Spatial Lag Model Research Articles

Exploring demographic, healthcare, and socio-economic factors as predictors of COVID-19 incidence rate: A spatial regression analysis.

This study investigated the relationship between demographic, healthcare, and socio-economic factors, and COVID-19 incidence rate per 100,000 population in Thailand at the province level between January 2020 and March 2022, using a five-phase approach by spatial analysis. OLS models were initially used with significant variables: household, hospital, and industry density, nighttime light index (NTLI). Spatial dependency led to spatial error (SEM) and spatial lag models (SLM), performing better with similar significant variables being applied. SEM explains 58, 65 and, 70 percent in Wave 1, 4 and 5 of COVID-19 variation. SLM explains 25 and 76 percent in Wave 2 and 3 of incidence rate. Positive associations were found between incidence and household density, hospital/medical establishments with beds, Nighttime Light Index (NTLI), and negative with population, hospital, and industry density. Wave 5 showed significant changes with negative for household, hospital, and industry density, urban population; positive for hospital/medical establishments with beds, internet access, NTLI. The study showed that significant predictors of COVID-19 incidence rate vary across waves. Population, household and hospital density, urbanization, access to medical facilities, industrialization, internet access, and NTLI all play a role. The study suggests SEM and SLM models are more appropriate, providing useful information for policymakers and health officials in managing pandemic in Thailand.

Investigating the Heterogeneity Effects of Urban Morphology on Building Energy Consumption from a Spatio-Temporal Perspective Using Old Residential Buildings on a University Campus

The significant increase in building energy consumption poses a major challenge to environmental sustainability. In this process, urban morphology plays a pivotal role in shaping building energy consumption. However, its impact may exhibit latent heterogeneity due to differences in temporal resolution and spatial scales. For urban energy planning and energy consumption modeling, it is crucial to pinpoint when and where urban morphology parameters matter, an overlooked aspect in prior research. This study quantitatively explores this heterogeneity, utilizing a detailed dataset from old residential buildings within a university campus. Spatial lag models were employed for cross-modeling across various temporal and spatial dimensions. The results show that annual and seasonal spatial regression models perform best within a 150 m buffer zone. However, not all significant indicators fall within this range, suggesting that blindly applying the same range to all indicators may lead to inaccurate conclusions. Moreover, significant urban morphology indicators vary in quantity, category, and directionality. The green space ratio exhibits correlations with energy consumption in annual, summer, and winter periods within buffer zones of 150 m, 50~100 m, and 100 m, respectively. It notably displays a negative correlation with annual energy consumption but a positive correlation with winter energy consumption. To address this heterogeneity, this study proposes a three-tiered framework—macro-level project decomposition, establishing a key indicator library, and energy consumption comparisons, facilitating more targeted urban energy model and energy management decisions.

Spatial heterogeneity in unintended pregnancy and its determinants in India

BackgroundUnderstanding the geographic variation of unintended pregnancy is crucial for informing tailored policies and programs to improve maternal and child health outcomes. Although spatial analyses of unintended pregnancy have been conducted in several developing countries, such research is lacking in India. This study addresses this gap by investigating the geographic distribution and determinants of unintended pregnancy in India.MethodsWe analysed data from the National Family Health Survey-5 encompassing 232,920 pregnancies occurring between 2014 and 2021 in India. We conducted a spatial analysis to investigate the distribution of unintended pregnancies at both state and district levels using choropleth maps. To assess spatial autocorrelation, Global Moran’s I statistic was employed. Cluster and outlier analysis techniques were then utilized to identify significant clusters of unintended pregnancies across India. Furthermore, we employed Spatial Lag Model (SLM) and Spatial Error Model (SEM) to investigate the factors influencing the occurrence of unintended pregnancies within districts.ResultsThe national rate of unintended pregnancy in India is approximately 9.1%, but this rate varies significantly between different states and districts of India. The rate exceeded 10% in the states situated in the northern plain such as Haryana, Delhi, Uttar Pradesh, Bihar, and West Bengal, as well as in the Himalayan states of Himachal Pradesh, Uttarakhand, Sikkim, and Arunachal Pradesh. Moreover, within these states, numerous districts reported rates exceeding 15%. The results of Global Moran’s I indicated a statistically significant geographical clustering of unintended pregnancy rates at the district level, with a coefficient of 0.47 (p < 0.01). Cluster and outlier analysis further identified three major high-high clusters, predominantly located in the districts of Arunachal Pradesh, northern West Bengal, Bihar, western Uttar Pradesh, Haryana, Delhi, alongside a few smaller clusters in Odisha, Madhya Pradesh, Uttarakhand, and Himachal Pradesh. This geographic clustering of unintended pregnancy may be attributed to factors such as unmet needs for family planning, preferences for smaller family sizes, or the desire for male children. Results from the SEM underscored that parity and use of modern contraceptive were statistically significant predictors of unintended pregnancy at the district level.ConclusionOur analysis of comprehensive, nationally representative data from NFHS-5 in India reveals significant geographical disparities in unintended pregnancies, evident at both state and district levels. These findings underscore the critical importance of targeted policy interventions, particularly in geographical hotspots, to effectively reduce unintended pregnancy rates and can contribute significantly to improving reproductive health outcomes across the country.

