A Spatial Autocorrelation Model of the Effects of Population Density on Fertility

1983 ◽  
Vol 48 (1) ◽  
pp. 121 ◽  
Author(s):  
Colin Loftin ◽  
Sally K. Ward
Keyword(s):  
Population Density ◽  
Spatial Autocorrelation

scholarly journals The Spatiotemporal Interaction Effect of COVID-19 Transmission in the United States

2021 ◽  
Vol 10 (6) ◽  
pp. 387
Author(s):  
Lingbo Liu ◽  
Tao Hu ◽  
Shuming Bao ◽  
Hao Wu ◽  
Zhenghong Peng ◽  
...  
Keyword(s):  
United States ◽  
Population Density ◽  
Spatial Autocorrelation ◽  
Low Income ◽  
Pearson Correlation ◽  
The United States ◽  
Time Varying ◽  
Ols Regression ◽  
Highly Educated ◽  
The Impact

(1) Background: Human mobility between geographic units is an important way in which COVID-19 is spread across regions. Due to the pressure of epidemic control and economic recovery, states in the United States have adopted different policies for mobility limitations. Assessing the impact of these policies on the spatiotemporal interaction of COVID-19 transmission among counties in each state is critical to formulating epidemic policies. (2) Methods: We utilized Moran’s I index and K-means clustering to investigate the time-varying spatial autocorrelation effect of 49 states (excluding the District of Colombia) with daily new cases at the county level from 22 January 2020 to 20 August 2020. Based on the dynamic spatial lag model (SLM) and the SIR model with unreported infection rate (SIRu), the integrated SLM-SIRu model was constructed to estimate the inter-county spatiotemporal interaction coefficient of daily new cases in each state, which was further explored by Pearson correlation test and stepwise OLS regression with socioeconomic factors. (3) Results: The K-means clustering divided the time-varying spatial autocorrelation curves of the 49 states into four types: continuous increasing, fluctuating increasing, weak positive, and weak negative. The Pearson correlation analysis showed that the spatiotemporal interaction coefficients in each state estimated by SLM-SIRu were significantly positively correlated with the variables of median age, population density, and proportions of international immigrants and highly educated population, but negatively correlated with the birth rate. Further stepwise OLS regression retained only three positive correlated variables: poverty rate, population density, and highly educated population proportion. (4) Conclusions: This result suggests that various state policies in the U.S. have imposed different impacts on COVID-19 transmission among counties. All states should provide more protection and support for the low-income population; high-density populated states need to strengthen regional mobility restrictions; and the highly educated population should reduce unnecessary regional movement and strengthen self-protection.

SPATIAL AUTOCORRELATION, SPATIAL MODELING, AND IMPROVEMENTS IN GRASSHOPPER SURVEY METHODOLOGY

1989 ◽  
Vol 121 (7) ◽  
pp. 579-588 ◽  
Author(s):  
D.L. Johnson
Keyword(s):  
Population Density ◽  
Spatial Autocorrelation ◽  
Survey Data ◽  
Sampling Method ◽  
Survey Methodology ◽  
Crop Fields ◽  
Density Maps ◽  
Crop Type ◽  
Roadside Survey ◽  
Linear Regressions

AbstractAnalysis of 10 years of grasshopper survey data (1978–1987) indicated that grasshopper populations in fields can be reliably predicted from roadside survey counts. Direct estimation of grasshopper densities in crop fields is no longer required for summaries of infestation levels or forecasts. Spatial autocorrelation was significant and positive for both roadside and field counts. The coefficient of variation of the field counts was greater than that of the roadside counts in each of the last 10 years. Population density was summarized by crop type and sampling method for the last 10 years. Linear regressions fitted to the 1978–1984 grasshopper survey data were used to estimate field population density from crop type and roadside counts in 1985–1987. Maps of population density were generated from the predicted and observed field counts with SPANS, a microcomputer-based geographic information system. Large coefficients of association (73–79%) between the predicted and observed maps attested to the sufficiency of road-side counts as the basis for production of population density maps.

