Selecting Prices Determinants and Including Spatial Effects in Peer-to-Peer Accommodation

Peer-to-peer accommodation has grown significantly during the last decades, supported, in part, by digital platforms. These websites make available a wide range of information intended to help the customers’ decision. All these factors, in addition to the property location, may therefore influence rental price. This paper proposes different procedures for an efficient selection of a high number of price determinants in peer-to-peer accommodation when applying the perspective of the geographically weighted regression. As a case study, these procedures have been used to find the factors affecting the rental price of properties advertised on Airbnb in Gran Canaria (Spain). The results show that geographically weighted regression obtains better indicators of goodness of fit than the traditional ordinary least squares method, making it possible to identify those attributes influencing price and how their effect varies according to property locations. Moreover, the results also show that the selection procedures working directly on geographically weighted regression obtain better results than those that take good global solutions as their starting point.

Download Full-text

The Distribution of Rural Accommodation in Extremadura, Spain-between the Randomness and the Suitability Achieved by Means of Regression Models (OLS vs. GWR)

Sustainability ◽

10.3390/su12114737 ◽

2020 ◽

Vol 12 (11) ◽

pp. 4737

Author(s):

José-Manuel Sánchez-Martín ◽

José-Luis Gurría-Gascón ◽

Juan-Ignacio Rengifo-Gallego

Keyword(s):

Spatial Statistics ◽

Geographically Weighted Regression ◽

Regression Models ◽

Spatial Relations ◽

Ordinary Least Squares ◽

Weighted Regression ◽

Independent Variables ◽

Starting Point ◽

Regression Techniques ◽

Random Nature

There are multiple types of regression, the essential task of which is the obtaining of models which, starting from a set of regressive values, are capable of finding explanations for the variability of a dependent. However, in many cases, the territorial criterion is not considered to be a noteworthy factor of analysis, owing to which this deficiency has encouraged the arising of spatial statistics. Nevertheless, given the variety of regressions, it is not clear which can best be adapted to the analysis of tourism. In this sector, when the supply of accommodation is analysed, it is understood that it must be strongly related to the presence of resources, owing to which it has been taken as an example of an application between two differentiated regression techniques: ordinary least squares (OLS) and geographically weighted regression (GWR), with the objective of determining which of the two is best adapted to this type of analysis. The model has been drawn up based on various methods, although it has been shown that it is more efficient to resort to the declared preferences of the rural tourist, with the starting point being a survey made of the tourists. These aspects have been taken as independent variables with the aim of explaining the distribution of accommodation establishments. The results obtained show that the configuration of the spatial relations between the variable included in the model encourages the explanation of the latter, owing to which GWR is much more suitable than OLS, even when a system as complex as the distribution of accommodation establishments is analysed. Likewise, it is noteworthy that the distribution of accommodation does not also follow the guidelines marked by demand; far from it, it appears that in some areas, it is of a random nature.

Download Full-text

Modeling spatial determinates of teenage pregnancy in Ethiopia; geographically weighted regression

BMC Women s Health ◽

10.1186/s12905-021-01400-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Seblewongel Tigabu ◽

Alemneh Mekuriaw Liyew ◽

Bisrat Misganaw Geremew

Keyword(s):

Spatial Distribution ◽

Geographically Weighted Regression ◽

Teenage Pregnancy ◽

Wealth Quintile ◽

Ordinary Least Squares ◽

Weighted Regression ◽

Contraceptive Methods ◽

Hotspot Analysis ◽

Increased Risk ◽

Risk Areas

Abstract Background In developing countries, 20,000 under 18 children give birth every day. In Ethiopia, teenage pregnancy is high with Afar and Somalia regions having the largest share. Even though teenage pregnancy has bad maternal and child health consequences, to date there is limited evidence on its spatial distribution and driving factors. Therefore, this study is aimed to assess the spatial distribution and spatial determinates of teenage pregnancy in Ethiopia. Methods A secondary data analysis was conducted using 2016 EDHS data. A total weighted sample of 3381 teenagers was included. The spatial clustering of teenage pregnancy was priorly explored by using hotspot analysis and spatial scanning statistics to indicate geographical risk areas of teenage pregnancy. Besides spatial modeling was conducted by applying Ordinary least squares regression and geographically weighted regression to determine factors explaining the geographic variation of teenage pregnancy. Result Based on the findings of exploratory analysis the high-risk areas of teenage pregnancy were observed in the Somali, Afar, Oromia, and Hareri regions. Women with primary education, being in the household with a poorer wealth quintile using none of the contraceptive methods and using traditional contraceptive methods were significant spatial determinates of the spatial variation of teenage pregnancy in Ethiopia. Conclusion geographic areas where a high proportion of women didn’t use any type of contraceptive methods, use traditional contraceptive methods, and from households with poor wealth quintile had increased risk of teenage pregnancy. Whereas, those areas with a higher proportion of women with secondary education had a decreased risk of teenage pregnancy. The detailed maps of hotspots of teenage pregnancy and its predictors had supreme importance to policymakers for the design and implementation of adolescent targeted programs.

