Designing a Geospatial Information Infrastructure
for Mitigation of Heat Wave Hazards in Urban Areas

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Olga V. Wilhelmi1; Kathleen L. Purvis2; and Robert C. Harriss3
Abstract: Extreme heat is a natural hazard that could rapidly increase in magnitude in the 21st century. The combination of increasing
urbanization, growing numbers of vulnerable people, and the evidence of global warming indicate an urgent need for improved heat-wave
mitigation and response systems. A review of the literature on heat-wave impacts in urban environments and on human health reveals
opportunities for improved synthesis, integration, and sharing of information resources that relate to the spatial and temporal nature of
threats posed by extreme heat. This paper illustrates how geospatial technologies can aid in the mitigation of urban heat waves.
DOI: 10.1061/共ASCE兲1527-6988共2004兲5:3共147兲
CE Database subject headings: Urban areas; Health hazards; Geographic information systems; Remote sensing; Temperature.

Introduction
Cities and climate are coevolving in a manner that could place
more vulnerable populations at risk from exposure to extreme
heat. Throughout the world, the 21st century will be the first
period of time in recent history with more people living in urban
areas than in rural areas. In the United States, for example, the
urban population already comprises nearly two-thirds of the country’s total population and is likely to continue to grow 共U.S. Census Bureau 2000兲. Throughout much of human history, cities have
been catalysts for civilizations and nations to become more
wealthy, healthy, and rich in culture and knowledge. However,
with an increasing number of urban inhabitants, especially vulnerable groups such as the elderly and children, management of
urban health, sustainability, and quality of life is a continuing
challenge.
Global urbanization and its associated industrialization exacerbates concentrations of greenhouse gases and other atmospheric
constituents, which alter the global climate system. Largely because of that, the 21st century is expected to be characterized by
global warming 共IPCC 2001兲. According to scientific predictions,
global climate change will likely lead to increases in the frequency, duration, and magnitude of heat waves 共Kattenberg et al.
1996; IPCC 2001兲.
The rapid growth of urban population, the urban heat island
effect, and a potential increase in the frequency and duration of
heat waves due to global climate change, raise a series of issues
1
Project Scientist, Environmental and Societal Impacts Group,
National Center for Atmospheric Research, P.O. Box 3000, Boulder,
CO 80307-3000.
2
Assistant Professor, Joint Science Dept. of the Claremont Colleges,
W.M. Keck Science Center, 925 N. Mills Ave., Claremont, CA 91711.
3
Senior Scientist, Environmental and Societal Impacts Group,
National Center for Atmospheric Research, P.O. Box 3000, Boulder
CO, 80307-3000.
Note. Discussion open until January 1, 2005. Separate discussions
must be submitted for individual papers. To extend the closing date by
one month, a written request must be filed with the ASCE Managing
Editor. The manuscript for this paper was submitted for review and possible publication on May 10, 2002; approved on April 19, 2004. This
paper is part of the Natural Hazards Review, Vol. 5, No. 3, August 1,
2004. ©ASCE, ISSN 1527-6988/2004/3-147–158/$18.00.

about the increased health risks of sensitive urban populations to
extreme heat and the effective means of mitigating impacts of
heat waves. The underlying assumption of this study is that an
inadequate understanding of the geospatial nature of vulnerable
populations and their surroundings limits the design of efficient
and effective strategies for reducing human health threats posed
by extreme heat events in cities.
The goal of this paper is to evaluate how geospatial technologies can enhance understanding and improve mitigation of heatwave impacts in urban areas. First, we summarize research on
heat-wave impacts, urban heat island effect, and factors of societal vulnerability to extreme heat. We then review heat-wave mitigation strategies employed in several urban areas and discuss how
geospatial technologies might be used to improve understanding
of human vulnerabilities to extreme heat in urban environments
and enhance hazard mitigation. A conceptual framework for designing geospatial information infrastructure for the extreme heat
impacts mitigation is presented.

Heat Waves and Their Impacts
Heat-related mortality and morbidity in the United States indicate
continuing vulnerability of urban populations to extreme heat.
The National Weather Service Office of Climate, Water, and
Weather Services estimates that a total of 2,248 people throughout
the United States lost their lives due to extreme heat between
1986 and 2000 共Fig. 1兲.
Exposure to extreme heat events can cause a myriad of health
conditions due to vital fluid and mineral loss in the body. Heat
stroke can become life threatening within minutes and occurs
when body temperature rises above 105°F (40.6°C) 共Center for
Disease Control 共CDC兲 1995兲. Compared to other meteorological
hazards, which pose threats to property and human health 共e.g.,
floods, hurricanes, and tornadoes兲, heat waves rank first as the
cause of human mortality. Table 1 summarizes economic losses
and deaths due to a variety of hazards that occurred in the U.S.
between 1996 –2000 共National Weather Service 2001兲. Although
not intuitive, heat waves 共here shown in concurrence with
droughts兲 resulted in the largest number of deaths in the United
States in this class of natural disasters. The complexity of heatwave hazards derives from the fact that unlike most natural hazNATURAL HAZARDS REVIEW © ASCE / AUGUST 2004 / 147

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Fig. 1. Spatial distribution of heat related mortality from 1986 to
2000. Mortality statistics were obtained from the National Weather
Service Office of Climate, Water and Weather Services.

