Bayesian maximum entropy mapping and the soft data problem in urban climate research

Seung Jae Lee, Robert Balling, Patricia Gober

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

The pressing problem of Phoenix's urban heat island (UHI) has spawned numerous academic studies of the spatiotemporal nature of this physical process and its relationship to energy and water use, urban design features, and ecosystem processes. Critical to these studies is an accurate representation of the UHI over space and time. This article is concerned chiefly with representing the UHI by using the Bayesian Maximum Entropy (BME) method of modern geostatistics to account for data uncertainty from missing records. We apply BME to the UHI in Phoenix by retrieving and mapping minimum temperature observations over time from historical weather station networks, then testing our mapping accuracy compared to traditional maps that do not account for data uncertainty. The results demonstrate that BME leads to increases of mapping accuracy (up to 35.28 percent over traditional linear kriging analysis). A subsequent synthetic case study confirms that substantial increases in mapping accuracy occur when there are many cases of missing or uncertain data. Use of BME reduces the need for costly sampling protocols and produces UHI maps that can be integrated with other data about human and environmental processes in future studies of urban sustainability.

Original languageEnglish (US)
Pages (from-to)309-322
Number of pages14
JournalAnnals of the Association of American Geographers
Volume98
Issue number2
DOIs
StatePublished - 2008

Fingerprint

urban climate
heat island
entropy
heat
climate
uncertainty
studies (academic)
urban design
geostatistics
weather station
energy use
kriging
water use
sustainability
energy
water
ecosystem
sampling
temperature

Keywords

  • Bayesian Maximum Entropy
  • Geostatistics
  • Soft data
  • Spatiotemporal mapping
  • Urban heat island

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes

Cite this

Bayesian maximum entropy mapping and the soft data problem in urban climate research. / Lee, Seung Jae; Balling, Robert; Gober, Patricia.

In: Annals of the Association of American Geographers, Vol. 98, No. 2, 2008, p. 309-322.

Research output: Contribution to journalArticle

@article{5256da4badbe4f41980cd5d9e2b28774,
title = "Bayesian maximum entropy mapping and the soft data problem in urban climate research",
abstract = "The pressing problem of Phoenix's urban heat island (UHI) has spawned numerous academic studies of the spatiotemporal nature of this physical process and its relationship to energy and water use, urban design features, and ecosystem processes. Critical to these studies is an accurate representation of the UHI over space and time. This article is concerned chiefly with representing the UHI by using the Bayesian Maximum Entropy (BME) method of modern geostatistics to account for data uncertainty from missing records. We apply BME to the UHI in Phoenix by retrieving and mapping minimum temperature observations over time from historical weather station networks, then testing our mapping accuracy compared to traditional maps that do not account for data uncertainty. The results demonstrate that BME leads to increases of mapping accuracy (up to 35.28 percent over traditional linear kriging analysis). A subsequent synthetic case study confirms that substantial increases in mapping accuracy occur when there are many cases of missing or uncertain data. Use of BME reduces the need for costly sampling protocols and produces UHI maps that can be integrated with other data about human and environmental processes in future studies of urban sustainability.",
keywords = "Bayesian Maximum Entropy, Geostatistics, Soft data, Spatiotemporal mapping, Urban heat island",
author = "Lee, {Seung Jae} and Robert Balling and Patricia Gober",
year = "2008",
doi = "10.1080/00045600701851184",
language = "English (US)",
volume = "98",
pages = "309--322",
journal = "Annals of the American Association of Geographers",
issn = "2469-4452",
publisher = "Taylor and Francis Ltd.",
number = "2",

}

TY - JOUR

T1 - Bayesian maximum entropy mapping and the soft data problem in urban climate research

AU - Lee, Seung Jae

AU - Balling, Robert

AU - Gober, Patricia

PY - 2008

Y1 - 2008

N2 - The pressing problem of Phoenix's urban heat island (UHI) has spawned numerous academic studies of the spatiotemporal nature of this physical process and its relationship to energy and water use, urban design features, and ecosystem processes. Critical to these studies is an accurate representation of the UHI over space and time. This article is concerned chiefly with representing the UHI by using the Bayesian Maximum Entropy (BME) method of modern geostatistics to account for data uncertainty from missing records. We apply BME to the UHI in Phoenix by retrieving and mapping minimum temperature observations over time from historical weather station networks, then testing our mapping accuracy compared to traditional maps that do not account for data uncertainty. The results demonstrate that BME leads to increases of mapping accuracy (up to 35.28 percent over traditional linear kriging analysis). A subsequent synthetic case study confirms that substantial increases in mapping accuracy occur when there are many cases of missing or uncertain data. Use of BME reduces the need for costly sampling protocols and produces UHI maps that can be integrated with other data about human and environmental processes in future studies of urban sustainability.

AB - The pressing problem of Phoenix's urban heat island (UHI) has spawned numerous academic studies of the spatiotemporal nature of this physical process and its relationship to energy and water use, urban design features, and ecosystem processes. Critical to these studies is an accurate representation of the UHI over space and time. This article is concerned chiefly with representing the UHI by using the Bayesian Maximum Entropy (BME) method of modern geostatistics to account for data uncertainty from missing records. We apply BME to the UHI in Phoenix by retrieving and mapping minimum temperature observations over time from historical weather station networks, then testing our mapping accuracy compared to traditional maps that do not account for data uncertainty. The results demonstrate that BME leads to increases of mapping accuracy (up to 35.28 percent over traditional linear kriging analysis). A subsequent synthetic case study confirms that substantial increases in mapping accuracy occur when there are many cases of missing or uncertain data. Use of BME reduces the need for costly sampling protocols and produces UHI maps that can be integrated with other data about human and environmental processes in future studies of urban sustainability.

KW - Bayesian Maximum Entropy

KW - Geostatistics

KW - Soft data

KW - Spatiotemporal mapping

KW - Urban heat island

UR - http://www.scopus.com/inward/record.url?scp=39749108233&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=39749108233&partnerID=8YFLogxK

U2 - 10.1080/00045600701851184

DO - 10.1080/00045600701851184

M3 - Article

AN - SCOPUS:39749108233

VL - 98

SP - 309

EP - 322

JO - Annals of the American Association of Geographers

JF - Annals of the American Association of Geographers

SN - 2469-4452

IS - 2

ER -