scholarly journals The Impact of Built Environment Characteristics on Energy Consumption Using Geographically Weighted Regression in Mashhad, Iran

2017 ◽  
Vol 10 (5) ◽  
pp. 198
Author(s):  
Bita Rezaeian ◽  
Mohammad Rahim Rahnama ◽  
Jafar Javan ◽  
Omid Ali Kharazmi
Keyword(s):  
Energy Consumption ◽  
Population Density ◽  
Fuel Consumption ◽  
Geographically Weighted Regression ◽  
Travel Behavior ◽  
Block Size ◽  
Positive Association ◽  
Weighted Regression ◽  
Main Element ◽  
The Impact

Concerns over rising fuel consumption have prompted research into the influences of built environments on travel behavior. On the basis of data from origin-destination(OD) travel survey data of Mashhad (74287 trip data in 2011) and using Geographically Weighted Regression, socio-demographic characteristics, are shown to be strongly and positively associated with the fuel consumption per capita (car ownership elasticity=0.347878); we also found a positive association between distance to center and designs that are not pedestrian friendly with fuel consumption (average block size=0.147489, distance to center =0.334953) Although the study demonstrates a moderately strong negative elasticity between population density and the fuel consumption(population density = -0.259335). It suggests that the largest energy consumption reductions would come from creating compact communities which have land-use diversity and more walkable areas with pedestrian cycling infrastructure around all of the stations along transit lines.In order to enhance a sustainable urban plan, the socio-economic driving factors should be considered as one of the main element of energy consumption as well.

Space Matters. The Group Threat Hypothesis Revisited with Geographically Weighted Regression. The Case of the NPD 2009 Electoral Success

2012 ◽  
Vol 41 (3) ◽  
Author(s):  
Celine Teney
Keyword(s):  
Geographically Weighted Regression ◽  
Positive Association ◽  
West Germany ◽  
Weighted Regression ◽  
Significant Positive Association ◽  
Group Threat ◽  
Electoral Success ◽  
Unemployment Rates ◽  
East And West Germany ◽  
New Research

SummaryA geographically weighted regression approach is used to assess the association of the electoral success of the NPD, an extreme right-wing political party, during the 2009 German federal election with levels of and changes in immigrant and unemployment rates. The results do not support the group threat hypothesis: the immigrant rate remains non-significant in large areas of West Germany while it shows a negative and significant relationship with NPD electoral success in most localities in East Germany as well as in Northern Bavaria. Instead, findings tend to confirm the contact hypothesis: a higher percentage of immigrants within an electoral district seems to lead to larger interethnic contact opportunities and thus to a lower proportion of votes for the NPD. The largest significant positive association of unemployment rate with NPD electoral results is observed with respect to localities that are situated around the former border between East and West Germany. The large regional variations in the effects of immigrant and unemployment rates point to different mechanisms which are at stake in the association of populist radical right success with unemployment and immigrant rates. These findings illustrate the importance of spatial variability and make the case for a broader new research agenda dedicated to exploring the mechanisms underlying spatial nonstationarity.

Parasite infection and host dynamics in a naturally fluctuating rodent population

2012 ◽  
Vol 90 (9) ◽  
pp. 1149-1160 ◽  
Author(s):  
J.C. Winternitz ◽  
M.J. Yabsley ◽  
S.M. Altizer
Keyword(s):  
Population Density ◽  
Experimental Studies ◽  
Host Density ◽  
Positive Association ◽  
Population Cycles ◽  
Host Population ◽  
Reproductive Status ◽  
Vole Cycles ◽  
Host Infection ◽  
The Impact

Parasites can both influence and be affected by host population dynamics, and a growing number of case studies support a role for parasites in causing or amplifying host population cycles. In this study, we examined individual and population predictors of gastrointestinal parasitism on wild cyclic montane voles ( Microtus montanus (Peale, 1848)) to determine if evidence was consistent with theory implicating parasites in population cycles. We sampled three sites in central Colorado for the duration of a multiannual cycle and recorded the prevalence and intensity of directly transmitted Eimeria Schneider, 1875 and indirectly transmitted cestodes from a total of 267 voles. We found significant associations between host infection status, individual traits (sex, age, and reproductive status) and population variables (site, trapping period, and population density), including a positive association between host density and cestode prevalence, and a negative association between host density and Eimeria prevalence. Both cestode and Eimeria intensity correlated positively with host age, reproductive status, and population density, but neither parasite was associated with poorer host condition. Our findings suggest that parasites are common in this natural host, but determining their potential to influence montane vole cycles requires future experimental studies and long-term monitoring to determine the fitness consequences of infection and the impact of parasite removal on host dynamics.