ards the climatological indicators of heat hazard vary between
different regions. Also, the absence of common definitions and
criteria for heat hazard identification may contribute to delayed
actions in response to an already established heat wave. Heat
waves are sporadic phenomena, occurring throughout the United
States. Frequency, intensity, and duration of heat waves, however,
vary drastically from year to year. In the United States, there is no
single universal threshold temperature above which the rate of
heat-related morbidity and mortality increase sharply. Instead, tolerance of excess heat varies regionally according to the population and its preparation for hot weather and according to the local
average temperatures and frequency of extreme temperatures
共McMichael et al. 1996兲.
The magnitude of morbidity and mortality attributed to excessive heat exposure is most likely underestimated. This underestimation can be attributed to the lack of standardized definitions of
heat-related mortality and resulting misclassification of cases.
Heat can exacerbate existing medical conditions, which contribute to the cause of death. However, several studies showed that
some cases of illnesses and deaths resulting from multiple causes
共including heat as one of the contributing factors兲 were only attributed to preexisting medical conditions 共Oechsli and Buechley
1970; Schuman 1972; Bridger et al. 1976; Kalkstein 1995; Semenza 1996兲. The effects of heat waves can be seen in increased
mortality the day after the high temperatures occur 共Kalkstein
1991兲. During a July 1988 heat wave in Allegheny County, Pennsylvania, excess mortality was correlated with the average temperature of the previous day, signifying that consecutive high day

Table 1. Summary of Monetary Losses, Morbidity, and Mortality for
Major Natural Hazards Occurred in the U.S. from 1996 –2000 共NWS
2001兲
Natural hazard
Drought/heat wave
Tropical cyclone
共tropical storm/hurricane兲
Tornado
Flooding
Lightning
Winter storms
Extreme cold

Monetary loss
共Millions of dollars兲 Morbidity Mortality
2,200
12,400

3,238
141

950
66

5,600
15,900
190
1,800
1,400

6,136
7,408
1,505
2,017
99

345
490
235
318
146

and night temperatures caused the heat-related mortality and morbidity among sensitive populations 共Ramlow and Kuller 1990兲.
Originally it was thought that the single predictive meteorological variable for increased mortality during a heat wave was
temperature 共Oechsli and Buechley 1970兲. Now it is known that a
variety of meteorological variables contribute to human illnesses
and deaths during heat waves. These variables include relative
humidity, wind speed, and fluxes in both short- and long-wave
radiation, in addition to temperature 共Steadman 1984兲. The ‘‘heat
index’’ is a combination of these factors and is used to evaluate
heat stress on the human body. One problem with the heat index
rating is that it is the same across the world, with no adjustment
for differing climates in various regions. For instance, 95°F
(35°C) might be considered ‘‘normal’’ in Houston, Texas and
extremely hot in a more temperate climate, such as New York
City.
Urbanization is one of the risk factors contributing to heatwave impacts on humans. Heat-related illnesses and deaths are a
greater problem in cities than in suburban or rural areas, because
the combined effect of high temperature and high humidity are
more intense in the centers of urban areas. In a study of the July
1966 heat wave in New York City and St. Louis, Missouri, Schuman 共1972兲 discovered that areas of cities with more concrete and
less vegetation exhibited higher mortality rates due to heat-related
illnesses. Further study on this same extreme temperature event
revealed a positive correlation between higher temperature and
population density during this heat wave in the New York–New
Jersey metropolitan area, leading to a mortality rate 55 times
greater in the city, as opposed to rural areas 共Buechley et al.
1972兲. Urban settings often include high-rise apartment buildings,
and people residing in the top floors of such buildings are at a
greater risk 共Barrow and Clark 1998兲.
Examples from the literature showed that heat waves have a
more pronounced effect on the population of temperate climates,
and that health impacts are unevenly distributed over the course
of the warm season. During July 1993, the city of Philadelphia
exhibited 118 heat-related deaths 共CDC 1994兲. Another highmortality heat wave occurred in Chicago in mid-July 1995. Over
500 people died in the heat wave, which lasted only 3 days. The
high number of deaths was primarily due to high day and nighttime temperatures that lasted for 48 h 共Karl and Knight 1997兲. In
addition to high nighttime temperatures, which did not allow the
body to recover from extreme heat during the day, both Chicago
and Philadelphia are in temperate regions that do not experience
as many high-temperature episodes as the southwestern United
States for instance. Increased mortality due to heat waves has
been linked to regions that experience greater summer weather
variations, such as New York City and Chicago, as opposed to
Houston and Los Angeles 共Greene and Kalkstein 1996; CDC
1984; CDC 1994兲. People who live in persistently hot regions of
the world acclimate to the higher temperatures and adapt more
easily, compared to those who live in a region that is usually
colder and less oppressive. Davis et al. 共2001兲 analyzed mortality
data for over 25 years and concluded that excess summer mortality in the U.S. has occurred in most northern cities, but there was
little or no mortality response to high apparent temperatures in
southern cities, regardless of the severity of the extreme heat
event.
Heat waves that occur earlier in the summer usually exhibit
higher mortality rates as well, primarily because the human body
has not had time to acclimate to the inclement climate 共McMichael et al. 1996兲. In addition, the most susceptible portion of
the population would be affected by an extreme event early in the