Strengths and weaknesses of EU regional reactivity to shocks

AbstractThis study introduces the novel concept of regional reactivity to shocks. A region is considered to be much more reactive if it bounced back to the level of labour productivity achieved before a shock in the same or less time than it took to reach the pre-2008 economic crisis peak from an equivalent lower bound. The analysis of the reactivity of the EU-NUTS2 regions reveals a clear spatial pattern. By using a spatial lag model selected via a Bayesian comparison approach, we show that tertiary education and institutional quality are key to promote reactivity. On the other hand, population density acts in the opposite direction. Our results are potentially useful for defining policy strategies that emphasise or refocus the strengths of each region in light of current territorial trends and emerging challenges.

Cross-Country Assessment of Socio-Ecological Drivers of COVID-19 Dynamics in Africa: A Spatial Modelling Approach

Understanding how countries’ socio-economic, environmental, health status, and climate factors have influenced the dynamics of COVID-19 is essential for public health, particularly in Africa. This study explored the relationships between African countries’ COVID-19 cases and deaths and their socio-economic, environmental, health, clinical, and climate variables. It compared the performance of Ordinary Least Square (OLS) regression, the spatial lag model (SLM), the spatial error model (SEM), and the conditional autoregressive model (CAR) using statistics such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and coefficient of determination (R2). Results showed that the SEM with the 10-nearest neighbours matrix weights performed better for the number of cases, while the SEM with the maximum distance matrix weights performed better for the number of deaths. For the cases, the number of tests followed by the adjusted savings, Gross Domestic Product (GDP) per capita, dependence ratio, and annual temperature were the strongest covariates. For deaths, the number of tests followed by malaria prevalence, prevalence of communicable diseases, adjusted savings, GDP, dependence ratio, Human Immunodeficiency Virus (HIV) prevalence, and moisture index of the moistest quarter play a critical role in explaining disparities across countries. This study illustrates the importance of accounting for spatial autocorrelation in modelling the dynamics of the disease while highlighting the role of countries’ specific factors in driving its dynamics.

Waiting times spillovers in a National Health Service hospital network: a little organizational diversity can go a long way

BackgroundThe objective of this study is to assess if waiting times for elective surgeries within the Portuguese National Health Service (NHS) are influenced by the waiting times at neighboring hospitals. Recognizing these interdependencies, and their extent, is crucial for understanding how hospital network dynamics affect healthcare delivery efficiency and patient access.MethodsWe utilized patient-level data from all elective surgeries conducted in Portuguese NHS hospitals to estimate a hospital-specific index for waiting times. This index served as the dependent variable in our analysis. We applied a spatial lag model to examine the potential strategic interactions between hospitals concerning their waiting times.ResultsOur analysis revealed a significant positive endogenous spatial dependence, indicating that waiting times in NHS hospitals are strategic complements. Furthermore, we found that NHS contracts with private not-for-profit hospitals not only reduce waiting times within these hospitals but also exert positive spillover effects on other NHS hospitals.ConclusionsThe findings suggest that diversifying the organization of the NHS hospital network, particularly through contracts with private entities for marginal patients, can significantly enhance competitive dynamics and reduce waiting times. This effect persists even when patient choice is confined to a small fraction of the patient population, highlighting a strategic avenue for policy optimization in healthcare service delivery.

