Spatial Error Regression Models Research Articles

Crime against women in India: district-level risk estimation using the small area estimation approach

BackgroundThe global prevalence of crimes against women has made it an enduring public health challenge that has persisted over time. The achievement of the 2030 Sustainable Development Goal (SDG) is intricately tied to the actions taken to prevent these crimes as their repercussions directly affect progress across various SDGs. This study aimed to provide a comprehensive examination of the prevalence of crimes against women across districts and states in India, analyzing changes from 2020 to 2022, and subsequently identifying associated factors.MethodsThe study is an ecological analysis conducted across all districts of India using the data on crimes against women for the period 2020 and 2022 obtained from the National Crime Records Bureau (NCRB) of India. A small area estimation method was used to obtain district-level relative risks of crime against women for both periods. Hotspot analysis was carried out to identify the current hotspots and coldspots. Further spatial regression was used to identify the factors associated with crimes against women in the year 2022.ResultsThe results indicated a rise in the reported crime against women cases between 2020 and 2022. The rate of crimes against women at the national level was found to be 57 in the year 2020, whereas, in 2022, it increased to 67. The highest crime rate in the year 2022 was found to be 145 in Delhi, while Nagaland had the lowest crime rate of 5. The relative risk of crime against women varied from 0.046 to 4.68 in 2020, while in 2022, it spanned from 0.02 to 6.10. Significant hotspots were found in parts of Rajasthan, Madhya Pradesh, Haryana, Telangana, and Odisha. The results of the spatial error regression model showed that the sex ratio and the population density of the district have significant associations with the occurrence of crimes against women.ConclusionThe rise in the incidence of crime against women emphasizes the importance of tackling the spatial inequality in relative risk across Indian districts. By thoughtfully addressing this variation and conducting targeted studies in high-risk areas, we can enhance our understanding of the obstacles to implementing effective measures against violence targeting women.

Poverty Modeling in Indonesia: a Spatial Regression Analysis

The government has made various efforts to reduce poverty in Indonesia. However, based on the World Population Review report, Indonesia is still ranked as the 73rd poorest country in the world in 2022 based on the value of gross national income. Therefore, it is necessary to identify the factors that affect poverty. This research was conducted by comparing classical, spatial lag, and spatial error regression, and the best model will be selected. The results show that the spatial error regression model is the best, based on the highest coefficient of determination and the lowest Akaike's information criterion value. Based on the best model, it is found that the expected years of schooling, the rate of gross regional domestic product, the percentage of households that have access to proper sanitation services, and the percentage of households with electric lighting sources have a significant effect on the percentage of poor people. The percentage of poor people in a province is also influenced by the percentage of poor people in the surrounding provinces. The results of this simulation can help the government take initiatives or policies aimed at reducing poverty in Indonesia based on variables that affect poverty.

Violent Crime, Physical Inactivity, and Obesity: Examining Spatial Relationships by Racial/Ethnic Composition of Community Residents.

Violence is a public health issue that disproportionately affects communities of color in urban centers. There is limited understanding of how violent crime is associatedwith adult physical inactivity and obesity prevalence given the racial/ethnic composition of community residents. This research aimed to address this gap by examining census tract-level data in Chicago, IL. Ecological data gathered from a variety of sources were analyzed in 2020. Violent crime rate represented the number of police-reported incidents of homicide, aggravated assault, and armed robbery per 1,000 residents. Spatial error and ordinary least square regression models were evaluated to determine if violent crime rate was significant associated with prevalence (%) of adult physical inactivity and obesity among all Chicago census tracts (N = 798), majority non-Hispanic (NH) White tracts (n = 240), majority NH Black tracts (n = 280), majority Hispanic tracts (n = 169), and racially diverse tracts (n = 109). Majority was defined as ≥ 50% representation. After adjusting for socioeconomic and environmental measures (e.g., median income, grocery store availability, walkability index), violent crime rate was associated with % physical inactivity and % obesity at the census tract level in Chicago, IL (both p < 0.001). Associations were statistically significant among majority NH Black and Hispanic tracts, but not majority NH White and racially diverse tracts. Future studies should evaluate the structural drivers of violence and the influence these drivers have on adult physical inactivity and obesity risk, particularly in communities of color.

