Concrete evidence &amp; geographically weighted regression: A regional analysis of wealth and the land cover in Massachusetts

Ogneva‐Himmelberger, Yelena; Pearsall, Hamil; Rakshit, Rahul

doi:10.1016/j.apgeog.2009.03.001

Cited by 96 publications

(38 citation statements)

References 28 publications

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“…This tool calculates a statistic (e.g. minimum, mean, sum, and standard deviation) for each individual zonal spatial feature, such as a UCI polygon, based on values from another raster dataset (LST in this case) (Sommer & Wade, 2006;Ogneva-Himmelberger et al, 2009). There are 153 UCIs used for the following analysis (Fig.…”

Section: Identification Of Uci and Its Intensitymentioning

confidence: 99%

A satellite image-based analysis of factors contributing to the green-space cool island intensity on a city scale

Kong

Yin

Wang

et al. 2014

Urban Forestry & Urban Greening

102

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Urban green spaces provide cooler microclimates and create localized urban cool islands and, as part of an adaption strategy to cope with future urban climate change, have been proposed as a means to mitigate the urban heat island effect. Numerous previous research papers have discussed green-space size, type, and vegetation density, as well as many other factors that might influence green-space cooling effects. However, little has been done with regard to exploring and quantifying the characteristics of the green-space cool island (UCI). It is also largely unknown whether or how the patterns of green space and land use, as well as the adjacent urban thermal environment, affect UCIs. In this paper, based on the satellite image, the land surface temperature (LST) was retrieved and the UCI was first identified, then the UCI intensity, one of the UCI characteristics, is defined and at last multiple linear regression models used to explore and quantify the combined effects of factors related to UCI intensity. The results show that the intensity differed between UCIs, and that it was correlated significantly with the extent of and mean temperature reduction associated with a UCI. Multiple linear regression analysis shows that UCI intensity was affected by areas of forest vegetation and its spatial arrangements, as well as by the composition of the cool island and its neighboring thermal environment. The study validated the suitability of using intensity as an indicator of the UCI. Identifying the UCI as a result of the green-space cooling 2 effect, will help in the management and planning of the spatial arrangement of green spaces in cities to mitigate the effects of the urban heat environment and help cities adapt to the climate change.

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Section: Identification Of Uci and Its Intensitymentioning

confidence: 99%

A satellite image-based analysis of factors contributing to the green-space cool island intensity on a city scale

Kong

Yin

Wang

et al. 2014

Urban Forestry & Urban Greening

102

View full text Add to dashboard Cite

show abstract

“…To tackle spatial heterogeneity, geographically weighted regression (GWR) is regularly used through locally estimating coefficients, rendering a contextual layer of coefficient estimates that vary over space. Examples of GWR span many fields, such as ecology, wealth and epidemics (Platt 2004, Ognev-Himmelberger et al 2009, Atkinson et al 2003, and Nagaya et al 2010, traffic count and crash count predictions across road networks (Zhao and Park 2004;Hadayeghi et al 2009), and land use (Páez 2006;Wang et al 2011). …”

Section: Motivations For Spatial Modelsmentioning

confidence: 98%

A Synthesis of Spatial Models for Multivariate Count Responses

Wang

Kockelman

Jamali

2017

Regional Research Frontiers - Vol. 2

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“…GWR is prone to spatial autocorrelation among variables and is an increasingly popular method of analyzing spatially dependent relationships in urban geographic analyses [34,35]. This study employed GWR to identify the spatial relationships between UGS and the degree of urban compaction within the study area.…”

Section: Geographically-weighted Regressionmentioning

confidence: 99%

Decision Making on Allocating Urban Green Spaces Based upon Spatially-Varying Relationships between Urban Green Spaces and Urban Compaction Degree

Chang

Chen

2015

Sustainability

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Abstract:The compact city is becoming a prevailing paradigm in the world to control urban sprawl and achieve a pattern of sustainable urban development. However, discussions of the area's overcrowded neighborhoods, its health problems, and the destruction of its green areas have inspired self-examination with respect to the compact city paradigm. High population density attracts even more residents and frequently renders the existing urban green space (UGS) insufficient for use as part of a living environment. Due to the unique benefits that these qualities confer, UGS allocation is now considered a significant contributing factor to urban livability. In addition, the UGS allocation may be different due to the presence of many spatial non-stationarity processes. Therefore, this study employs geographically-weighted regression (GWR) to explore the unique and spatially-explicit relationships between the degree of urban compaction and UGS within the Taipei metropolitan area. Maps summarizing the GWR results demonstrate that there is significantly insufficient UGS allocation in the central area, which consists mainly of Taipei City. Townships with higher parameters contain UGS levels that better meet the needs of their residents. Overall, the exploration of conceptualizing spatial heterogeneity of relationships between the degree of urban compaction and UGS can provide insightful analyses for decision-making on allocating UGS. OPEN ACCESSSustainability 2015, 7 13400

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Concrete evidence & geographically weighted regression: A regional analysis of wealth and the land cover in Massachusetts

Cited by 96 publications

References 28 publications

A satellite image-based analysis of factors contributing to the green-space cool island intensity on a city scale

A satellite image-based analysis of factors contributing to the green-space cool island intensity on a city scale

A Synthesis of Spatial Models for Multivariate Count Responses

Decision Making on Allocating Urban Green Spaces Based upon Spatially-Varying Relationships between Urban Green Spaces and Urban Compaction Degree

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