scholarly journals Spatiotemporal Distribution of Varicella in South Korea

2020 ◽  
Author(s):  
Young Hwa Lee ◽  
Young June Choe ◽  
Seung-Sik Hwang ◽  
Sung-Il Cho
Keyword(s):  
Population Density ◽  
Spatial Autocorrelation ◽  
Vaccine Coverage ◽  
Demographic Data ◽  
Spatial Regression ◽  
National Level ◽  
Geographic Region ◽  
Incidence Rates ◽  
Surveillance Period ◽  
Varicella Zoster

Abstract Background Varicella is a highly contagious disease caused by the varicella-zoster virus (VZV). Given its tendency to cluster geographically, spatial analyses may provide better understanding of the pattern of varicella transmission. We investigated the spatial characteristics of varicella in Korea and the risk factors for varicella at a national level. Methods Using national surveillance and demographic data, we examined the spatial distribution of incidence rates and their spatial autocorrelation and calculated Moran’s index. Spatial regression analysis was used to identify sociodemographic predictors of varicella incidence at the district level. Results An increasing tendency in the annual incidence of varicella was observed over a 12-year period (2006–2017), with a surge in 2017. There was a clear positive spatial autocorrelation of the varicella incidence rate during the surveillance period. During 2006–2014, High-High (HH) clusters were mostly confined to the northeast region and neighboring districts. Population density and the number of hospitals per 1,000 persons had negative coefficients, and the former was significant. Childhood percentage, percentage of children under 12 years of age among total population, and vaccine coverage rate had positive coefficients, but only the former was significant. Conclusion There was a temporal uptrend in the incidence of varicella in Korea from 2006 to 2017 in the setting of positive spatial associations. The varicella incidence according to geographic region varied with population density, childhood percentage, suggesting the importance of the community-level surveillance and monitoring strategies.

scholarly journals Geospatial evaluation of trade-offs between equity in physical access to healthcare and health systems efficiency

2020 ◽  
Vol 5 (10) ◽  
pp. e003493
Author(s):  
Hari S Iyer ◽  
John Flanigan ◽  
Nicholas G Wolf ◽  
Lee Frederick Schroeder ◽  
Susan Horton ◽  
...  
Keyword(s):  
Population Density ◽  
Spatial Autocorrelation ◽  
Travel Time ◽  
Geographical Distribution ◽  
Pearson’S Correlation ◽  
Trade Offs ◽  
Health Centres ◽  
Pearson's Correlation ◽  
Physical Access ◽  
Geographical Clusters

IntroductionDecisions regarding the geographical placement of healthcare services require consideration of trade-offs between equity and efficiency, but few empirical assessments are available. We applied a novel geospatial framework to study these trade-offs in four African countries.MethodsGeolocation data on population density (a surrogate for efficiency), health centres and cancer referral centres in Kenya, Malawi, Tanzania and Rwanda were obtained from online databases. Travel time to the closest facility (a surrogate for equity) was estimated with 1 km resolution using the Access Mod 5 least cost distance algorithm. We studied associations between district-level average population density and travel time to closest facility for each country using Pearson’s correlation, and spatial autocorrelation using the Global Moran’s I statistic. Geographical clusters of districts with inefficient resource allocation were identified using the bivariate local indicator of spatial autocorrelation.ResultsPopulation density was inversely associated with travel time for all countries and levels of the health system (Pearson’s correlation range, health centres: −0.89 to −0.71; cancer referral centres: −0.92 to −0.43), favouring efficiency. For health centres, negative spatial autocorrelation (geographical clustering of dissimilar values of population density and travel time) was weaker in Rwanda (−0.310) and Tanzania (−0.292), countries with explicit policies supporting equitable access to rural healthcare, relative to Kenya (−0.579) and Malawi (−0.543). Stronger spatial autocorrelation was observed for cancer referral centres (Rwanda: −0.341; Tanzania: −0.259; Kenya: −0.595; Malawi: −0.666). Significant geographical clusters of sparsely populated districts with long travel times to care were identified across countries.ConclusionNegative spatial correlations suggested that the geographical distribution of health services favoured efficiency over equity, but spatial autocorrelation measures revealed more equitable geographical distribution of facilities in certain countries. These findings suggest that even when prioritising efficiency, thoughtful decisions regarding geographical allocation could increase equitable physical access to services.

scholarly journals Explaining COVID-19 contagion in Portuguese municipalities using spatial autocorrelation models