Environmental Inequality in Four European Cities: A Study Combining Household Survey and Geo-Referenced Data

AbstractCombining individual-level survey data and geo-referenced administrative noise data for four European cities (Bern, Zurich, Hanover, and Mainz; n = 7,450), we test the social gradient hypothesis, which states that exposure to residential noise is higher for households in a lower socioeconomic position (measured by income and migration background). In addition, we introduce and test the ‘environmental shielding hypothesis’, which states that, given environmental ‘bads’ in the neighbourhood, privileged social groups have better opportunities to shield themselves against them. Our results show that, for many residents of the four cities, observed road traffic and aircraft noise levels are above World Health Organization limits. Estimates of spatial error regression models only partly support the social gradient hypothesis. While we find significant but relatively small income effects and somewhat stronger effects of having a (non-Western) migration background, these effects are not significant in all cities. However, especially high-income households are more capable of avoiding exposure to indoor noise. Due to their residence characteristics and having the resources to maintain high standards of noise protection, these households have more capabilities to shield themselves against environmental bads in their neighbourhood. This supports the environmental shielding hypothesis.

Spatial analysis of factors impacting lower limb major amputation rates in Hungary: Is geography partially destiny?

Background: Lower limb major amputations represent a substantial public health burden in Hungary, where previous research revealed markedly high rates with significant spatial variations. Therefore, we aimed to assess to what extent healthcare and socio-economic factors in the local environment explain the regional disparity. Patients and methods: In a retrospective cohort analysis, based on the healthcare administrative data of the Hungarian population, lower limb major amputations were identified from 1st of January 2017 to 31st of December 2019. The permanent residence of the amputees on the local administrative level (197 geographic units) was used to identify potential healthcare (outpatient care, revascularisation activity) and socio-economic (educational attainment, local infrastructure and services, income and employment) determinants of amputations. Spatial effects were modelled using the spatial Durbin error regression model. Results: 10,209 patients underwent 11,649 lower limb major amputations in the observational period. In our spatial analysis, outpatient care was not associated with local amputation rates. However, revascularisation activity in a geographic unit entailed an increased rate of amputations, while revascularisations in the neighbouring areas were associated with a lower rate of amputations, resulting in an overall neutral effect (β=-0.002, 95% CI: -0.05 - 0.04, p=0.96). The local socio-economic environment had a significant direct inverse association with amputations (β=-7.45, 95% CI: -10.50 - -4.42, p<0.0001) . Our spatial model showed better performance than the traditional statistical modelling (ordinary least squares regression), explaining 37% of the variation in amputations rates. Conclusions: Regional environmental factors explain a substantial portion of spatial disparities in amputation practice. While the socio-economic environment shows a significant inverse relationship with the regional amputation rates, the impact of the local healthcare-related factors (outpatient care, revascularisation activity) is not straightforward. Unravelling the impact of the location on amputation practice requires complex spatial modelling, which may guide efficient healthcare policy decisions.

Understanding the Change in the Prevalence and Factors Influencing the Childhood Stunting Using District-Level Data from NFHS-4 and NFHS-5 in India.

To compare the district level prevalence of childhood stunting between NFHS-4 and NFHS-5 and to explore the correlates of it at the district level. Although malnutrition rates in India have decreased over a period, country is still a home for the highest number of stunted and wasted children in the world. Among the South Asian countries, India has the second highest number of stunted children. An ecological study conducted by using the data from fourth and fifth round of National Family Health Survey. Study concentrated on percentage of children who were stunted across 692 Indian districts during 2 survey periods and its correlates from NFHS-5. District level change in childhood stunting was calculated by differencing the NFHS-5 estimates from NFHS-4. Descriptive statistics were used to understand the nature of the variables and Moran's I statistic was calculated to check for the spatial autocorrelation in the childhood stunting. Spatial error regression model was used to identify the correlates of childhood stunting. Among the Indian districts considered, 243 districts showed the increase in childhood stunting between the time periods considered. Currently, about 33.56% of children in India are stunted and there is high spatial disparity in the prevalence of childhood stunting among the districts of it. Major hotspots of childhood stunting were found in the parts of UP, Bihar, Jharkhand, and West Bengal. Households access to improved sanitation facility, iodized salt, clean fuel, women 10 plus years of schooling, post-natal care of mother were found to be the significant protective factors. Closed spacing of births, teenage pregnancy, low BMI of women, childhood diarrhea, and anemia were found to be the significant risk factors of childhood stunting. Stunting depends on several other factors apart from poverty, working on these factors will help in reducing childhood stunting in India.