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summer season, resulting in a phenomenon often called mortality
displacement 共Kalkstein 1995兲. This means that people were
likely to die from other causes and that the heat wave caused
death a little sooner, resulting in a lower mortality rate during
extreme events later in the summer. This can be explained with
the concept of competing causes, which refers to the multiple
potential causes of death, whereby one cause 共e.g., extreme heat兲
supercedes another 共e.g., an illness兲 thus causing an earlier than
anticipated death 共Rothman and Greenland 1998兲. Overall, the
factors that most affect the rate of mortality include the duration
and timing of a heat wave, and also the geographical location and
expected exposure to heat in a certain region.
In the next several decades, the reinforcing effects of global
warming and continued urbanization could result in even more
catastrophic human health impacts in the U.S. In order to assess
the potential effects of global warming on large U.S. cities, with a
population greater than 1 million, Kalkstein and Greene 共1997兲
developed a novel air mass-based synoptic approach to determine
a relationship between climate change and heat-related mortality
in large cities. In the current climate, two air masses dominate the
increase in mortality in midwestern and eastern cities during the
summer season. General circulation models were used to predict
the change in frequency and intensity of the air masses that cause
increased heat-related mortality. If the climate warms as the models predict, summer deaths will increase substantially, and although winter deaths will decrease slightly, this will not be
enough to offset the amplification of summer mortality.

Characterizing Urban Heat Islands
The local-to-regional influence of cities on climate is well documented by more than a century of observational, modeling and
laboratory studies that have compared weather and climate parameters in urban environments to nearby rural areas. As urban
populations grow, ever increasing attention is being directed to
various climatological and environmental issues associated with
urban development, including the urban heat island effect 共American Meteorological Society 2000兲. The term ‘‘urban heat island’’
has been used in climate research primarily to describe differences in background surface temperature between urban areas and
rural surroundings. Urban heating is largely attributed to excess
heat absorbed and released from urban infrastructure, such as
buildings, streets, and parking lots 共Kim 1992兲. A temperature
gradient between an urban area and nearby rural land can often
reach up to 10°C.
Quantitative studies of the urban influence on near-surface air
temperatures have been ongoing for almost 2 centuries 共e.g.,
Howard 1833; Sunborg 1950; Duckworth and Sandburg 1954;
Mitchell 1961; DeMarrais 1961; Chandler 1965; Gallo and Owen
2000兲. Books describing and analyzing the impacts of cities on
local climate have been written by Howard 共1833兲; Chandler
共1965兲; Oke 共1978兲; Landsberg 共1981兲, and others. Several review
papers summarize the first generation of urban climate studies
共Lowry 1967; Landsberg 1970; Landsberg 1981兲. From these
studies it was clear that land use change, particularly the development of built infrastructure, could significantly alter temperatures, humidity, winds, visibility, radiation, and other meteorological parameters in urban areas and that heat stress in cities
would typically exceed the heat stress experienced in surrounding
rural areas. The influence of the building infrastructure and nonnatural surfaces characteristics was clearly detectable at a variety
of local time 共e.g., differences in the diurnal temperature gradient兲

Table 2. Selected Typical Urban Climate Effects and Surface and

Atmospheric Properties for a Midlatitude Cty with about 1 Million
Inhabitants. Modified from 共Oke 1997兲
Variable

Change

Air temperature

Warmer

Humidity
Heat storage
Wind speed
Cloudiness
Property

Magnitude of change
or comment

1 – 3°C per 100 years; 1 – 3°C
annual mean; up to 12°C
hourly mean 共summer daytime兲
Drier
Summer daytime
More moist Summer night, all day winter
Greater
About 200%
Decreased 5–30% at 10 min strong flow
Increased In weak flow with heat island
More haze In and downwind of city
More cloud Especially in lee of city
Change

Albedo

Lower

Anthropogenic heat

Greater

Typical magnitude
Rural: 0.12–0.20
Suburban: 0.15
Urban: 0.14
Rural: absent
Suburban: 15– 50 W m⫺2
Urban: 50– 100 W m⫺2
共winter up to 250 W m⫺2 )

and space 共e.g., changes in surface characteristics across an urban
area兲 scales. Oke 共1997兲 estimated typical differences in meteorological and climatological parameters between a rural area and
a mid-latitude city with about 1 million inhabitants. Examples of
these differences are shown in Table 2.
The causes of the urban heat island can be related directly to
the integrated effects of the net albedo 共reflectivity兲 of the urban
surface 共Taha et al. 1988兲, the thermal storage capacity of the
urban physical infrastructure and remaining natural ecosystems
共Myrup 1969, Atwater 1972兲, gaseous and particulate air pollutants, and the interaction of boundary layer winds with topography
and infrastructure 共Oke 1997兲. Thermal storage of heat in building
materials is an important consideration in the urban energy balance and in mitigation of hot season human health impacts. Many
materials used for the construction of buildings and streets will
store heat more effectively than did the natural ecosystems they
replace. That energy is released slowly at night, accentuating the
nighttime urban heat island 共Akbari et al. 1989兲. The process of
heat storage in the infrastructure also delays daytime temperature
rise and shifts the time of peak cooling demand depending on the
number of consecutive days of a particular weather pattern.
The distribution of vegetation is also important because latent
heat loss from evapotranspiration decreases the energy available
for heating the near-surface air. Differences in the amount of vegetation cover in a city can influence the urban-rural gradient by as
much as 8 – 10°C. Reducing vegetation cover lowers evapotranspiration, and, consequently, more energy goes into sensible heat
flux and thermal storage 共Nunez and Oke 1977兲.

