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Spatial Data Science in Guiding Climate Conscious Spatial Planning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Spatial Data Science in Guiding Climate Conscious Spatial Planning./
Author:	Zhou, Yulun.
Description:	1 online resource (217 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
Subject:	Geographic information science. -
Online resource:	click for full text (PQDT)
ISBN:	9798492728427

Spatial Data Science in Guiding Climate Conscious Spatial Planning.
Zhou, Yulun.

Spatial Data Science in Guiding Climate Conscious Spatial Planning. - 1 online resource (217 pages)

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2021.

Includes bibliographical references

One of the most concerning consequences arising from the global urbanization process is the excessive warming in cities and elevated public health risks due to heat exposures. In many global cities, urban areas can be up to 10°C warmer compared to their surrounding rural areas. Such excessive heat increases human exposure to high ambient temperatures, elevates citizen health risks, and increases the demand for cooling energy. Extensive scientific discoveries during the past century have attributed excessive urban warming to the Urban Heat Island (UHI) effect with evident associations highlighting artificial land-use changes, from natural surfaces to impervious ones, as the leading driver. Nevertheless, we note that few studies have advanced the scientific discoveries to mitigation plans, mostly due to the lack of approach to search for the optimal design with adequate accountability for spatio-temporal heterogeneities in climate responses. Despite the evident associations between the amount of growth and the magnitude of warming, it is difficult for cities to implement anti-growth strategies due to the significant economic cost. The challenge is especially profound in high-density cities like Shenzhen where land is among the most scarce resources highly needed to shelter the living and production activities of residents. Instead of limiting growth, existing researches connecting urban warming with urban form have indicated the possible mitigation effectiveness of climate-conscious urban growth planning (CUGP) for urban warming. The CUGP allows the same amount of new urban lands while being able to reduce their associated warming impacts by optimizing the spatial arrangements of future urban growth in a climate-conscious manner. No existing research, to the authors' knowledge, has systematically investigated the likelihood of the CUGP as a useful measure for mitigating excessive urban heat and quantified its cost-effectiveness, which has become the ultimate objective of this thesis. This analysis sets to tackle two major challenges which have prevented the systematic use of geospatial artificial intelligence in urban climate mitigation. The first goal is to advance geospatial optimization methods in response to the requirements of climate-conscious planning of natural resources. It is known that spatial land-use allocation problems can be formulated and solved as (multi-objective) combinatorial optimizations. However, spatial land-use allocation problems considering environmental objectives, or what we call the climate-conscious spatial planning (CCSP) problems, are different and challenging to solve. Such problems are called expensive optimization problems in operations research since accurate and reliable environmental impact assessments of candidate plans throughout the optimization require minutes, hours, or even days to complete, making the optimization computationally expensive to converge within an acceptable time. Moreover, climate-conscious urban growth planning contains multiple objectives and feasibility constraints, operates in discrete and sophisticated solution space, and so the convergence can be easily distracted by local optima. We first propose a framework for CCSP by pairing spatial optimization models with both spatial-explicit and spatial-implicit fitness functions that enable fast and accurate environmental impact assessments, integrating spatio-temporal varying environmental responses. Through a simple and intuitive apple game, we demonstrate the necessity and impacts of integrating spatial-explicit fitness functions in classic spatial optimization models.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798492728427Subjects--Topical Terms:

1372545
Geographic information science.
Subjects--Index Terms:

Spatial data scienceIndex Terms--Genre/Form:

554714
Electronic books.

Spatial Data Science in Guiding Climate Conscious Spatial Planning.
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504 $a Includes bibliographical references
520 $a One of the most concerning consequences arising from the global urbanization process is the excessive warming in cities and elevated public health risks due to heat exposures. In many global cities, urban areas can be up to 10°C warmer compared to their surrounding rural areas. Such excessive heat increases human exposure to high ambient temperatures, elevates citizen health risks, and increases the demand for cooling energy. Extensive scientific discoveries during the past century have attributed excessive urban warming to the Urban Heat Island (UHI) effect with evident associations highlighting artificial land-use changes, from natural surfaces to impervious ones, as the leading driver. Nevertheless, we note that few studies have advanced the scientific discoveries to mitigation plans, mostly due to the lack of approach to search for the optimal design with adequate accountability for spatio-temporal heterogeneities in climate responses. Despite the evident associations between the amount of growth and the magnitude of warming, it is difficult for cities to implement anti-growth strategies due to the significant economic cost. The challenge is especially profound in high-density cities like Shenzhen where land is among the most scarce resources highly needed to shelter the living and production activities of residents. Instead of limiting growth, existing researches connecting urban warming with urban form have indicated the possible mitigation effectiveness of climate-conscious urban growth planning (CUGP) for urban warming. The CUGP allows the same amount of new urban lands while being able to reduce their associated warming impacts by optimizing the spatial arrangements of future urban growth in a climate-conscious manner. No existing research, to the authors' knowledge, has systematically investigated the likelihood of the CUGP as a useful measure for mitigating excessive urban heat and quantified its cost-effectiveness, which has become the ultimate objective of this thesis. This analysis sets to tackle two major challenges which have prevented the systematic use of geospatial artificial intelligence in urban climate mitigation. The first goal is to advance geospatial optimization methods in response to the requirements of climate-conscious planning of natural resources. It is known that spatial land-use allocation problems can be formulated and solved as (multi-objective) combinatorial optimizations. However, spatial land-use allocation problems considering environmental objectives, or what we call the climate-conscious spatial planning (CCSP) problems, are different and challenging to solve. Such problems are called expensive optimization problems in operations research since accurate and reliable environmental impact assessments of candidate plans throughout the optimization require minutes, hours, or even days to complete, making the optimization computationally expensive to converge within an acceptable time. Moreover, climate-conscious urban growth planning contains multiple objectives and feasibility constraints, operates in discrete and sophisticated solution space, and so the convergence can be easily distracted by local optima. We first propose a framework for CCSP by pairing spatial optimization models with both spatial-explicit and spatial-implicit fitness functions that enable fast and accurate environmental impact assessments, integrating spatio-temporal varying environmental responses. Through a simple and intuitive apple game, we demonstrate the necessity and impacts of integrating spatial-explicit fitness functions in classic spatial optimization models.
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