Socio-demographic and health vulnerability in prescribed-burn exposed versus unexposed counties near the National Forest System

Prescribed fire is an increasingly important tool in restoring ecological conditions and reducing uncontrolled wildfire. Prescribed burn techniques could reduce public health impacts associated with wildfire smoke exposure. However, there have been few assessments of the health impacts of prescribed burning, and potential vulnerabilities among populations exposed to smoke from prescribed fires. Our study area focused on counties in and near U.S. National Forests – a set of lands distributed across the U.S. In county-level analyses, we compared the sociodemographic and health characteristics of areas that were exposed with those that were not exposed to prescribe burns during the years 2010–2019 on a national level and within three regions. In addition, using spatial error regression models, we looked for associations between prescribed fire exposure and health behaviors and outcomes while controlling for spatial autocorrelation. On a national level, we found disproportionate prescribed fire exposure in rural counties with higher percentage mobile home and vacant housing units, and higher percentage African-American and white populations. Regionally, we found evidence of disproportionate exposure to prescribed burns among counties with lower percentage white population, higher percentage Hispanic population and mobile homes in the southern region, and to high poverty counties with high vacancy in the western region. These findings could indicate that vulnerable populations face potential health risks from prescribed burning smoke exposure, but also that they are not missing out on the benefits of prescribed burning, which could involve considerably lower smoke exposure compared to uncontrolled wildfire. In addition, in regression analyses, we found no evidence of disproportionate health burden in exposed compared to unexposed counties. Awareness of these patterns could influence both large-scale or institutional polices about prescribed burning practice, and could be used to build decision-making factors into modeling tools and smoke management plans, as well as community-engagement around wildfire risk reduction.

Impact of Women Empowerment on Infant Mortality Rate – Spatial Lag and Error Regression Model

Background: Despite significant progress in recent decade, the burden of infant mortality in India remains high. As women is the main caregiver to the infant, women empowerment may be the important determinant of infant mortality rate which has received little attention. Therefore, the current study was planned to study the impact of women empowerment on infant mortality and to find the best fit regression model to predict infant mortality rate in various states of India.&#x0D; Methods: The National Family Health Survey (NFHS) 2015-16 data was used and infant mortality rate in various states and union territories of India was mapped. Regression analysis was performed with infant mortality rate as the dependent variable. The exploratory variables (Percentages) used for women empowerment were the literacy among women, women who opted for institutional delivery, women owning a house and/or land (alone or jointly with others), women having a bank or saving account and women having mobile phone that they themselves used. Ordinary Least Square Regression and spatial lag and error regression models were applied. Infant mortality rates predicted by using the best fit model were mapped.&#x0D; Results: Institutional delivery, owning a bank account and availability of personal mobile phone showed statistically significant inverse relation with infant mortality rate. Out of three regression models, spatial lag regression model was found to be the best fit model to predict infant mortality rate. The northern and eastern states were predicted to have highest infant mortality rate.&#x0D; Conclusion: As northern and eastern states were predicted to have highest infant mortality rate, further studies at individual level may be planned to find out the factors in these regions.

Sociodemographic determinants of COVID-19 incidence rates in Oman: Geospatial modelling using multiscale geographically weighted regression (MGWR)

The current COVID-19 pandemic is evolving rapidly into one of the most devastating public health crises in recent history. By mid-July 2020, reported cases exceeded 13 million worldwide, with at least 575,000 deaths and 7.33 million people recovered. In Oman, over 61,200 confirmed cases have been reported with an infection rate of 1.3. Spatial modeling of disease transmission is important to guide the response to the epidemic at the subnational level. Sociodemographic and healthcare factors such as age structure, population density, long-term illness, hospital beds and nurse practitioners can be used to explain and predict the spatial transmission of COVID-19. Therefore, this research aimed to examine whether the relationships between the incidence rates and these covariates vary spatially across Oman. Global Ordinary Least Squares (OLS), spatial lag and spatial error regression models (SLM, SEM), as well as two distinct local regression models (Geographically Weighted Regression (GWR) and multiscale geographically weighted regression MGWR), were applied to explore the spatially non-stationary relationships. As the relationships between these covariates and COVID-19 incidence rates vary geographically, the local models were able to express the non-stationary relationships among variables. Furthermore, among the eleven selected regressors, elderly population aged 65 and above, population density, hospital beds, and diabetes rates were found to be statistically significant determinants of COVID-19 incidence rates. In conclusion, spatial information derived from this modeling provides valuable insights regarding the spatially varying relationship of COVID-19 infection with these possible drivers to help establish preventative measures to reduce the community incidence rate.