Vulnerable Populations
Societal vulnerability often determines the severity of impacts of
a natural hazard on an individual or a group. A number of case
studies used epidemiological and statistical techniques to understand the relationship between heat waves, heat-related morbidity,
and mortality and to identify vulnerable groups of people
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共Smoyer 1998; Kalkstein and Greene 1997; Chan et al. 2001兲. In
the natural hazards research literature, many authors have discussed factors contributing to vulnerability 共Downing 1991; Dow
1993; Blaikie et al. 1994; Cutter 1996; Morrow 2000兲. Among
these factors are characteristics of the environment, individuals
共e.g., special needs population, poverty/wealth indicators, gender,
and race兲, and society. Specific studies of heat waves and associated mortality and health impacts have identified characteristics
of vulnerable populations. The increase in mortality during heat
waves rests disproportionately on such segments of the population as the elderly, newborn babies, young children 共less than 4
years old兲, infirm 共e.g., people with cardiovascular disease兲, poor,
socially isolated, and people with mental disabilities 共Ellis 1972;
Schuman 1972; Bridger et al. 1976; Jones et al. 1982; Bross et al.
1994; McMichael 1996; Batscha 1997; How et al. 2000; McGeehin and Mirabelli 2001兲.
The body’s ability to regulate temperature is hampered by old
age, obesity, heart disease, poor circulation, and certain medications. As opposed to healthy people, individuals with these conditions do not exhibit the same ability to thermoregulate their
body through radiation and evaporative heat loss when exposed to
high temperature and elevated humidity 共CDC 1996兲 and often do
not have the mobility to leave their hot residence for a cooler
locale.
Living conditions and social networks also contribute to overall vulnerability to extreme heat 共McGeehin and Mirabelli 2001兲.
A significantly higher death rate occurred for people living in
nursing homes without air conditioning, as opposed to those with
artificial cooling systems during heat waves in New York City in
1972 and 1973 共Marmor 1978兲. The elderly, in general, was the
most vulnerable age group during extensive heat waves in New
York City during August 1975 and June 1984 共Ellis and Nelson
1978; CDC 1984兲. The high mortality rate was attributed to those
people who lived at home and took care of themselves, as opposed to those who stayed in nursing homes or hospitals during
the extreme event. Also, the elderly and infirm are more at risk if
they are confined to their bed or unable to care for themselves,
incapable of leaving home each day, or live alone, as was determined by a study on the Chicago heat wave of July 1995 共Semenza et al. 1996兲. This risk decreases if they have a social network of friends or family in the neighborhood, air conditioning,
and access to transportation.
The poor are another vulnerable group in the context of heat
waves. This group often lacks access to an air conditioner or a
means of transportation to go to a cooler location and often lives
in city centers, where the effect of the urban heat island is the
most pronounced 共Schuman 1972; Jones et al. 1982; Smoyer
et al. 2000兲. During extended heat waves, the lack of nocturnal
cooling can have a particularly devastating impact on morbidity
and mortality, especially in urban high-rise buildings, which are
susceptible to heat island effects. Homes located in high crime
rate areas are also more vulnerable, because people are often
afraid to leave windows open at night, which would increase air
circulation indoors.
With the changing demographics in the U.S., there has been
increased attention in the natural hazards field to the vulnerability
of racial and ethnic communities 共Perry et al. 1983; Fothergill
et al. 1999兲. A lower preparedness level, language barriers, and
socio-economic factors such as income, housing issues, and availability of quality health care, may contribute to increased vulnerability of racial and ethnic communities to the impacts of heat
waves.

Reducing Heat Wave Mortality and Morbidity
Existing Mitigation Strategies
Following major heat waves in the 1990s, several warning systems were implemented in places with high heat-related mortality
rates. Kalkstein et al. 共1996兲 developed a Health Watch/Warning
System for Philadelphia, in order to identify approaching heat
waves and warn vulnerable populations about those hazardous
weather events. This system is based on a synoptic climatological
approach by grouping days with homogeneous meteorological
conditions such as temperature, humidity, cloud coverage, wind
speed, and other variables and comparing these to heat wave episodes in the past that have caused high mortality events. For
example, in Philadelphia there is a strong correlation between
certain excessively hot and humid maritime air masses and mortality. When one of these air masses is forecast 共up to 48 h in
advance兲, the city health commissioner consults with the Philadelphia Department of Public Health and the National Weather
Service and issues special health advisories. Health advisories are
accompanied by media broadcasts, a telephone-based ‘‘heatline,’’
and the implementation of a ‘‘buddy system,’’ which encourages
neighbors to check on susceptible individuals, such as elderly or
ill people on their street. Also, local utilities are encouraged not to
interrupt service during the heat emergency. A similar study was
developed for five major Australian cities, correlating temporal
synoptic indices and past heat-related mortality data to identify
dangerous air masses 共Guest et al. 1999兲.
A different type of weather warning system that depends on
the comparison of a weather stress index 共WSI兲 with mortality
rates has been developed in Hong Kong 共Li and Chan 2000兲. The
WSI compares the apparent temperature with the mean value for
that date and location. The apparent temperature adjusts the ambient temperature according to relative humidity and human perception 共Steadman 1979兲. If the WSI is particularly high, meaning
an increased mortality rate occurred during similar weather conditions in the past, then the public is warned about possible extreme weather conditions. In addition, precautionary measures
that the public can take to reduce their risk are included with the
warning.
Community-based outreach programs can be especially effective in reaching vulnerable populations and helping them to protect themselves during extreme heat waves. The Medical College
of Pennsylvania developed a community-based assessment of isolated, older adults 共65 years and older兲 in northern Philadelphia to
determine their vulnerability and knowledge of heat-related illnesses 共Mattern et al. 2000兲. They discovered that one-on-one
intervention is the most effective approach to reducing heatrelated health threats. In addition, they found that health-related
materials should be designed for older adults, such as an easily
readable thermometer and other materials designed with cultural
and age-specific characteristics.
After an intense heat wave in St. Louis in 1980 resulted in the
hospitalization or death of one in every 1,000 residents due to
heat-related illnesses 共Jones et al. 1982兲, the city developed a
Heat Wave Mortality Prevention Program 共Smoyer 1998兲. This
involved the implementation of a heat wave health watch/warning
system, public education about health risks, development of cooling shelters, and the distribution of air-conditioners to high-risk
populations. The efficacy of the program has not been evaluated
to determine whether it actually deters excess mortality rates during high temperature episodes. The number of people at risk in