Spatio-temporal effects and influence mechanism of digital technology on tourism efficiency in Chinese provinces

Digital technology serves as a new industrial driving force, playing a crucial role in promoting the efficiency of regional tourism. To explore the inherent logic between digital technology and tourism development, the study employed the DEA-BCC model and the entropy-weighted TOPSIS method to measure the tourism efficiency and digital technology development level of 31 provinces (municipalities and districts) in China from 2011 to 2022. The evolutionary characteristics of the relationship between digital technology and tourism efficiency were analyzed from the spatio-temporal dimension, and empirical tests were carried out using the spatial error model (SEM) and the spatial lag model (SLM) to explore the mechanism of the impact of digital technology on tourism efficiency. The results show that: during the study period, the overall trend of digital technology development and tourism efficiency is upward, but there is a certain degree of spatial mismatch between the two; digital technology is not only conducive to the improvement of tourism efficiency in the province, but also acts on neighboring provinces through spatial spillover effects, mechanism of action tests show that digital technology can positively moderate the effects of tourism economic growth and tourism industry structure on tourism efficiency; in western and northeast China, the positive effect of digital technology on tourism efficiency is more obvious. The conclusions provide a new perspective for understanding and analyzing the development of provincial tourism in China, as well as a reference for the rational use of digital technology to promote tourism development.

Estimating the impact of cycling infrastructure improvements on usage: A spatial difference-in-differences approach

As a sustainable mode with numerous benefits, cycling offers promoting physical fitness and providing a cost-effective transport option. To promote cycling, cities worldwide are striving to increase cycling usage by improving infrastructure, and network connectivity. Understanding the magnitude of the influence of cycling infrastructure on cycling usage is vital. This paper hence aims to estimate the impact of cycling infrastructure improvement on cycling usage. To this end, a generalised spatial difference-in-differences (GS-DiD) fixed effect spatial lag model is utilised using data collected from bicycle counters in Lyon and Paris. The model allows controlling spatial and temporal unobserved heterogeneity. The results indicate that cycling infrastructure improvement has a positive and statistically significant effect on cycling usage. Additionally, the spatial coefficient was found to be negative and statistically significant, suggesting that an increase in cycling infrastructure usage in neighbouring areas leads to a decrease in infrastructure usage in the area being examined. These findings provide evidence of the positive impact of cycling infrastructure improvement on cycling usage, while also highlighting the importance of considering spatial spillover effects in the analysis.

Exploring the influencing factors of scrub typhus in Gannan region, China, based on spatial regression modelling and geographical detector

Scrub typhus is a significant public health issue with a wide distribution and is influenced by various determinants. However, in order to effectively eradicate scrub typhus, it is crucial to identify the specific factors that contribute to its incidence at a detailed level. Therefore, the objective of our study is to identify these influencing factors, examine the spatial variations in incidence, and analyze the interplay of two factors on scrub typhus incidence, so as to provide valuable experience for the prevention and treatment of scrub typhus in Gannan and to alleviate the economic burden of the local population.This study employed spatial autocorrelation analyses to examine the dependent variable and ordinary least squares model residuals. Additionally, spatial regression modelling and geographical detector were used to analyze the factors influencing the annual mean 14-year incidence of scrub typhus in the streets/townships of Gannan region from 2008 to 2021. The results of spatial1 autocorrelation analyses indicated the presence of spatial correlation. Among the global spatial regression models, the spatial lag model was found to be the best fitting model (log likelihood ratio = −319.3029, AIC = 666.6059). The results from the SLM analysis indicated that DEM, mean temperature, and mean wind speed were the primary factors influencing the occurrence of scrub typhus. For the local spatial regression models, the multiscale geographically weighted regression was determined to be the best fitting model (adjusted R2 = 0.443, AICc = 726.489). Further analysis using the MGWR model revealed that DEM had a greater impact in Xinfeng and Longnan, while the southern region was found to be more susceptible to scrub typhus due to mean wind speed. The geographical detector results revealed that the incidence of scrub typhus was primarily influenced by annual average normalized difference vegetation index. Additionally, the interaction between GDP and the percentage of grassland area had a significant impact on the incidence of scrub typhus (q = 0.357). This study illustrated the individual and interactive effects of natural environmental factors and socio-economic factors on the incidence of scrub typhus; and elucidated the specific factors affecting the incidence of scrub typhus in various streets/townships. The findings of this study can be used to develop effective interventions for the prevention and control of scrub typhus.