Pricing Moral Hazard in Residential Properties: The Impact of Sinkhole Claims on House Prices

Previous research shows that sinkhole presence, proximity, and density create negative externalities and all have a significant negative effect on house prices. This study extends sinkhole research by examining opportunistic fraud and moral hazard associated with the relationship between sinkhole insurance claims and house prices. This is done by evaluating the relationship between house prices and the payment or denial of sinkhole insurance claims for both affected properties and neighboring properties. Applying a spatial error regression model to single-family detached home sales and sinkhole insurance claims data for Hillsborough County, Florida for the period 2008 to 2016, we find that not only do sinkhole insurance claims have a negative impact on the property associated with the claim, but also have a negative impact on surrounding properties regardless of the source of classification. This result holds for both paid and unpaid insurance claims. The results also show a price discount even after the sinkhole has been remediated.

Urban Form as a Technological Driver of Carbon Dioxide Emission: A Structural Human Ecology Analysis of Onroad and Residential Sectors in the Conterminous U.S.

This study investigates the role of urban form as a technological driver of U.S. CO2 emissions for the onroad and residential sectors. The STIRPAT (Stochastic Impacts by Region on Population, Affluence, and Technology) human structural ecology framework is extended by drawing from science and technology studies (STS) to theorize urban form as a sociotechnical system involving practices and knowledge that contribute to urban land use as a material artifact on the landscape influencing emissions. Questions addressed are: (1) “What is the influence of urban form on total sector CO2 emissions?” and (2) “How does the influence of urban form on CO2 emissions differ for metropolitan versus non-metropolitan status?” Spatial error regression models were estimated using county-level CO2 emissions data from Project Vulcan. The National Land Cover Dataset (NLCD) was used to quantify measures of urban form. Other independent variables were derived from U.S. Census data. Results demonstrate carbon reduction benefits achievable through a developed land use mix containing a greater proportion of high intensity relative to low intensity use. Urban form matters, but it matters differently in terms of sign, significance, and interpretation depending on emission sector and metro versus non-metro status. A focus on urban form provides policymakers potential leverage for carbon mitigation efforts that focus on total emissions as opposed to per capita emission. A feature of the research is its integration of concepts and theory from structural human ecology, STS, land change science, and GIScience.

Structural change and agglomeration in the Hungarian pork industry

ABSTRACT The paper analyses agglomeration effects and spatial externalities in the Hungarian hog sector between 2000 and 2010. We apply a spatial lag-spatial error regression model to capture horizontal and vertical spillover effects and to understand the environmental restrictions that determine the location of pork production at the municipality level. Due to the dual nature of the structure of the Hungarian pork industry, we investigate agglomeration effects for individual and corporate farms separately. Results indicate that pork production by these farm groups is affected by different factors in different ways. We distinguish two different ‘worlds’ within the Hungarian pork industry. The ‘introvert world’ of individual farms is very sensitive to agglomeration effects and spatial externalities. The ‘extrovert world’ of corporate farms is more resistant to agglomeration economies and spatial externalities.

Residential segregation in hillside areas of Seoul, South Korea: A novel approach of geomorphons classification

This research examines residential segregation in the hillside residential areas (HRAs) of Seoul, South Korea, by applying a newly classified geomorphons -- a synthetic land classification system -- created from 30 × 30 m2 resolution digital elevation model data to redefine Seoul's hillside residential areas. The multi-group Entropy Index explores segregation within and among the dimensions of socio-economic and built-environment characteristics in HRAs and non-HRAs. We apply the Local Indicator of Spatial Association to confirm strong spatial autocorrelation, and the clusters and outliers within the HRAs. This is rectified by the Spatial Error (regression) Model which affirms that the residential communities are most strongly segregated along dimensions of household composition, housing-built year, and educational attainments, and have significant relationship with the economic status of HRA residents. The newly classified geomorphons also suggest that segregation along the dimensions of socio-economic and built-environment characteristics are higher in HRAs compared to the non-HRAs.