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Fig. 2. 共Color兲 Thermal infrared aircraft data provide comparison of nighttime and daytime surface temperatures in downtown Atlanta 共Data
source: NASA Marshall Space Flight Center兲.

the city has actually increased between 1980 and 1995 due to
economic decline.
After the 1995 heat wave, the city of Chicago developed an
Extreme Weather Operations Plan, which included mitigation
measures to reduce impacts of heat waves on the city’s population. After receiving a heat warning from the National Weather
Service, the Chicago Fire Commissioner mobilizes the city social
service departments to carry out well-being checks, provide cooling centers, check buildings for proper ventilation, monitor nursing homes and hospital emergency rooms, watch for citizens at
risk from excessive heat exposure, and distribute ‘‘Heat Tips’’
brochures with information about how to stay healthy during a
heat wave. During the consequent heat wave event in 1999, the
death toll from the heat in Chicago was reduced by 80% compared to the 1995 heat wave. The reduced impacts were partially
due to meteorological differences between the two heat waves
共e.g., lower intensity and longer warm onset of the latter兲, but
largely because of improved preparedness and timely responsiveness of municipalities 共Palecki et al. 2001兲.
In 2002, the National Oceanic and Atmospheric Administration 共NOAA兲 launched a new early heat warning system, which
can provide local emergency officials with advanced warning of
prolonged periods of extreme heat up to 7 days before their onset.
The early warning system uses the daily mean heat index, which
incorporates information on both nighttime lows and daytime
highs and is calculated by using the forecasted values of minimum and maximum temperature and relative humidity 共NOAA
2002兲.

Role of Geospatial Technologies
The potential for effective applications of geospatial technologies
to disaster mitigation is closely linked to the predictability of the
events in time and space and their consequences 共Alexander
1991兲. Extreme heat events are a complex, subtle, and deadly
threat compared to most other natural hazards. Yet, heat waves are
more readily forecast and more amenable to actions that can prevent or mitigate human health impacts. The continuing development of geospatial technologies provides increasingly sophisticated, yet flexible tools for data collection, analysis, monitoring,
mapping, management, and communication to the public. The use
of geospatial technologies, including geographic information systems 共GIS兲 and remote sensing, is increasing in many sectors of

society, including meteorology and climatology, urban planning,
and disaster management. Successful implementation of these
technologies in various sectors and disciplines, such as medical
geography, hazard management, and urban planning, allows us to
believe that technological capability to mitigate risk of urban heat
impacts exists. However, no comprehensive methods have been
developed for utilizing geospatial technology in the mitigation of
the impacts of extreme heat on an urban population. Studies of
urban heat islands, vulnerability assessment, and communitybased hazard mitigation could benefit from applications of remote
sensing and GIS. In the following sections, benefits and limitations of remote sensing and GIS technologies are discussed in the
context of heat wave impact mitigation and the writers’ proposed
conceptual framework.
Remote Sensing
The spatial nature of land surface air temperatures from urban to
regional scales has been studied with direct ground-based meteorological monitoring, remote sensing from airborne and satellite
platforms, and computer modeling methods. Each of these methods for measuring, forecasting, and simulating temperatures or
heat stress index has associated uncertainties and limitations. The
writers focus on the specific type and quality of information that
can aid in the mitigation of heat wave impacts on vulnerable
urban populations.
One application of remote sensing observations is thermal
mapping. Emitted ‘‘heat’’ energy from objects on the earth and
from the earth surface itself can be sensed through the windows at
3 to 5 ␮m and 8 to 14 ␮m of the electromagnetic spectrum, using
airborne or spaceborne thermal scanners 共Lillesand and Kiefer
1994兲. Fig. 2 illustrates an example of thermal remote sensing
applied in a study of an urban heat island. High-resolution thermal infrared aircraft data show the distinction of nighttime and
daytime surface temperatures in downtown Atlanta, clearly indicating an urban heat island effect. The nighttime data reflect the
differential cooling rates associated with soils and vegetation
compared to the heat storage associated with built infrastructure.
The daytime data illustrate more uniform surface temperatures
across the landscape due to direct radiation, active mixing by
surface winds, and the diminished influence of heat storage.
Satellite and airborne remote sensing techniques can provide
relatively frequent and synoptic coverage of urban versus rural
temperature variations and detect spatial variability within urban
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Table 3. Selected Satellite and Airborne Sensors with Potential Use