2021 ◽  
Vol 30 (1) ◽  
pp. 1-12
Author(s):  
Paulo Mourao ◽  
Ricardo Bento
Keyword(s):  
Population Density ◽  
Spatial Autocorrelation ◽  
Elderly Population ◽  
The Elderly ◽  
Exponential Phase ◽  
Spatial Proximity ◽  
Positive Effect

This paper investigates the pattern of COVID-19 contagion in Portuguese municipalities from March 23rd to April 5th (the exponential phase). We have recurred to spatial autocorrelation models to discuss how the conglomeration of highly infectious spaces has also contributed to infecting neighbouring municipalities. We have used several indicators for the contagion of COVID-19 from the number of infectious individuals to rates of infectious. As explicative variables, additionally to spatial proximity, we also considered population density, the share of the elderly population as well as the length of municipal perimeter/border. Our results show that highly dense municipalities tended to contaminate close areas. Lengthier perimeters also showed a positive effect on the contagious indicators for a given municipality.

scholarly journals Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China

2021 ◽  
Vol 18 (12) ◽  
pp. 6261
Author(s):  
Xiangxue Zhang ◽  
Yue Lin ◽  
Changxiu Cheng ◽  
Junming Li
Keyword(s):  
Population Density ◽  
Spatial Autocorrelation ◽  
Socioeconomic Factors ◽  
North China ◽  
Interactive Effects ◽  
Atmospheric Composition ◽  
Composition Analysis ◽  
Industrial Output ◽  
Road Density ◽  
Global And Local

Severe air pollution has significantly impacted climate and human health worldwide. In this study, global and local Moran’s I was used to examine the spatial autocorrelation of PM2.5 pollution in North China from 2000–2017, using data obtained from Atmospheric Composition Analysis Group of Dalhousie University. The determinant powers and their interactive effects of socioeconomic factors on this pollutant are then quantified using a non-linear model, GeoDetector. Our experiments show that between 2000 and 2017, PM2.5 pollution globally increased and exhibited a significant positive global and local autocorrelation. The greatest factor affecting PM2.5 pollution was population density. Population density, road density, and urbanization showed a tendency to first increase and then decrease, while the number of industries and industrial output revealed a tendency to increase continuously. From a long-term perspective, the interactive effects of road density and industrial output, road density, and the number of industries were amongst the highest. These findings can be used to develop the effective policy to reduce PM2.5 pollution, such as, due to the significant spatial autocorrelation between regions, the government should pay attention to the importance of regional joint management of PM2.5 pollution.

scholarly journals Geographic Variation in Access to Pathology and Laboratory Services in Tanzania: A Cross-Sectional Geospatial Analysis

2020 ◽  
Vol 6 (Supplement_1) ◽  
pp. 45-45
Author(s):  
Hari S. Iyer ◽  
Nicholas G. Wolf ◽  
Edda Vuhahula ◽  
Charles Massambu ◽  
Devanshi Shah ◽  
...  
Keyword(s):  
Population Density ◽  
Spatial Autocorrelation ◽  
Travel Time ◽  
Geographic Variation ◽  
District Level ◽  
Health Centers ◽  
Travel Times ◽  
Spatial Correlations ◽  
District Hospitals ◽  
Referral Hospitals

PURPOSE Increasing noncommunicable disease burden in sub-Saharan Africa requires the urgent scale-up of pathology and laboratory medicine (PALM) services. To identify service gaps at the district level, we studied geographic variation in the correlation between travel time to health facilities and population density. METHODS We linked geospatial data for Tanzania from multiple sources. Facility locations were extracted from a comprehensive facility list in Africa. Data on geographic factors, demographics, and roads were collected from government and nonprofit databases. We classified facilities assuming increasing PALM service readiness by level: dispensaries, health centers, district hospitals, and regional/referral hospitals. We input these data into the AccessMod 5 algorithm to estimate travel time across Tanzania with 1-km resolution for each PALM classification. We then calculated district-level averages of population and travel time for each PALM category. Associations between these variables were estimated using a bivariable local indicator of spatial autocorrelation, specifying immediate contiguity neighborhood definition. Spatial analysis was restricted to 172 contiguous districts (islands not included). Significance tests were two sided, with an α of .05. RESULTS Analysis included 5,342 dispensaries, 667 health centers, 185 district hospitals, and 34 regional/referral hospitals. Maps revealed clusters of estimated travel time in excess of 6 hours in less populated western and southern districts. More districts reported an average travel time of less than 1 hour to the nearest dispensary (69%) than to regional/referral hospitals (16%). Bivariable local indicators of spatial autocorrelation revealed few significant clusters of spatial correlations; however, significant correlations between low population density and longer travel times in neighboring districts were obtained for 13%, 16%, 15%, and 13% of districts for dispensaries, health centers, district hospitals, and regional/referral hospitals, respectively. CONCLUSION Limited variability of district-level spatial correlations suggests somewhat equitable geographic allocation of PALM services in Tanzania, with small areas of low population density and long travel times that demand additional intervention. Limitations include a lack of ascertainment of specific PALM services.