Government failures and non-performing loans in European countries: a spatial approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ferdinando Ofria ◽  
Massimo Mucciardi
Keyword(s):  
Public Debt ◽  
Geographically Weighted Regression ◽  
European Countries ◽  
Weighted Regression ◽  
Second Step ◽  
Ols Regression ◽  
Content Type ◽  
The Impact ◽  
The Relationship ◽  
The Eu

PurposeThe purpose is to analyze the spatially varying impacts of corruption and public debt as % of GDP (proxies of government failures) on non-performing loans (NPLs) in European countries; comparing two periods: one prior to the crisis of 2007 and another one after that. The authors first modeled the NPLs with an ordinary lest square (OLS) regression and found clear evidence of spatial instability in the distribution of the residuals. As a second step, the authors utilized the geographically weighted regression (GWR) to explore regional variations in the relationship between NPLs and the proxies of “Government failures”.Design/methodology/approachThe authors first modeled the NPL with an OLS regression and found clear evidence of spatial instability in the distribution of the residuals. As a second step, the author utilized the Geographically Weighted Regression (GWR) (Fotheringham et al., 2002) to explore regional variations in the relationship between NPLs and proxies of “Government failures” (corruption and public debt as % of GDP).FindingsThe results confirm that corruption and public debt as % of GDP, after the crisis of 2007, have affected significantly on NPLs of the EU countries and the following countries neighboring the EU: Switzerland, Iceland, Norway, Montenegro, and Turkey.Originality/valueIn a spatial prospective, unprecedented in the literature, this research focused on the impact of corruption and public debt as % of GDP on NPLs in European countries. The positive correlation, as expected, between public debt and NPLs highlights that fiscal problems in Eurozone countries have led to an important rise of problem loans. The impact of institutional corruption on NPLs reports that the higher the corruption, the higher is the level of NPLs.

scholarly journals Commuting for work and neighbourhood energy consumption

2021 ◽  
Author(s):  
Dil Rowshan
Keyword(s):  
Energy Consumption ◽  
Comparative Analysis ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Travel Behavior ◽  
Active Transportation ◽  
Before And After ◽  
Motorized Vehicles ◽  
The Impact ◽  
Growth Centres

This study aimed to explore the impact of the Places to Grow Plan 2006 on travel behavior of the work commuters living in GTHA. A comparative analysis was done between the year 2001 and 2011 which represent the situations five year before and after the implementation of the Plan. Data were collected from Transportation Tomorrow Survey. The study revealed that in 2011, energy consumption by motorized vehicles increased in the Traffic Assessment Zones of GTHA around the Growth Centres designated by the Places to Grow Plan. Active transportation increased mainly in Toronto in 2011. It is apprehended that the intensification strategy of the Places to Grow Plan contributed in increasing the energy consumption of work commuters either by increasing the number of trips or length of trips made by motorized vehicles (including cars and different forms of transit) which also affect the Greenhouse Gas emissions in the atmosphere.

scholarly journals Valuing Environmental Amenities across Space: A Geographically Weighted Regression of Housing Preferences in Greenville County, SC

Land ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 147
Author(s):  
Hiebert ◽  
Allen
Keyword(s):  
Geographically Weighted Regression ◽  
City Planning ◽  
Spatial Dynamics ◽  
Ordinary Least Squares ◽  
Hedonic Model ◽  
Weighted Regression ◽  
Hedonic Price ◽  
Environmental Amenities ◽  
Forested Areas ◽  
The Impact

As global consumption and development rates continue to grow, there will be persistent stress placed on public goods, namely environmental amenities. Urban sprawl and development places pressure on forested areas, as they are often displaced or degraded in the name of economic development. This is problematic because environmental amenities are valued by the public, but traditional market analysis typically obscures the value of these goods and services that are not explicitly traded in a market setting. This research examines the non-market value of environmental amenities in Greenville County, SC, by utilizing a hedonic price model of home sale data in 2011. We overlaid home sale data with 2011 National Land Cover Data to estimate the value of a forest view, proximity to a forest, and proximity to agriculture on the value of homes. We then ran two regression models, an ordinary least squares (OLS) and a geographically weighted regression to compare the impact of space on the hedonic model variables. Results show that citizens in Greenville County are willing to pay for environmental amenities, particularly views of a forest and proximity to forested and agricultural areas. However, the impact and directionality of these variables differ greatly across space. These findings suggest the need for an integration of spatial dynamics into environmental valuation estimates to inform conservation policy and intentional city planning.

scholarly journals Commuting for work and neighbourhood energy consumption

2021 ◽  
Author(s):  
Dil Rowshan
Keyword(s):  
Energy Consumption ◽  
Comparative Analysis ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Travel Behavior ◽  
Active Transportation ◽  
Before And After ◽  
Motorized Vehicles ◽  
The Impact ◽  
Growth Centres

This study aimed to explore the impact of the Places to Grow Plan 2006 on travel behavior of the work commuters living in GTHA. A comparative analysis was done between the year 2001 and 2011 which represent the situations five year before and after the implementation of the Plan. Data were collected from Transportation Tomorrow Survey. The study revealed that in 2011, energy consumption by motorized vehicles increased in the Traffic Assessment Zones of GTHA around the Growth Centres designated by the Places to Grow Plan. Active transportation increased mainly in Toronto in 2011. It is apprehended that the intensification strategy of the Places to Grow Plan contributed in increasing the energy consumption of work commuters either by increasing the number of trips or length of trips made by motorized vehicles (including cars and different forms of transit) which also affect the Greenhouse Gas emissions in the atmosphere.