Spatial autoregressive with a spatial autoregressive error term model and its parameter estimation with two-stage generalized spatial least square procedure

Spatial relationship models often use dependence relationships into covariance structures through the autoregressive model. The autoregressive process is shown through the dependence relationship between a set of observations or a location from now on called the dependent spatial model. The Spatial dependent model is divided into two categories: spatial lag and spatial error. The spatial lag regression model is a model that considers dependent variables on an area with other areas associated with it, and the spatial error regression model is a model that takes into account the dependency of error values of an area with errors in other areas associated with it. Models with both dependencies are expressed as spatial autoregressive models with a spatial autoregressive error term (SAR-SAR). These dependencies resulted in the estimation of parameters by the ordinary least square method (OLS) resulting in inconsistent estimators. Therefore a special estimation method is required which results in a consistent estimate of the generalized spatial two-stage least square (GS2SLS). In this paper, we review the parameter estimation of SAR-SAR model with GS2SLS. To complete this paper, we also applicate of SAR-SAR model in dengue hemorrhagic fever (DHF) case in Surakarta, Central Java.

The association between county-level safety net treatment access and opioid hospitalizations and mortality in New York

BackgroundIncreases in pharmaceutical opioid sales have paralleled the quadrupling of prescription opioid overdose deaths and spikes in emergency department visits for non-medical opioid prescription overdoses. In response, federal and state governments have advanced a myriad of policies to reduce opioid availability and increase treatment access, aimed to ultimately reduce opioid-related mortality. Despite these efforts, including ACA Medicaid expansion and more robust prescription drug monitoring programs, opioid-related mortality has continued to rise in NY and 29 other states. This study examined whether geographic access to Federally Qualified Health Centers, opioid treatment programs, and buprenorphine providers mitigated opioid-related hospital visits (emergency department, inpatient, and all visits) and mortality, by county, between 2012 and 2014. MethodsThe authors examined the relationships among opioid-related health outcomes and geographic access to treatment options using spatial error regression models at the county (n = 62) level in 2012 and 2014. Z-tests further assessed significant differences in access coefficients between 2012 and 2014. ResultsOf the 62 counties in New York State in 2014, 54 (87.1%) showed increased opioid overdose-related emergency department rates (t = 9.125, p < 0.001), and 37 (59.7%) showed mortality rate increases (t = 1.687, p < 0.1), compared to 2012. Regression models demonstrated significant negative relationships between county-level opioid-related mortality rates and geographic access to opioid treatment programs, Federally Qualified Health Centers in both 2012 and 2014 and buprenorphine providers concentration in 2014 while adjusting for county socio-demographics (all p values < 0.05). Access coefficients were not significantly different between 2012 and 2014 (p > 0.05). ConclusionsGreater geographic access to treatment services was protective against opioid-related mortality. Access to opioid treatment may not be sufficient to mitigate opioid-related hospital visits or mortality, but may offset climbing mortality rates in select counties.

The distribution of cardiac diagnostic testing for acute coronary syndrome in the Brazilian healthcare system: A national geospatial evaluation of health access.

BackgroundLittle is known about the utilization of cardiac diagnostic testing in Brazil and how such testing is related with local rates of acute coronary syndrome (ACS)-related mortality.Methods and resultsUsing data from DATASUS, the public national healthcare database, absolute counts of diagnostic tests performed were calculated for each of the 5570 municipalities and mapped. Spatial error regression and geographic weighted regression models were used to describe the geographic variation in the association between ACS mortality, income, and access to diagnostic testing.From 2008 to 2014, a total of 4,653,884 cardiac diagnostic procedures were performed in Brazil, at a total cost of $271 million USD. The overall ACS mortality rate during this time period was 133.8 deaths per 100,000 inhabitants aged 20 to 79. The most commonly utilized test was the stress ECG (3,015,993), followed by catheterization (862,627), scintigraphy (669,969) and stress echocardiography (105,295). The majority of these procedures were conducted in large urban centers in more economically developed regions of the country. Increased access to testing and increased income were not uniformly associated with decreased ACS mortality, and tremendous geographic heterogeneity was observed in the relationship between these variables.ConclusionsThe majority of testing for ACS in Brazil is conducted at referral centers in developed urban settings. Stress ECG is the dominant testing modality in use. Increased access to diagnostic testing was not consistently associated with decreased ACS mortality across the country.