for Heat Detection

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Thermal
band
number

Bandwidth
共␮m兲

Spatial resolution
共m兲

Temporal
resolution

Landsat Thematic Mapper 共TM兲 on Landsat 4 and 5
6
10.4 –12.5
120⫻120
16 days
Landsat Enhanced Thematic Mapper (ETM⫹) on Landsat 7
6
10.4 –12.5
60⫻60
16 days
NOAA AVHRR
3
3.55–3.93
1100⫻1100
Daily
共every 12 hours兲
4
10.3–11.3
1100⫻1100
5
11.5–12.5
1100⫻1100
NASA Thermal Infrared Multispectral Scanner 共TIMS兲 共airborne兲
1
8.20– 8.6 Variable (IFOV⫽2.5 mrad)
Variable
2
8.6 –9.0
3
9.0–9.4
4
9.4 –10.2
5
10.2–11.2
6
11.2–12.2
Terra ASTER 共Advanced Spaceborne Thermal Emission
and Reflection Radiometer兲
10
8.125– 8.475
90⫻90
Daily
11
8.475– 8.825
90⫻90
12
8.925–9.275
90⫻90
13
10.25–10.95
90⫻90
14
10.95–11.65
90⫻90
MODIS 共Moderate Resolution Imaging Spectrometer兲
20
3.660–3.840
1000⫻1000
Daily
21
3.929–2.989
1000⫻1000
22
3.929–2.989
1000⫻1000
23
4.020– 4.080
1000⫻1000
31
10.780–11.280
1000⫻1000
32
11.770–12.270
1000⫻1000
Multispectral thermal imager
15 bands
Visible
5⫻5
Variable
Other
20⫻20

heat islands. Lo et al. 共1997兲 studied the urban heat island in
Huntsville, Alabama, using high-resolution thermal infrared imagery from the NASA Advanced Thermal and Land Applications
Sensor 共ATLAS兲 system. Satellite data provided accurate characterization of the urban land cover types for the spatial modeling of
the urban heat island effect. Aniello et al. 共1995兲 used Landsat
Thematic Mapper data and a GIS to map microurban heat islands
in a portion of Dallas. The results of the study showed that the
temperature of microurban heat islands was 5 – 11°C higher compared to surrounding areas by midmorning. Ben-Dor and Saaroni
共1997兲 found airborne video thermal radiometry to be an effective
and low-cost tool for detection and monitoring of microscale
structures of the urban heat island.
Remote sensors used in various studies differ in terms of their
spectral, spatial, and temporal resolutions, and their use depends
on a particular research task. Table 3 shows characteristics of the
selected satellite and airborne sensors that can operate in the thermal portion of the electromagnetic spectrum and, therefore, have
potential in applications of urban heat detection and monitoring.
There are also an increasing number of commercially available
sensors 共not included in Table 3兲, which can operate from satellites, aircrafts, and helicopters. For example, the Vid/Thermal

Tracker 2000 共AVCAN Systems Corporation, Vancouver, BC兲
provides a helicopter-based technology that is able to accurately
measure points of interest within an accuracy of 1 m and 0.2°C
temperature. Originally designed to provide information for major
industries, this technology can be utilized in urban heat island
mapping.
There have been significant advancements in remote sensing
technology, as indicated by the studies of the urban heat island
discussed previously. However, many limitations still exist, especially for the health applications of remote sensing. According to
Vicente and Maynard 共2002兲, limiting factors include lack of appropriately high spatial, temporal, and spectral resolution of existing sensors and the absence of continuous temporal and spatial
data sets required for the study of specific health-related problems. In addition, the high cost of high-resolution remote sensing
data, differences in data formats, and poor technology transfer
methods often limit integration of remote sensing and health data.
Many of these issues are being addressed as new satellites are
being developed and launched. However, there is still a need for
improvement of communication between data providers and the
users, as well as a need for user-friendly, operational, and lowcost decision-support systems.
Geographic Information Systems
A geographic information system is an invaluable tool for integration, analysis, and visualization of spatial information. GIS
presents a set of concepts, methods, and tools to explore spatial
patterns in data. Geospatial databases could help bring multiple
datasets together for comprehensive analysis of a research question or practical application. Although GIS as a technology is well
developed and has been widely used in urban planning, disaster
management, and various environmental applications, researchers
only recently started to use GIS in studies of human health,
共Gatrell and Loytonen 1998兲 meteorology, and climatology 共Shipley et al. 2000; Wilhelmi and Brunskill 2003兲.
Environmental epidemiology is one of the fields where GIS,
remote sensing, and health research have come together. Examples can be found in research on infectious and vector-borne
disease surveillance 共Glass et al. 1995; Kitron et al. 1994; Richards 1993兲, exposure assessments 共Wartenberg 1992; Hjalmars
et al. 1996; Holm et al. 1995兲, identification of study populations
共Croner et al. 1996兲, disease mapping 共Jacquez 1998; Xue et al.
1999兲, and public health surveillance 共Rushton 1998兲.
The spatial characterization of risk and vulnerability factors in
GIS is a key step for hazard mitigation. In an earlier study, Martinez et al. 共1989兲 used the county-level dot mapping technique to
show fatalities due to extreme heat among elderly persons. This
technique helped to identify high-risk areas of the continental
U.S. in which severe adverse health impacts are likely to occur.
Improvements in GIS technology have allowed for easier integration of biophysical and socioeconomic data and have led to an
increased number of studies on assessment of spatial risk and
vulnerability and disaster management 共Dangermond 1991;
FEMA 1994, 1996; Cova and Church 1997; NOAA Coastal Service Center 1999; Cutter et al. 2000; Wilhelmi and Wilhite,
2002兲.
A number of limitations still exist in successful applications of
GIS to heat wave impact mitigation. Some of these limitations
include 共1兲 existing GIS are limited in their ability to represent the
spatiotemporal dynamics of natural phenomena; 共2兲 there is a lack
of data sharing among public and private agencies, organizations,
and government sectors; 共3兲 social, economic, and demographic
data are often presented in a census unit form, which makes it

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Fig. 3. Integrated heat-wave mitigation process

difficult to pinpoint the most vulnerable households; 共4兲 often
there are differences in scale and resolution between social and
environmental data; 共5兲 cost of GIS software could be a limiting
factor to some organizations; and 共6兲 lack of human resources to
timely process and map data. Many of these factors are being
addressed with the advancements in GIS science and technology.