Application of Semi-parametric Spatial Lag Model in the Study of the Relationship Between Innovation Efficiency and Industrial Development in China’s Pharmaceutical Manufacturing Industry

Application of Semi-parametric Spatial Lag Model in the Study of the Relationship Between Innovation Efficiency and Industrial Development in China’s Pharmaceutical Manufacturing Industry

Does supply match demand? Assessing the relationship between urban parks and residents from the perspective of equity and efficiency

The supply-demand relationship between urban parks and residents is a hot topic for promoting sustainable and efficient cities. Most studies have focused on the assessment of spatial equity, while ignoring the evaluation of park use efficiency. Taking Nanjing as an example, we developed specific indicators, namely, the supply intensity of urban park (UPSI), per capita park area (PCPA), usage ratio (UR), and demand satisfaction ratio (DSR), to measure equity and efficiency on the basis mobile phone signalling data. The results show that the service areas of urban parks cover only approximately 4/5 of the urban residences, and that the PCPAs of urban residences were divergent. In addition, most urban parks had unsuitable URs, and the DSRs indicated that most residents’ demand could not be satisfied by the surrounding parks. A spatial lag model (SLM) was introduced to explore the factors influencing residents’ demand. In combination with the literature, we concluded that the disparity between urban park supply and resident demand resulted from the spatial distribution of physical geographic factors, changes in urban planning and development strategies, and the diverse and dynamic demands of urban residents. On the basis of the above results, we further made recommendations for the management and planning of urban parks.

Spatial Analysis of Health System Factors in Infectious Disease Management: Lessons Learned from the COVID-19 Pandemic in Korea.

Infectious disease outbreaks present ongoing and substantial challenges to health systems at local, national, and global levels, testing their preparedness, response capabilities, and resilience. This study aimed to identify and analyze critical health system-level factors that influence infection outbreaks, focusing on the experience of the COVID-19 pandemic in Korea. Conducted as a secondary data analysis, this study utilized national datasets from Korea. Given the inherent spatial dependencies in the spread of infectious diseases, we employed a spatial lag model to analyze data. While city-specific characteristics did not emerge as significant factors, health system variables, particularly the number of community health centers and health budgets, showed significant influence on the course of the COVID-19 outbreak, along with spatial autocorrelation coefficients. Our findings underscore the importance of enhancing public healthcare infrastructure, considering regional specificities, and promoting collaboration among local governments to bolster preparedness for future outbreaks. These insights are crucial for policymakers and healthcare professionals in formulating effective strategies to prevent, manage, and mitigate the impact of infectious disease outbreaks.

A fine-grained investigation on the predictors of urban green space growth in Nanjing

ABSTRACT Existing studies have primarily examined urban green space (UGS) evolution in isolation from the urban development context, neglecting the dynamicity of the factors affecting UGS growth in different spatiotemporal conditions and urban development levels. Taking Nanjing as the study site, we constructed spatial lag models using multi-source data and investigated the patterns and predictors of UGS growth across diverse spatiotemporal conditions in high-density environments. The model results show that: (1) although good natural conditions can support UGS development, this effect is greatly weakened in fast urban expansion as other land uses show higher priority in land occupation; (2) industrial and public service lands and commercial lands show a negative effect on UGS growth in the early and late urbanization, respectively, while residential lands demonstrated sustained negative effect in all the urbanization phases; (3) the effect of the total population and population structure varies in different urban development phases; (4) at an advanced urban development level, the areas close to city centre have better opportunities to install new UGSs. These findings offer useful references to the decision makers and planners, especially for their work on the UGS layout optimization within the high-density areas.