Social Disorganization and Homicide in Recife, Brazil

2015 ◽  
Vol 61 (14) ◽  
pp. 1570-1592 ◽  
Author(s):  
Débora V. S. Pereira ◽  
Caroline M. M. Mota ◽  
Martin A. Andresen
Keyword(s):  
Population Density ◽  
Water Supply ◽  
Spatial Autocorrelation ◽  
Social Disorganization ◽  
Census Tract ◽  
Census Data ◽  
Spatial Regression ◽  
Social Disorganization Theory ◽  
Census Tract Level ◽  
Negative Relationships

In this article, we investigate the determinants of homicide in Recife, Brazil, considering social disorganization theory. Using georeferenced homicide data, 2009-2013, and census data, we analyze homicide in Recife using a spatial regression technique that controls for spatial autocorrelation and heteroskedasticity at the census tract level. Overall, we find that homicide in Recife, Brazil, is characterized by social disorganization theory. Specifically, positive relationships are found for inequality, rented houses, and quantity of people, but negative relationships are found for income, literacy, percentage of married people, water supply, public illumination, the percentage of women responsible for the house, and population density. Overall, we find that social disorganization theory provides an instructive framework for understanding homicide in Recife, Brazil. However, there are specific contexts to Brazil that are different from North American contexts.

scholarly journals The Spatiotemporal Interaction Effect of COVID-19 Transmission in the United States

2021 ◽  
Author(s):  
Lingbo Liu ◽  
Tao Hu ◽  
Shuming Bao ◽  
Hao Wu ◽  
Zhenghong Peng ◽  
...  
Keyword(s):  
United States ◽  
Population Density ◽  
Spatial Autocorrelation ◽  
Low Income ◽  
Pearson Correlation ◽  
The United States ◽  
Time Varying ◽  
Ols Regression ◽  
Highly Educated ◽  
The Impact

Abstract Background: Human mobility among geographic units is a possible cause of the widespread transmission of COVID-19 across regions. Due to the pressure of epidemic control and economic recovery, the states of the United States have adopted different policies for mobility limitations. Assessing the impact of these policies on the spatiotemporal interaction of COVID-19 transmission among counties in each state is critical to formulating the epidemic policies.Methods: The study utilized Moran’s I index and K-means clustering to investigate the time-varying spatial autocorrelation effect of 49 states (except the District of Colombia) with the daily new cases at the county level from Jan 22, 2020, to August 20, 2020. Based on the dynamic spatial lag model (SLM) and the SIR model with unreported infection rate (SIRu), the integrated SLM-SIRu model was constructed to estimate the inter-county spatiotemporal interaction coefficient of daily new cases in each state, which was further explored by Pearson correlation and stepwise OLS regression with socioeconomic factors.Results: The K-means clustering divided the time-varying spatial autocorrelation curves of 49 states into four types: continuous increasing, fluctuating increasing, weak positive, and weak negative. The Pearson correlation analysis showed that the spatiotemporal interaction coefficients in each state estimated by SLM-SIRu were significantly positively correlated with median age, population density, and the proportion of international immigrants and the highly educated population, but negatively correlated with the birth rate. The voting rate for Donald Trump in the 2016 U.S. presidential election showed a weak negative correlation. Further stepwise OLS regression retained only three positive correlated variables: poverty rate, population density, and the highly educated population proportion.Interpretation: This result suggests that various state policies in the U.S. have imposed different impacts on COVID-19 transmission among counties. All states should provide more protection and support for the low-income population, high-density populated states need to strengthen regional mobility restrictions, and the highly educated population should reduce unnecessary regional movement and strengthen self-protection.

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