Download Full-text

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

10.21203/rs.2.13021/v3 ◽

2020 ◽

Author(s):

Marlvin Anemey Tewara ◽

Liu Yunxia ◽

Weiqiang Ling ◽

Binang Helen Barong ◽

Prisca Ngetemalah Mbah-Fongkimeh ◽

...

Keyword(s):

Environmental Factors ◽

Spatial Variability ◽

Geographically Weighted Regression ◽

Environmental Variables ◽

Vegetation Index ◽

Spatial Association ◽

Ordinary Least Squares ◽

Variable Coefficients ◽

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 R2 = 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 R2= 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.

Download Full-text

Modelling Housing Rents Using Spatial Autoregressive Geographically Weighted Regression: A Case Study in Cracow, Poland

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9060346 ◽

2020 ◽

Vol 9 (6) ◽

pp. 346

Author(s):

Mateusz Tomal

Keyword(s):

Geographically Weighted Regression ◽

Rental Housing ◽

Ordinary Least Squares ◽

Weighted Regression ◽

Spatial Econometric ◽

Econometric Methods ◽

Depth Analysis ◽

Sar Model ◽

Internet Platform ◽

Spatial Autoregressive

The proportion of tenants will undoubtedly rise in Poland, where at present, the ownership housing model is very dominant. As a result, the rental housing market in Poland is currently under-researched in comparison with owner-occupancy. In order to narrow this research gap, this study attempts to identify the determinants affecting rental prices in Cracow. The latter were obtained from the internet platform otodom.pl using the web scraping technique. To identify rent determinants, ordinary least squares (OLS) regression and spatial econometric methods were used. In particular, traditional spatial autoregressive model (SAR) and spatial autoregressive geographically weighted regression (GWR-SAR) were employed, which made it possible to take into account the spatial heterogeneity of the parameters of determinants and the spatially changing spatial autocorrelation of housing rents. In-depth analysis of rent determinants using the GWR-SAR model exposed the complexity of the rental market in Cracow. Estimates of the above model revealed that many local markets can be identified in Cracow, with different factors shaping housing rents. However, one can identify some determinants that are ubiquitous for almost the entire city. This concerns mainly the variables describing the area of the flat and the age of the building. Moreover, the Monte Carlo test indicated that the spatial autoregressive parameter also changes significantly over space.

Download Full-text

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

Land ◽

10.3390/land8100147 ◽

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.

Download Full-text

Spatial Variations in Fertility of South Korea: A Geographically Weighted Regression Approach

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8060262 ◽

2019 ◽

Vol 8 (6) ◽

pp. 262 ◽

Cited By ~ 2

Author(s):

Myunggu Jung ◽

Woorim Ko ◽

Yeohee Choi ◽

Youngtae Cho

Keyword(s):

South Korea ◽

Geographically Weighted Regression ◽

Population Policy ◽

Local Population ◽

Sociodemographic Factors ◽

Ordinary Least Squares ◽

Spatial Variations ◽

Low Fertility ◽

Weighted Regression

South Korea has witnessed a remarkable decline in birth rates in the last few decades. Although there has been a large volume of literature exploring the determinants of low fertility in South Korea, studies on spatial variations in fertility are scarce. This study compares the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to investigate the potential role of the spatially heterogeneous response of the total fertility rate (TFR) to sociodemographic factors. The study finds that the relationships between sociodemographic factors and TFRs in South Korea vary across 252 sub-administrative areas in terms of both magnitude and direction. This study therefore demonstrates the value of using spatial analysis for providing evidence-based local-population policy options in pursuit of a fertility rebound in South Korea.

Download Full-text

Spatial Heterogeneity, Broadband, and New Firm Formation

Quality Innovation Prosperity ◽

10.12776/qip.v20i3.791 ◽

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

Purpose: This paper examines the spatial heterogeneity associated with broadband Internet and new firm formation in a number of U.S. states.Methodology/Approach: Both ordinary least-squares regression and Geographically Weighted Regression are used for the estimation purpose.Findings: 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.Originality/Value of paper: 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.