Spatial prediction of poverty in Gauteng province (South Africa) in‐between Censuses using land use datasets

AbstractTo realize the first sustainable development goal of ending “poverty in all its forms everywhere,” local governments in South Africa need to implement informed targeted policy interventions based on up‐to‐date data and sound analytics. Statistics South Africa (Stats SA) Censuses reveal the socioeconomic circumstances of people living in South Africa but are only conducted every 10 years. As a result, most analytical studies done in‐between Censuses rely on outdated socioeconomic data. This study demonstrates how poverty levels in one of the provinces of South Africa, Gauteng, can be predicted when up‐to‐date Census datasets are not available. The spatial lag model is used to explain the relationship between the South African Multidimensional Poverty Index (SAMPI) and statistically significant variables extracted from land use datasets (i.e., land areas classified as built‐up, informal, residential, township, and non‐urban), and to ultimately predict the levels of poverty. Out‐of‐sample predicted poverty levels obtained based on the spatial lag model correlate with the actual levels of poverty thereby reflecting known spatial patterns of the levels of poverty in Gauteng province.

Bayesian Estimation of the Semiparametric Spatial Lag Model

This paper proposes a semiparametric spatial lag model and develops a Bayesian estimation method for this model. In the estimation of the model, the paper combines Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, random walk Metropolis sampler, and Gibbs sampling techniques to sample all the parameters. The paper conducts numerical simulations to validate the proposed Bayesian estimation theory using a numerical example. The simulation results demonstrate satisfactory estimation performance of the parameter part and the fitting performance of the nonparametric function under different spatial weight matrix settings. Furthermore, the paper applies the constructed model and its estimation method to an empirical study on the relationship between economic growth and carbon emissions in China, illustrating the practical application value of the theoretical results.

Using Nighttime Light Data to Explore the Extent of Power Outages in the Florida Panhandle after 2018 Hurricane Michael

The destructive forces of tropical cyclones can have significant impacts on the land, contributing to degradation through various mechanisms such as erosion, debris, loss of vegetation, and widespread damage to infrastructure. Storm surge and flooding can wash away buildings and other structures, deposit debris and sediments, and contaminate freshwater resources, making them unsuitable for both human use and agriculture. High winds and flooding often damage electrical disubstations and transformers, leading to disruptions in electricity supply. Restoration can take days or even weeks, depending on the extent of the damage and the resources available. In the meantime, communities affected by power outages may experience difficulties accessing essential services and maintaining communication. In this study, we used a weighted maximum likelihood classification algorithm to reclassify NOAA’s National Geodetic Survey Emergency Response Imagery scenes into debris, sand, water, trees, and roofs to assess the extent of the damage around Mexico Beach, Florida, following the 2018 Hurricane Michael. NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) was processed to estimate power outage duration and rate of restoration in the Florida Panhandle based on the 7-day moving averages. Percent loss of electrical service at a neighborhood level was estimated using the 2013–2017 American Community Survey block group data. Spatial lag models were employed to examine the association between restoration rates and socioeconomic factors. The analysis revealed notable differences in power-restoration rates between urbanized and rural areas and between disadvantaged and more affluent communities. The findings indicated that block groups with higher proportions of minorities, multi-family housing units, rural locations, and households receiving public assistance experienced slower restoration of power compared to urban and more affluent neighborhoods. These results underscore the importance of integrating socioeconomic factors into disaster preparedness and recovery-planning efforts, emphasizing the need for targeted interventions to mitigate disparities in recovery times following natural disasters.

Spatial Distribution of Smear-Positive Pulmonary Tuberculosis Incidence Rates in Iran: A Registry-Based Study (2018-2022).

Smear-positive pulmonary tuberculosis (SPPTB) is a significant public health concern in Iran. This registry-based study aimed to investigate the incidence rates of SPPTB in Iran from 2018 to 2022. The study analyzed SPPTB cases using the Spatial Lag Model to investigate the spatial distribution of SPPTB incidence rates, income inequality, and delayed diagnosis across the provinces of Iran and mapped the results using GIS maps. The study found that SPPTB is prevalent among older individuals and males. The analysis identified significant spatial variation in the distribution of SPPTB incidence rates, income inequality, and delayed diagnosis across the provinces. The highest incidence rate of SPPTB was found in Sistan and Baluchestan Province and Golestan provinces. The study found a positive association between income inequalities, measured by the Gini index, and SPPTB incidence rates, indicating that provinces with higher income inequality may have higher incidence rates of SPPTB. The negative correlation with delayed diagnosis implies that predominantly, SPPTB cases are identified in the early months. The study highlights the need to address socioeconomic disparities in health outcomes and implement targeted interventions in areas with higher income inequality to reduce the burden of SPPTB in Iran. Despite decreased SPPTB incidence rates in Iran over the past decade, some regions, such as Sistan and Baluchestan, still have high incidence rates. The Iranian government has implemented policies and programs to reduce income inequality and delayed diagnosis within the country, which can contribute to reducing the burden of SPPTB.

