scholarly journals Ecological Study of Variability in the Relationship between Liver Cancer Mortality and Racial Residential Segregation

2021 ◽  
Vol 18 (18) ◽  
pp. 9732
Author(s):  
Amin Bemanian ◽  
Laura Cassidy ◽  
Raphael Fraser ◽  
Purushottam Laud ◽  
Kia Saeian ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Liver Cancer ◽  
Cancer Mortality ◽  
Ecological Study ◽  
Metropolitan Areas ◽  
Spatial Regression ◽  
Racial Segregation ◽  
The United States ◽  
Hotspot Analysis ◽  
Spatial Regression Analysis

Racial segregation has been identified as a predictor for the burden of cancer in several different metropolitan areas across the United States. This ecological study tested relationships between racial segregation and liver cancer mortality across several different metropolitan statistical areas in Wisconsin. Tract-level liver cancer mortality rates were calculated using cases from 2003–2012. Hotspot analysis was conducted and segregation scores in high, low, and baseline mortality tracts were compared using ANOVA. Spatial regression analysis was done, controlling for socioeconomic advantage and rurality. Black isolation scores were significantly higher in high-mortality tracts compared to baseline and low-mortality tracts, but stratification by metropolitan areas found this relationship was driven by two of the five metropolitan areas. Hispanic isolation was predictive for higher mortality in regression analysis, but this effect was not found across all metropolitan areas. This study showed associations between liver cancer mortality and racial segregation but also found that this relationship was not generalizable to all metropolitan areas in the study area.

scholarly journals Relationship Between Liver Cancer Mortality and Racial Residential Segregation Across Wisconsin Metropolitan Areas

2020 ◽  
Author(s):  
Amin Bemanian ◽  
Laura D. Cassidy ◽  
Raphael Fraser ◽  
Purushottam W. Laud ◽  
Kia Saeian ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Liver Cancer ◽  
Cancer Mortality ◽  
Metropolitan Areas ◽  
Spatial Regression ◽  
Racial Segregation ◽  
Cross Sectional Study ◽  
Hotspot Analysis ◽  
Cross Sectional ◽  
Spatial Regression Analysis

AbstractPurposeThis cross-sectional study tested relationships between racial segregation and liver cancer across several different metropolitan areas in Wisconsin.MethodsTract level liver cancer mortality rates in Wisconsin were calculated using cases from 2003-2012. Hotspot analysis was conducted and segregation scores in high, low, and baseline mortality tracts were compared with ANOVA. Spatial regression analysis was done controlling for socioeconomic advantage and rurality.ResultsBlack isolation scores were significantly higher in high mortality tracts compared to baseline and low mortality tracts, but stratification by metropolitan areas found this relationship was driven by two of the five metropolitan areas. Hispanic isolation was predictive for higher mortality in regression analysis, but this effect was not found across all metropolitan areas.ConclusionsThis study showed associations between liver cancer mortality and racial segregation, but found this relationship was not generalizable to all metropolitan areas in the study area.

Why Might Developers Be Reluctant to Build Conservation Subdivisions? Insights from Spatial Regression Analysis

2017 ◽  
Vol 54 (6) ◽  
pp. 1170-1190
Author(s):  
Rayman Mohamed
Keyword(s):  
United States ◽  
Regression Analysis ◽  
Autoregressive Model ◽  
Spatial Regression ◽  
The United States ◽  
Spatial Autoregressive Model ◽  
Independent Variables ◽  
Spatial Autoregressive ◽  
Spatial Regression Analysis ◽  
Shed Light

Despite evidence that households pay more for lots or houses in conservation subdivisions, developers are sometimes reluctant to build them. I use a spatial autoregressive model to shed light on this contradiction. The presence of nearby conservation lots reduces the value of a given conservation lot. I present two possible explanations for this result: (1) Lots located close to each other are indicative of higher density, which is frowned upon by Americans, and (2) conservation lots compete for views and rural aesthetics, and the construction of one lot decreases their availability to other lots. Results for other independent variables corroborate these explanations. Developers’ reluctance to embrace conservation subdivisions in some locations might be a result of regulations that discourage their development. More research is needed on how regulations for conservation subdivisions vary across the United States and how these affect developers’ decisions.

scholarly journals Analisis Regresi Spatial Durbin Model (SDM) untuk Pemodelan Kemiskinan Provinsi Jawa Timur Tahun 2017

2019 ◽  
Vol 12 (1) ◽  
pp. 40-46
Author(s):  
Nur Roudlotul Hidayah ◽  
Artanti Indrasetianingsih
Keyword(s):  
Regression Analysis ◽  
Spatial Regression ◽  
Statistical Technique ◽  
Predictor Variables ◽  
Poor People ◽  
Spatial Durbin Model ◽  
Classic Problem ◽  
Almost All ◽  
Spatial Regression Analysis ◽  
The Relationship