In the shadow of production: Coal waste accumulation and environmental inequality formation in Eastern Kentucky

This article advances an environmental-sociological and quantitative spatial-analytic approach to the study of environmental inequality formation in coal country. We use spatial error regression models in a case study of 2000 census block group proximity to hazardous coal waste impoundments amidst shifting coal production trajectories and impoundment disaster contexts in the declining Eastern Kentucky coalfields. Proximity to abandoned and sealed mines, coal production density, and the “buffering effect” of rural-agricultural context are the most powerful predictors of impoundment proximity in the period encapsulating the boom years of coal production and culminating in 2000. Amidst continued coal industry decline and the post-2000 Martin County impoundment disaster context, proximity to older impoundments, including the failed Martin County impoundment, proximity to abandoned and sealed mines, and poverty levels by 2000 are the most powerful predictors of proximity to impoundments sited from 2001 to 2006. Findings have important environmental justice research and policy implications.

Hedonic modeling of commercial property values: distance decay from the links and nodes of rail and highway infrastructure

This study investigates the impacts of positive and negative externalities of highways and light rail on commercial property values in Phoenix, Arizona. We hypothesize that the positive externality (i.e., accessibility) of highway and light rail accrues at exits and stations, whereas nodes and links of highways and light rail emanate negative effects. Positive and negative effects decay with increasing distance and are captured by multiple distance bands. Hypotheses are tested using a spatial error regression model. Results show that commercial property values are positively and significantly associated with the accessibility benefits of transport nodes. The distance-band coefficients form a typical distance decay curve for both modes with no detectable disamenity donut effect immediately around the nodes. Unexpectedly, impacts of light rail stations extend farther than those of highway exits. Only the links of light rail are negatively associated with property values, as hypothesized. When the sample is subdivided by type of commercial property, the magnitude and distance extent of impacts are surprisingly consistent, with light rail stations having stronger impacts than highway exits on all three classes of commercial property: industrial, office, and retail and service. Rail links have a significant negative relationship with price for all three types of commercial property, but highways have a significant negative relationship only with industrial properties.

Geographic variation in sexual behavior can explain geospatial heterogeneity in the severity of the HIV epidemic in Malawi

BackgroundIn sub-Saharan Africa, where ~ 25 million individuals are infected with HIV and transmission is predominantly heterosexual, there is substantial geographic variation in the severity of epidemics. This variation has yet to be explained. Here, we propose that it is due to geographic variation in the size of the high-risk group (HRG): the group with a high number of sex partners. We test our hypothesis by conducting a geospatial analysis of data from Malawi, where ~ 13% of women and ~ 8% of men are infected with HIV.MethodsWe used georeferenced HIV testing and behavioral data from ~ 14,000 participants of a nationally representative population-level survey: the 2010 Malawi Demographic and Health Survey (MDHS). We constructed gender-stratified epidemic surface prevalence (ESP) maps by spatially smoothing and interpolating the HIV testing data. We used the behavioral data to construct gender-stratified risk maps that reveal geographic variation in the size of the HRG. We tested our hypothesis by fitting gender-stratified spatial error regression (SER) models to the MDHS data.ResultsThe ESP maps show considerable geographic variation in prevalence: 1–29% (women), 1–20% (men). Risk maps reveal substantial geographic variation in the size of the HRG: 0–40% (women), 16–58% (men). Prevalence and the size of the HRG are highest in urban centers. However, the majority of HIV-infected individuals (~75% of women, ~ 80% of men) live in rural areas, as does most of the HRG (~ 80% of women, ~ 85% of men). We identify a significant (P < 0.001) geospatial relationship linking the size of the HRG with prevalence: the greater the size, the higher the prevalence. SER models show HIV prevalence in women is expected to exceed the national average in districts where > 20% of women are in the HRG. Most importantly, the SER models show that geographic variation in the size of the HRG can explain a substantial proportion (73% for women, 67% for men) of the geographic variation in epidemic severity.ConclusionsTaken together, our results provide substantial support for our hypothesis. They provide a potential mechanistic explanation for the geographic variation in the severity of the HIV epidemic in Malawi and, potentially, in other countries in sub-Saharan Africa.

Sinkholes and Residential Property Prices: Presence, Proximity, and Density

Spatial amenities, along with structural characteristics, affect residential property values. Although the bundle of structural characteristics is typically the primary value determinant, studies have shown that externalities and risk factors can adversely affect property values. We use residential property sales data from 2010 to 2014 and sinkhole data from “sinkhole alley” in Florida to examine the effect of sinkhole presence, proximity, and density on the sale price of residential real estate. Using a spatial error regression model, the results show that sinkhole proximity and sinkhole exposure (density) create a negative externality and both have a significant negative effect on house prices. While the results show a negative effect of having a sinkhole on the property, those results are not statistically significant.