Conceptual Framework for Mitigating Heat-Wave
Impacts
The literature review showed that impacts of extreme heat largely
depend on geographic location, characteristics of an urban environment, and human vulnerabilities, in addition to meteorology
and climatology of heat waves. The environmental and social
factors of vulnerability to heat-related morbidity and mortality
change in time and vary between different geographic locations,
even within one neighborhood. In addition, anthropogenic climate
change is generally expected to increase the frequency, duration,
and severity of heat-stress conditions in many regions. Therefore,
effective mitigation of a heat-wave hazard should be viewed as a
continuing process with frequent updates on social, climatological, and environmental factors contributing to the overall risk.
An illustration of a heat-stress mitigation process is shown in
Fig. 3. This framework emphasizes continuous learning through
integration and updating of existing knowledge related to social
changes, vulnerabilities, adaptations, and the lessons learned from
previous heat-wave events. Such a process includes five major
steps: preparing, initiating, developing, implementing, and learning and adapting. The first step is built upon previous experiences
and events, including inventory, assessment, and analysis of historical heat-stress impacts and community responses. Goals and
mitigation actions are identified through the stakeholders’ involvement early in the process. The second and third steps focus
on risk analysis, incorporating all known factors of heat-related
health impacts, and loss of human life. The implementation stage
includes environmental and social monitoring, developing a heatresistant urban landscape, and building networks in communities

and organizations. The last stage of the process evaluates overall
strategies and addresses ‘‘lessons learned.’’
Because of the spatial nature of information required for development and implementation of the heat-wave mitigation process framework, geospatial technologies will play a major role in
data management, analysis, and decision support. Fig. 4 illustrates
details for how remote sensing and GIS will aid in the heat-stress
mitigation process in general and in risk analysis in particular. In
this model, the risk of a heat wave hazard is determined by the
level of exposure to extreme heat, the current state of societal
vulnerability, as well as the cases of previous heat-wave impacts.
The heat-stress cases and underlying factors of exposure and
vulnerability should be identified first. For example, cases would
include mortality and morbidity data, where extreme heat was
either the primary or secondary cause in human death or illness.
Duration, intensity, and timing of the heat wave are among the
exposure factors affecting human death and illness from the extreme heat. The literature also emphasized that spatial patterns of
temperature in urban areas could be significantly influenced for
example by urban design, vegetation type and patterns, and the
albedo of both natural and built surfaces. 共Myrup 1969; Atwater
1972; Kim 1992; Oke 1997兲. Vulnerability largely depends on
characteristics of individuals 共e.g., special needs population,
poverty/wealth indicators, gender, and race兲, and society. Understanding the relative importance of each of these components is
crucial to developing a mitigation strategy for urban heat islands.
The factors listed in Fig. 4 may vary between different communities and geographical regions; therefore, community involvement discussed earlier 共Fig. 3兲 is important.
GIS can be used to map out cases of heat-related illnesses and
deaths. This information when combined in GIS with data on
urban characteristics 共often acquired through remote sensing techniques兲, societal vulnerabilities, and meteorological information,
can be analyzed for spatial patterns and temporal trends. Such
information is often distributed between the whole hierarchy of
public and private agencies and organizations. GIS brings an opNATURAL HAZARDS REVIEW © ASCE / AUGUST 2004 / 153

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Fig. 4. Conceptual framework for urban heat-stress mitigation

portunity for an integrated new perspective to promote hazard
mitigation and control of illnesses.
Identification of ‘‘hot spots’’ or high-risk zones within the
urban area can lead to increased public awareness and more focused, location-specific mitigation measures. Many critical mitigation actions have been identified in numerous publications and
public advisories. Effective implementation of those actions still
remains a challenge for many U.S. urban communities, although
significant progress has been made in recent years. Those steps
include 共but are not limited to兲 creating a ‘‘heat resistant’’ urban
landscape 共e.g., planting trees, replacing roof tops with lighter,
more reflective materials兲, risk communication, and educational
activities about protection of individuals from heat impacts, developing ‘‘buddy’’ systems among neighbors, designating cooling
centers, transportation networks, and establishing heat forecasting
and warning systems. Fig. 5 illustrates these mitigation measures
and also shows a network of organizations responsible for imple-

mentation of these measures and means of information dissemination to the public. For example, creating a heat-resistant urban
landscape will involve urban planners, landscape architects, and
housing developers. Communicating risk to vulnerable populations, educating them about heat impacts, and establishing neighborhood ‘‘buddy systems’’ will be done through various media
programs, visits from social workers, and community meetings.
Because most risk factors for heat-related illnesses are known,
steps can be taken to prevent unnecessary morbidity and mortality
for such susceptible populations. However, much better communication between both public and private service organizations
will be required. For instance, Semenza et al. 共1996兲 discovered
that apartment dwellers have a lower rate of working air conditioners, as opposed to those who own their own home. The public
social service agencies and private property management services
need to share information on these types of vulnerabilities. It is
important to establish these networks before a heat event so that

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Fig. 5. Mitigation measures and informational networks relevant to heat-wave mitigation in urban areas.

once the heat warnings are issued, those individuals or communities most vulnerable can be reached without delays. Examples
from Chicago 共Palekie et al. 2001兲 and Philadelphia 共Kalkstein
2000兲 indicate that improvements in understanding the physical
and social system dynamics associated with heat-wave mortality
and morbidity can be essential to advances in mitigation. Despite
existing limitations, geospatial technologies can provide tools and
methods that integrate information and catalyze collaborative efforts across disciplinary and organizational boundaries.
Collecting data and integrating data through geospatial analysis and designing decision support systems would largely rely on
the availability of geospatial technologies and a data-sharing network of participating organizations. The feedback loop shown in
Fig. 4 emphasizes that existing knowledge, perceptions, and data
on heat-wave impacts, exposure, and vulnerability should be updated with changes in the environment and in society.