Download Full-text

A Semi-Parametric Geographically Weighted Regression Approach to Exploring Driving Factors of Fractional Vegetation Cover: A Case Study of Guangdong

Sustainability ◽

10.3390/su12187512 ◽

2020 ◽

Vol 12 (18) ◽

pp. 7512

Author(s):

Yuhao Jin ◽

Han Zhang ◽

Yuchao Yan ◽

Peitong Cong

Keyword(s):

Geographically Weighted Regression ◽

Vegetation Cover ◽

Southern China ◽

Ordinary Least Squares ◽

Driving Factors ◽

Weighted Regression ◽

Impact Factors ◽

Negative Effects ◽

Vegetation Growth ◽

Fractional Vegetation Cover

Ecological degradation caused by rapid urbanisation has presented great challenges in southern China. Fractional vegetation cover (FVC) has long been the most common and sensitive index to describe vegetation growth and to monitor vegetation degradation. However, most of the studies have failed to adequately explore the complexity of the relationship between fractional vegetation cover (FVC) and impact factors. In this research, we first constructed a Semi-parametric Geographically Weighted Regression (SGWR) model to analyse both the stationary and nonstationary spatial relationships between FVC and driving factors in Guangdong province in southern China on a county level. Then, climate, topographic, land cover, and socio-economic factors were introduced into the model to distinguish impacts on FVC from 2000–2015. Results suggest that the positive and negative effects of rainfall and elevation coefficients alternated, and local urban land and population estimates indicated a negative association between FVC and the modelled factors in each period. The SGWR FVC make significantly improves performance of the geographically weighted regression and ordinary least squares models, with adjusted R2 higher than 0.78. The findings of this research demonstrated that, although urbanisation in the Pearl River Delta in Guangdong has encroached on the regional vegetation cover, the total vegetation area remained unchanged with the implementation of protection policies and regulations.

Download Full-text

A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 2. Spatial Association and Model Specification Tests

Environment and Planning A Economy and Space ◽

10.1068/a34133 ◽

2002 ◽

Vol 34 (5) ◽

pp. 883-904 ◽

Cited By ~ 83

Author(s):

Antonio Páez ◽

Takashi Uchida ◽

Kazuaki Miyamoto

Keyword(s):

Spatial Data ◽

Geographically Weighted Regression ◽

Spatial Association ◽

Global Perspective ◽

Model Specification ◽

Weighted Regression ◽

Spatial Effects ◽

Modeling Framework ◽

Spatial Error ◽

Special Cases

Spatial association effects, perhaps the most important concern in the analysis of spatial data, have been amply studied from a global perspective in the exploratory and modeling domains, and more recently also from a local perspective in the realm of exploratory data analysis. In a local modeling framework, however, the issue of how to detect and model spatial association by using geographically weighted regression (GWR) remains largely unresolved. In this paper we exploit a recent development that casts GWR as a model of locational heterogeneity, to formulate a general model of spatial effects that includes as special cases GWR with a spatially lagged objective variable and GWR with spatial error autocorrelation. The approach also permits the derivation of formal tests against several forms of model misspecification, including locational heterogeneity in global models, and spatial error autocorrelation in GWR models. Application of these results is exemplified with a case study.

Download Full-text

Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic

ICES Journal of Marine Science ◽

10.1093/icesjms/fsp224 ◽

2009 ◽

Vol 67 (1) ◽

pp. 145-154 ◽

Cited By ~ 38

Author(s):

Matthew J. S. Windle ◽

George A. Rose ◽

Rodolphe Devillers ◽

Marie-Josée Fortin

Keyword(s):

Survey Data ◽

Geographically Weighted Regression ◽

Goodness Of Fit ◽

Generalized Additive Models ◽

Additive Models ◽

Weighted Regression ◽

Snow Crab ◽

Implicit Assumption ◽

Northwest Atlantic ◽

Goodness Of Fit Tests

Abstract Windle, M. J. S., Rose, G. A., Devillers, R., and Fortin, M-J. 2010. Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic. – ICES Journal of Marine Science, 67: 145–154. Analyses of fisheries data have traditionally been performed under the implicit assumption that ecological relationships do not vary within management areas (i.e. assuming spatially stationary processes). We question this assumption using a local modelling technique, geographically weighted regression (GWR), not previously used in fisheries analyses. Outputs of GWR are compared with those of global logistic regression and generalized additive models (GAMs) in predicting the distribution of northern cod off Newfoundland, Canada, based on environmental (temperature and distance from shore) and biological factors (snow crab and northern shrimp) from 2001. Results from the GWR models explained significantly more variability than the global logistic and GAM regressions, as shown by goodness-of-fit tests and a reduction in the spatial autocorrelation of model residuals. GWR results revealed spatial regions in the relationships between cod and explanatory variables and that the significance and direction of these relationships varied locally. A k-means cluster analysis based on GWR t-values was used to delineate distinct zones of species–environment relationships. The advantages and limitations of GWR are discussed in terms of potential application to fisheries ecology.

Download Full-text