Efectos contagio del Covid-19 a nivel espacial entre municipios

This article quantifies the spatial diffusion capacity –spatial spillover effect– of the COVID-19 virus at municipal level for the department of Antioquia, Colombia, from the onset of its spread in 2020 until December 2021. To this end, error and spatial lag models were implemented to identify the pattern of infections and classify municipalities in terms of their diffusion capacity. The empirical results show a characteristic spatial pattern conditioned by the distribution of economic activity. There is also evidence indicating a reduction of about 83% in the average diffusion capacity of the virus associated with the vaccination process implemented in the department. The findings are valuable for informing public policies that encompass the geographic environment and are necessary for mitigating viruses and similar pandemic processes.

Spatial modeling of the occurrences of geogenic fluoride in groundwater systems in Tanzania: Implications for the provision of safe drinking water

Inadequate data and spatial dependence in the observations during geochemical studies are among the disturbing conditions when estimating environmental factors contributing to the local variability in the pollutants of interest. Usually, spatial dependence occurs due to the researcher's imperfection on the natural scale of occurrence which affects the sampling strategy. As a consequence, observations on the study variable are significantly correlated in space. In this study, the machine learning approach was developed and used to study the environmental factors controlling the local variability in fluoride concentrations in drinking water sources of northern Tanzania within the East African Rift Valley. The approach constituted the use of geographical information systems (GIS) technology, exploratory spatial data analysis (ESDA) methods, and spatial regression modeling at a local level. The environmental variables used to study the local variation in fluoride concentration include topography, tectonic processes, water exchanges between hydrogeological layers during lateral movement, mineralization processes (EC), and water pH. The study was based on 20 local spatial regimes determined using GIS based on water sources density in the four hydrogeological environments. Specifically, the non-parametric (one-way Kruskal-Wallis sum ranks test and Multiple Comparisons Dunn Test), spatial statistics (Global Moran's I statistic), ordinary least squares (OLS) regression, and spatial lag models were used to quantify the effects of topography, tectonic processes, water exchange between hydrogeological environments and water physiochemical parameters (pH and EC) on the spatial variability of fluoride concentrations in drinking water sources at a local scale. In order of significance, the local spatial variation in fluoride concentration is influenced by the EC, topography, tectonic processes, pH, and water exchange between hydrogeological layers during water movement. The results presented in this paper are crucial for safe water access planning in naturally contaminated aquifer systems.

Can cycling compensate the distributional inequity of public service facilities? Evidence from Shenzhen, China

Most traditional studies measure accessible equity of public service facilities (PSFs) statically, and tend to neglect travelling scope disparities among people with different travelling abilities and conditions. This study offers a new perspective for dynamically assessing the actual occupancy of PSFs from both the provision and demand sides. Taking Shenzhen as an example, and employing big data on shared-bike orders, we explored the compensation mechanism of cycling for the distributional inequity of PSFs, with a two-phase measurement framework. First, the Gini index and Lorenz curve were adopted to compare the distributional equality of the grids’ originally-allocated PSFs (based on the division of block-scale grids) and their cycling-accessed PSFs (based on delineation of the cycling scope with shared-bike orders). Second, a spatial lag model was established to reveal the exact factors affecting the ability to improve the PSF occupancy through cycling. The results showed that cycling compensated the distributional inequity of PSFs. This compensation effect varied among groups of different ages, genders, and incomes. Moreover, mixed land use and the low density of trunk roads promoted this compensation. These findings can provide decision-makers with references for future PSF planning in Shenzhen, and other similar cities worldwide.