Multiscale geographically and temporally weighted regression: exploring the spatiotemporal determinants of housing prices

Wu, Chao; Ren, Fei; Hu, Wei; Du, Qingyun

doi:10.1080/13658816.2018.1545158

Cited by 97 publications

(41 citation statements)

References 52 publications

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“…For L ih (h 1 , h 2 ), the fitting value of the ih-th point is obtained by removing the observation information of the ihth point under the given space-time window width h 1 and h 2 , and the minimum value of CV is the best parameter of the space-time window width. If the smoothness of coefficient function varies greatly, the variable window width method should be used to improve the fitting accuracy [17,18]…”

Section: Local Linear Estimation Methods Of Regression Coefficientmentioning

confidence: 99%

A Spatiotemporal Deformation Modelling Method Based on Geographically and Temporally Weighted Regression

Yang

Dai

Santerre

et al. 2019

Mathematical Problems in Engineering

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The geographically and temporally weighted regression (GTWR) model is a dynamic model which considers the spatiotemporal correlation and the spatiotemporal nonstationarity. Taking into account these advantages, we proposed a spatiotemporal deformation modelling method based on GTWR. In order to further improve the modelling accuracy and efficiency and considering the application characteristics of deformation modelling, the inverse window transformation method is used to search the optimal fitting window width and furthermore the local linear estimation method is used in the fitting coefficient function. Moreover, a comprehensive model for the statistical tests method is proposed in GTWR. The results of a dam deformation modelling application show that the GTWR model can establish a unified spatiotemporal model which can represent the whole deformation trend of the dam and furthermore can predict the deformation of any point in time and space, with stronger flexibility and applicability. Finally, the GTWR model improves the overall temporal prediction accuracy by 43.6% compared to the single-point time-weighted regression (TWR) model.

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Section: Local Linear Estimation Methods Of Regression Coefficientmentioning

confidence: 99%

A Spatiotemporal Deformation Modelling Method Based on Geographically and Temporally Weighted Regression

Yang

Dai

Santerre

et al. 2019

Mathematical Problems in Engineering

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“…is the estimated coefficient of the ith sample for the kth variable; β 0 (u i , v i ) is the intercept term; ε i is the error term; and y is the dependent variable, representing the landscape metrics. Some scholars have noted that the bandwidth directly influences the scale variations of the estimated parameters [36,37,58,59]. GWR uses a uniform bandwidth for all independent variables to control the effects of the distance decay rate, but as a result, it is difficult to capture the different levels of spatial heterogeneity.…”

Section: Multiscale Gwrmentioning

confidence: 99%

“…Specifically, the spatially varying processes associated with the modeled relationships between the landscape and various driving factors often occur at different spatial scales. The degree or level of spatial heterogeneity may vary given different relationships between the landscape and urbanization factors [36,37]. For example, an increase in landscape sensitivity might be a function of both global climate change and inappropriate local land reclamation [38].…”

Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Analysis of the Effects of Urbanization’s Socio-Economic Factors on Landscape Patterns Considering Operational Scales

Liu

Chen

et al. 2020

Sustainability

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Landscape patterns are significantly affected during the urbanization process. Identifying the spatiotemporal impacts of urbanization’s socio-economic factors on landscape patterns is very important and can provide scientific evidence to support urban ecological management and guide managers to establish appropriate sustainability policies. This article applies multiscale geographically weighted regression (MGWR) to reveal the relationships between landscape patterns and the socio-economic factors of urbanization in Shenzhen, China, from 2000 to 2015, in five-year intervals. MGWR is a powerful extension of geographically weighted regression (GWR) that can not only reveal spatial heterogeneity patterns but also measure the operational scale of covariates. The empirical results indicate that MGWR is superior to GWR. Furthermore, the changes in operational scale represented by the spatial bandwidth of MGWR in different years reflect temporal changes in the spatial relationships of given factors, which is significant information for urban studies. These multiscale relationships between landscape patterns and the socio-economic factors of urbanization, revealed via MGWR, are useful for strategic planning around urban dynamic development and land resource and ecological landscape management. The results can provide additional insight into landscape and urbanization studies from a multiscale perspective, which is important for local, regional, and global urban planning.

