarang Regency, Banyumas, Cilacap, Kendal, and Demak. According to the GWR analysis in S1-S4, the total number of COVID-19 cases, PCR tests per day, vaccinations, and health care facilities all affect the number of COVID-19 patients in children by more than 75%. The total number of COVID-19 cases has a significant impact on the number of COVID-19 cases in children but the number of health care facilities has no effect. The results of the GWR prediction of COVID-19 cases in children show that the cities of Semarang and Banyumas became areas with a larger number of COVID-19 cases in two semesters. According to the hotspot and GWR analysis, the cities of Semarang and Banyumas are regions to be on the lookout for in the spread of COVID-19 cases in S1-S4.

Research on the Spatial Effect of Government Science and Technology Expenditure on the Development of Digital Economy

In the context of a unified large market, the development of digital economy has become the focus. Based on the panel data of 31 provinces in China from 2011 to 2020, this paper uses the spatial Durbin model to examine the impact of government spending on digital economy through different spatial weight matrices. The research results show that: 1) The government science and technology expenditure in all provinces in China has a significant positive spatial correlation with the level of digital economy development; 2) The comparative analysis of different weight matrices show that the impact of local government science and technology expenditure on the level of digital economy development is significant and positive, and there is a certain spatial spillover effect; 3) Through the heterogeneity test, the spillover effect of the central and western regions of China is more obvious under the setting of the geographical weight matrix, and the spillover effect of the east is more obvious under the setting of the economic matrix weight. At the same time, this study expands the research on the influencing factors of the development level of the digital economy, and puts forward policy recommendations corresponding to government science and technology expenditure and the development of the digital economy based on the empirical results.

Modelling Accessibility to Primary Healthcare Facilities in Argungu LGA: Using Multiscale Geographically Weighted Regression (MSGWR) Approach

This study uses socioeconomic and demographic data to demonstrate the value of a novel multidimensional approach to healthcare accessibility. The optimum location for healthcare facilities in relation to demand areas was determined using location-allocation models and local multiscale geographically weighted regression (MGWR) to explore spatially non-stationary relationships. The result shows that the potential accessibility of a community to primary healthcare depends on the geographic and socioeconomic characteristics of various places. The results of this study may be used to inform policy planning and decision-making for increasing accessibility to healthcare services, particularly in rural areas for achieving the Sustainable Development Goals (SDGs).

Modelling Accessibility to Primary Healthcare Facilities in Argungu LGA: Using Multiscale Geographically Weighted Regression (MSGWR) Approach

Modelling Accessibility to Primary Healthcare Facilities in Argungu LGA: Using Multiscale Geographically Weighted Regression (MSGWR) Approach

Spatial Pattern of the Determinants for the Private Housing Rental Prices in Highly Dense Populated Chinese Cities—Case of Chongqing

The private housing rental market has rapidly developed and demonstrated its outstanding contribution to improving affordability for the floating population in China. However, the forming pattern of private housing rental prices (PHRP) remains poorly understood in China’s highly dense populated cities. This study aims to comprehensively investigate the determinants of PHRP and depict their spatial pattern, considering the diverse functions of different areas within the city. A theoretical framework of the factors that influence PHRP has been developed based on an extensive literate study. Taking Chongqing city as a case, a Multiscale Geographically Weighted Regression (MGWR) analysis based on data from Lianjia.com and 58.com was conducted to investigate the spatial pattern of those influencing factors. The PHRP in Chongqing were mainly shaped by the factors of traffic condition and the neighborhood environment. The main findings highlighted that the influence of traffic condition on rental prices is more dominating in the industrial and financial zones, and the neighborhood factors represent spatial heterogeneity in the educational and commercial zones. This study provides a comprehensive examination of the spatial pattern of PHRP’s determinants in highly dense populated Chinese cities, extending the understanding of factors influencing housing rental prices. Practically, it provides scientific and reliable recommendations for the local governments and housing agencies in developing housing properties that consider the needs of the floating population. Moreover, tenants in highly dense populated cities benefit from suggestions about looking for proper accommodation with high value and accessibility in different functional zones of the city.