scholarly journals Examining the driving factors of industrial CO2 emissions in Chinese cities using geographically weighted regression model

2021 ◽  
Author(s):  
Huiping Wang ◽  
Xueying Zhang
Keyword(s):  
Spatial Heterogeneity ◽  
Geographically Weighted Regression ◽  
Service Industry ◽  
Industrial Sector ◽  
Weighted Regression ◽  
Impact Factors ◽  
Geographically Weighted Regression Model ◽  
Level Of Economic Development ◽  
Investment In Fixed Assets ◽  
The Impact

Abstract The industrial sector is the sector with the largest CO2 emissions, and to reduce overall CO2 emissions, analysis of the impact factors holds significance. Based on the 2015 industrial CO2 emissions of 282 cities in China combined with economic and social data, and a geographically weighted regression (GWR) model, we analysed the characteristics of the spatial distribution of CO2 emissions and the influencing factors of spatial heterogeneity. The results show that China's urban industrial CO2 emissions present a significant spatial agglomeration state that includes Shandong, Beijing, Tianjin, Shanghai, Zhejiang, and Jiangsu, and the core of the coastal areas form a high-high (H-H) concentration; a low-low aggregation (L-L) is formed in less developed areas such as Guizhou, Yunnan, Sichuan and Guangxi. The influence of various factors on industrial CO2 emissions has significant spatial heterogeneity. The Industrial scale, industry share of GDP, and share of the service industry in GDP are factors that promote industrial CO2 emissions. The technological innovation, population density, and social investment in fixed assets are important factors that inhibit industrial CO2 emissions, but their impact on industrial CO2 emissions shows spatial differences. In contrast, the level of economic development, foreign direct investment, financial development and government intervention have a two-way impact on industrial CO2 emissions.

scholarly journals Spatial Analysis Of Foreign Migration In Poland In 2012 Using Geographically Weighted Regression

2014 ◽  
Vol 17 (4) ◽  
pp. 137-154 ◽  
Author(s):  
Karolina Lewandowska-Gwarda
Keyword(s):  
Geographically Weighted Regression ◽  
Graphical Representation ◽  
Emigration And Immigration ◽  
Weighted Regression ◽  
Model Parameters ◽  
Population Changes ◽  
Principal Influence ◽  
Gis Tools ◽  
Spatially Varying ◽  
The Impact

Migration has a principal influence on countries’ population changes. Thus, the issues connected with the causes, effects and directions of people’s movements are a common topic of political and academic discussions. The aim of this paper is to analyse the spatial distribution of officially registered foreign migration in Poland in 2012. GIS tools are implemented for data visualization and statistical analysis. Geographically weighted regression (GWR) is used to estimate the impact of unemployment, wages and other socioeconomic variables on the foreign emigration and immigration measure. GWR provides spatially varying estimates of model parameters that can be presented on a map, giving a useful graphical representation of spatially varying relationships.  

scholarly journals Application of Remote Sensing, GIS and Machine Learning with Geographically Weighted Regression in Assessing the Impact of Hard Coal Mining on the Natural Environment

2020 ◽  
Vol 12 (22) ◽  
pp. 9338
Author(s):  
Anna Kopeć ◽  
Paweł Trybała ◽  
Dariusz Głąbicki ◽  
Anna Buczyńska ◽  
Karolina Owczarz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Coal Mining ◽  
Geographically Weighted Regression ◽  
Machine Learning Algorithms ◽  
Weighted Regression ◽  
Hard Coal ◽  
Mining Operations ◽  
Hard Coal Mining ◽  
The Impact

Mining operations cause negative changes in the environment. Therefore, such areas require constant monitoring, which can benefit from remote sensing data. In this article, research was carried out on the environmental impact of underground hard coal mining in the Bogdanka mine, located in the southeastern Poland. For this purpose, spectral indexes, satellite radar interferometry, Geographic Information System (GIS) tools and machine learning algorithms were utilized. Based on optical, radar, geological, hydrological and meteorological data, a spatial model was developed to determine the statistical significance of the selected factors’ individual impact on the occurrence of wetlands. Obtained results show that Normalized Difference Vegetation Index (NDVI) change, terrain height, groundwater level and terrain displacement had a considerable influence on the occurrence of wetlands in the research area. Moreover, the machine learning model developed using the Random Forest algorithm allowed for an efficient determination of potential flooding zones based on a set of spatial variables, correctly detecting 76% area of wetlands. Finally, the GWR (Geographically Weighted Regression (GWR) modelling enabled identification of local anomalies of selected factors’ influence on the occurrence of wetlands, which in turn helped to understand the causes of wetland formation.

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