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“…Advancement in GIS & RS science integrated with GWR has enabled statistical analysis on spatially distributed data across a certain geographical area [72]. Furthermore, GWR provides an opportunity to understand the difference of regression parameters and model performance across the study area [73] [74]. This study adopted the Gaussian adaptive kernel type to understand the relationship between vegetation cover as dependent variable and independent variables namely BE and agriculture.…”

Section: Geographically Weighted Regression Analysismentioning

confidence: 99%

Analysis of Socio-Ecological Impacts of Built Environment at Dar es Salaam Metropolitan Coastline, Tanzania

Manyama¹,

Hepelwa²,

Nahonyo³

2019

JSS

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This paper analyzed and mapped the impact of built environment (BE) on socio-ecological services along Dar es Salaam metropolitan coastline. In the period of 1995-2016, burgeoning population exacerbated high rate of construction processes and activities. Such anthropic initiatives affect the benefits and values delivered by landscape wetlands, estuaries, beach areas, open space and greenery patches in Dar es Salaam coastline. Motivated to understanding socio-ecological impacts (SEIs) of BE along the coastline; the study applied focus group discussions (FGDs) method to identify SEIs, and their category as either positive or negative. Using analytical hierarchy process (AHP) generated weight to each SEIs; thereafter combined that weight with landuse landcover (LULC) change statistics of each of the 67 wards involved in the study. Mapping results of geographically weighted regression and inverse distance weighting (IDW) interpolation (ArcMap v10.3) displayed the variables relationship magnitudes and distribution in positive and negative SEIs for year 1995, 2005 and 2016. logical and social systems [4]. Such intertwining feedbacks results in complex systemic relationship, termed as socio-ecological system (SES) [5] [6] [7].Existence of human desire for improved economic and social services in SES are external stimuli to coastline resources utilization hence degradation of ecological system [5]. Studies [8] explain that coastline, also referred to as coastal zone, ranges from 60 to 200 kilometre inland and 50 m depth of ocean water system. In between exists highly fertile areas like continental shelf, estuaries and wetlands at both low and high tide periods [9]; these fertile characters are a reason for dominant human perturbation in such areas [10] [11]. While [12] justified the relationship of anthropogenic activities and coastal zone, little has done to disclose the existing link of BE and its impact on SES.BE as a product of construction activities [13], in the search for suitable and comfortable places for recreation, residence and work; impacts the landscape by aggravating vegetation cover clearance, waste generation and permanent consumption of a piece of land [14] [15]. The habitat loss and fragmentation caused by such impacts account significantly for biodiversity loss. According to [16], the functioning of biodiversity species are greatly the determinant of the quality and quantity of services derived from SES. Thus, state of landscape loss or gain of such biological component is a socio-ecological impact (SEI) that alters SES functioning capacity [17]. Scholars such as [18] and [19] typify that some of the positive SEIs include cooling, shadow, air filtration, climate variability and flood prevention by trees; pollinations by avian and arthropods species as well as medicines and food from both fauna and flora.However, irreversible anthropic construction activities provoke landscape environmental challenges as result of megacities development processes and activities [20] [21]. In the search for quality prod...

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Multiscale geographically and temporally weighted regression: exploring the spatiotemporal determinants of housing prices

Cited by 97 publications

References 52 publications

A Spatiotemporal Deformation Modelling Method Based on Geographically and Temporally Weighted Regression

A Spatiotemporal Deformation Modelling Method Based on Geographically and Temporally Weighted Regression

A Spatiotemporal Analysis of the Effects of Urbanization’s Socio-Economic Factors on Landscape Patterns Considering Operational Scales

Analysis of Socio-Ecological Impacts of Built Environment at Dar es Salaam Metropolitan Coastline, Tanzania

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