Regression is a statistical technique used to describe the relationship between response variables with one or more predictor variables. The development of classical regression analysis that is influenced by the effects of space or location of a region is called spatial regression analysis. The purpose of this study is to conduct Spatial Durbin Model (SDM) regression analysis for poverty modeling in East Java in 2017. Poverty is a classic problem that occurs in almost all countries and is multidimensional, which is related to social, economic, cultural and other aspects. In 2017, poverty in East Java declined compared to the previous year. Therefore it is necessary to identify the factors that influence poverty. The variables used are the percentage of poor people as the response variable (Y) and predictor variables including Education does not finish elementary school (X1), Literacy Rate age 15 -55 years (X2), informal sector workers (X3), unemployment rate open (X4), household users of land as the widest floor (X5), and households using improper sanitation (X6), and households using drinking water sources are not feasible (X7).    Regresi merupakan teknik statistik yang digunakan untuk menggambarkan hubungan antara variabel respon dengan satu atau lebih variabel prediktor. Pengembangan dari analisis regresi klasik yang dipengaruhi oleh efek ruang atau lokasi wilayah disebut analisis regresi spasial. Tujuan dari penelitian ini adalah untuk melakukan analisis regresi Spatial Durbin Model (SDM) untuk pemodelan kemiskinan di Jawa Timur tahun 2017. Kemiskinan merupakan masalah klasik yang terjadi hampir diseluruh negara dan bersifat multidimensional, dimana berkaitan dengan aspek sosial, ekonomi, budaya dan aspek lainnya. Pada tahun 2017, kemiskinan di Jawa Timur mengalami penurunan jika dibandingkan dengan tahun sebelumnya. Oleh karena itu perlu dilakukan identifikasi faktor-faktor yang berpengaruh terhadap kemiskinan. Variabel yang digunakan yaitu persentase penduduk miskin sebagai variabel respon (Y) dan variabel prediktor antara lain Pendidikan tidak tamat SD (X1), Angka Melek Huruf  (AHM) usia 15 -55 tahun (X2), pekerja sektor informal (X3), tingkat pengangguran terbuka (X4), rumah tangga pengguna tanah sebagai lantai terluas (X5), dan rumah tangga pengguna sanitasi tidak layak (X6), dan Rumah tangga pengguna sumber air minum tidak layak (X7).

An Ecological Study of Cancer Mortality Rates in High Altitude Counties of the United States

2012 ◽  
Vol 13 (2) ◽  
pp. 98-104 ◽  
Author(s):  
Ada O. Youk ◽  
Jeanine M. Buchanich ◽  
Jon Fryzek ◽  
Michael Cunningham ◽  
Gary M. Marsh
Keyword(s):  
United States ◽  
High Altitude ◽  
Cancer Mortality ◽  
Ecological Study ◽  
The United States ◽  
Mortality Rates

SPATIAL REGRESSION ANALYSIS FOR MODELING THE SPATIAL VARIATION IN HIGHWAY CONSTRUCTION COSTS

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Minsoo Baek ◽  
Baabak Ashuri
Keyword(s):  
Regression Analysis ◽  
Spatial Variation ◽  
Spatial Regression ◽  
Highway Construction ◽  
Price Volatility ◽  
Construction Costs ◽  
Construction Market ◽  
Spatial Regression Analysis ◽  
Transportation Agencies ◽  
Geographical Locations

Price volatility in wages, materials, and equipment has a significant impact on highway construction costs. As the construction market and economy have experienced dynamic changes in prices, the price volatility becomes less predictable. In addition, various levels of the price volatility in different market locations aggravate the prediction. Thus, in developing highway construction costs, transportation agencies should consider geographical location of construction projects and market conditions of the locations. Transportation agencies face significant uncertainties in price volatility across different geographical locations. This volatility may not be uniformly distributed across different geographical locations due to changes in the availability of local contractors, materials, equipment, and labor. The objective of this research is to develop statistical models that are capable to explain spatial variations in submitted unit prices for asphalt line items in highway projects considering local market condition factors. Historical bid data used in this research consist of resurfacing and widening projects let in the state of Georgia, the United States, between 2008 and 2015. The methodology of this research is a spatial regression analysis to explain the spatial variation in the submitted unit prices for asphalt line items. The findings of this research indicate that volatility in submitted bid prices is not uniformly distributed across different geographical locations within the same transportation agency. The contribution to the body of knowledge of this research is an improved understanding of the role of local construction market and macroeconomic conditions to explain geographic variability in construction costs.

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