scholarly journals Spatial Heterogeneity, Broadband, and New Firm Formation

2017 ◽  
Vol 21 (1) ◽  
pp. 165
Author(s):  
Jitendra Parajuli ◽  
Kingsley Haynes
Keyword(s):  
Spatial Heterogeneity ◽  
Least Squares ◽  
Geographically Weighted Regression ◽  
Ordinary Least Squares ◽  
Weighted Regression ◽  
Least Squares Regression ◽  
Firm Formation ◽  
Broadband Internet ◽  
New Firm Formation ◽  
New Firm

<p><strong>Purpose:</strong> This paper examines the spatial heterogeneity associated with broadband Internet and new firm formation in a number of U.S. states.</p><p><strong>Methodology/Approach:</strong> Both ordinary least-squares regression and Geographically Weighted Regression are used for the estimation purpose.</p><p><strong>Findings:</strong> The global coefficient estimates of ordinary least-squares regression account for the marginal change in a phenomenon, but such a global measure cannot reveal the locally-varying dynamics. Using Geographically Weighted Regression, it was found that at the aggregate and economic sector levels, the association between single-unit firm births and the provision of broadband Internet varies across counties in Florida and Ohio.</p><p><strong>Originality/Value of paper:</strong> There are numerous studies on broadband Internet in the U.S., but this is the first that explicitly examines broadband provision and new firm formation by taking into account spatial heterogeneity across countries.</p>

scholarly journals Spatial varying relationship between financial development and new firm formation: Evidence from a developing country

2018 ◽  
Vol 65 (5) ◽  
pp. 633-675 ◽  
Author(s):  
Burhan Karahasan
Keyword(s):  
Spatial Heterogeneity ◽  
Developing Country ◽  
Financial Development ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Firm Formation ◽  
Regional Policies ◽  
New Firm Formation ◽  
Eastern Turkey ◽  
New Firm

This study investigates the spatial varying relationship between financial development and new firm formation in Turkey. Even preliminary findings show that regional financial development spurs new firm formation; evidence from Geographically Weighted Regression stresses the heterogeneous impact of finance, which mostly favours the developed regions. Results for the 2002 to 2009 epoch show impact of finance fluctuates within a range of 0.189 for deposits and 0.082 for credits with the lowest impact observed in Eastern Turkey. This points to the necessity of considering the spatial heterogeneity of regional policies, which underlines that one size does not fit all regions.

scholarly journals PROVIDING THE FIRE RISK MAP IN FOREST AREA USING A GEOGRAPHICALLY WEIGHTED REGRESSION MODEL WITH GAUSSIN KERNEL AND MODIS IMAGES, A CASE STUDY: GOLESTAN PROVINCE

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 477-481
Author(s):  
A. Shah-Heydari pour ◽  
P. Pahlavani ◽  
B. Bigdeli
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Geographically Weighted Regression ◽  
Fire Risk ◽  
Ordinary Least Squares ◽  
Gaussian Kernel ◽  
Weighted Regression ◽  
Slope Aspect ◽  
Risk Map ◽  
Geographically Weighted Regression Model

According to the industrialization of cities and the apparent increase in pollutants and greenhouse gases, the importance of forests as the natural lungs of the earth is felt more than ever to clean these pollutants. Annually, a large part of the forests is destroyed due to the lack of timely action during the fire. Knowledge about areas with a high-risk of fire and equipping these areas by constructing access routes and allocating the fire-fighting equipment can help to eliminate the destruction of the forest. In this research, the fire risk of region was forecasted and the risk map of that was provided using MODIS images by applying geographically weighted regression model with Gaussian kernel and ordinary least squares over the effective parameters in forest fire including distance from residential areas, distance from the river, distance from the road, height, slope, aspect, soil type, land use, average temperature, wind speed, and rainfall. After the evaluation, it was found that the geographically weighted regression model with Gaussian kernel forecasted 93.4% of the all fire points properly, however the ordinary least squares method could forecast properly only 66% of the fire points.

scholarly journals Geographically weighted regression modelling of the spatial association between malaria cases and environmental factors in Cameroon.

2019 ◽  
Author(s):  
Marlvin Anemey Tewara ◽  
Liu Yunxia ◽  
Weiqiang Lin ◽  
Binang Helen Barong ◽  
Prisca Ngetemalah Mbah-Fongkimeh ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Spatial Variability ◽  
Least Squares ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Spatial Association ◽  
Ordinary Least Squares ◽  
Geographical Information ◽  
Weighted Regression ◽  
Gwr Model

Abstract Background: Studies have illustrated the association of malaria cases with environmental factors in Cameroon but limited in addressing how these factors vary in space for timely public health interventions. Thus, we want to find the spatial variability between malaria hotspot cases and environmental predictors using Geographically weighted regression (GWR) spatial modelling technique. Methods: The global Ordinary least squares(OLS) in the modelling spatial relationships tool in ArcGIS 10.3. was used to select candidate explanatory environmental variables for a properly specified GWR model. The local GWR model used the global OLS candidate variables to examine, predict and explore the spatial variability between environmental factors and malaria hotspot cases generated from Getis-Ord Gi* statistical analysis. Results: The OLS candidate environmental variable coefficients were statistically significant ( adjusted R 2 = 22.3% and p < 0.01) for a properly specified GWR model. The GWR model identified a strong spatial association between malaria cases and rainfall, vegetation index, population density, and drought episodes in most hotspot areas and a weak correlation with aridity and proximity to water with an overall model performance of 0.243 (adjusted R 2 = 24.3%). Conclusion: The generated GWR maps suggest that for policymakers to eliminate malaria in Cameroon, there should be the creation of malaria outreach programs and further investigations in areas where the environmental variables showed strong spatial associations with malaria hotspot cases . Keywords: Geographically weighted regression, Ordinary least squares, malaria, spatial statistics, mapping, Geographical information systems.

scholarly journals A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters

2016 ◽  
Author(s):  
Abhishek K Kala ◽  
Chetan Tiwari ◽  
Armin R Mikler ◽  
Samuel F Atkinson
Keyword(s):  
West Nile Virus ◽  
Environmental Factors ◽  
Least Squares ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Environmental Data ◽  
Weighted Regression ◽  
Least Squares Regression ◽  
West Nile ◽  
The Relationship

Background. The primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity. Methods. We examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model. Results. LSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted R2=0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjusted R2 = 0.71). Conclusions. The spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.

scholarly journals A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3070 ◽  
Author(s):  
Abhishek K. Kala ◽  
Chetan Tiwari ◽  
Armin R. Mikler ◽  
Samuel F. Atkinson
Keyword(s):  
West Nile Virus ◽  
Environmental Factors ◽  
Least Squares ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Environmental Data ◽  
Weighted Regression ◽  
Least Squares Regression ◽  
West Nile ◽  
The Relationship

BackgroundThe primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity.MethodsWe examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model.ResultsLSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjustedR2 = 0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjustedR2 = 0.71).ConclusionsThe spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.

Sign in / Sign up

Export Citation Format

Share Document