Future Trends and Concluding Remarks
In order to effectively mitigate the impacts of extreme heat, interdisciplinary background research, development and maintenance of extensive databases on both physical and social characteristics of the urban environment, and the active participation of
many sectors and levels of municipalities and communities are
needed.
Extreme heat is a complex natural hazard causing loss of
human life and threats to human health. Global climate change
introduces an uncertain factor into the heat-wave threat equation.
Although temperatures are expected to rise, due to global warming, the question is when, where, and how much climate will
change at local and regional scales. Local changes in the spatial
and temporal characteristics of air temperature, humidity, and
other variables will most likely differ for varying regions of the
world.

Because of the uncertainty of extreme weather events that
could develop with global climate change, heat-wave warning
systems are being developed for many vulnerable cities around
the world, such as Rome, Shanghai, Toronto, and other cities in
the United States 共Kalkstein 2000兲. These systems are created in
collaboration with the World Meteorological Organization, World
Health Organization, United Nations Environmental Program,
United States Environmental Protection Agency, and the University of Delaware. The basic approach used is similar to the Philadelphia model. Further interdisciplinary research is needed to develop and integrate improved weather forecasting capabilities for
extreme heat events, more sophisticated vulnerability assessments, and innovations in warning and response systems 共Patz
et al. 2000兲.
The challenge of making progress in mitigation of heat wave
impacts will be exacerbated by current demographic trends. Continuing urbanization of the global population, a maturing of the
age distribution in many nations, the evolution of a complex ethnic mosaic, and increasingly wide disparities tied to education
and income are trends that can contribute to an increased threat of
heat-related mortality and morbidity. These trends are all well
documented in the United States 共e.g., Frey et al. 2001兲. Effective
mitigation of the vulnerability of the elderly and children to harm
and loss from extreme heat will require significant improvements
in understanding of the social, psychological, and physiological
dimensions of exposure and response to threats. Ngo 共2001兲 has
discussed how the complex interactions of chronological age,
gender, marital status, race, education, religion, socioeconomic
status, and geographic locations can influence the vulnerability of
elderly populations to natural disasters. Much more synthesis and
systemic analysis is needed to prepare society for the warmer,
more crowded world of the 21st century.
There is evidence that the series of deadly heat waves that
have occurred in the U.S. in the 1990s stimulated several cities to
work toward better preparedness and mitigation of extreme heat
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events 共Palecki et al. 2001兲. Applications of geospatial technologies in mitigation and management of other natural hazards 共e.g.,
wildfires, earthquakes, hurricanes兲 lead us to believe that mitigation of heat-wave impacts can be further improved with better
understanding and integration of spatial and temporal information
on urban heat island dynamics and vulnerable urban populations.
The writers’ review of existing technologies indicates capabilities
for developing a technologically advanced system that can compliment existing meteorological methods for heat-wave detection
and warning with risk analysis and mitigation planning. The
framework proposed in this paper illustrates how remote sensing
and GIS can provide additional data and tools for integrating and
sharing information resources across the wide range of public and
private stakeholder organizations with responsibilities for mitigation of heat-wave impacts.

Acknowledgments
This research was supported by funding from the National Science Foundation to the University Corporation for Atmospheric
Research and National Center for Atmospheric Research. We
thank S. McNeeley at NCAR and two anonymous reviewers for
their valuable suggestions to the earlier versions of this manuscript.

References
Akbari, H., Garbesi, K., and Martien, P. 共1989兲. ‘‘Controlling summer
heat islands.’’ Proc., Workshop on Saving Energy and Reducing Atmospheric Pollution by Controlling Summer Heat Islands, Lawrence
Berkeley National Laboratory, Univ. of California, Berkley, Calif.
Alexander, D. E. 共1991兲. ‘‘Information technology in real time for monitoring and managing natural disasters.’’ Prog. Phys. Geogr., 15共3兲,
238 –260.
American Meteorological Society. 共2000兲. Proc. 3rd Symp. on the Urban
Environment, August 14 –18, Davis, Calif., American Meteorological
Society, Boston.
Aniello, C., Morgan, K., Busbey, A., and Newland, L. 共1995兲. ‘‘Mapping
micro-urban heat islands using Landsat TM and a GIS.’’ Comput.
Geosci., 21共8兲, 965–969.
Atwater, M. A. 共1972兲. ‘‘Thermal effects of organization and industrialization in the boundary layer: A numerical study.’’ Boundary-Layer
Meteorol., 3, 229–245.
Barrow, M. W., and Clark, K. A. 共1998兲. ‘‘Heat-related illnesses.’’ Am.
Fam. Physician, 58共3兲, 749–756.
Batscha, C. L. 共1997兲. ‘‘Heat stroke. Keeping your clients cool in the
summer.’’ J. of Psychosocial Nursing, 35共7兲, 12–17.
Ben-Dor, E., and Saaroni, H. 共1997兲. ‘‘Airborne video thermal radiometry
as a tool for monitoring microscale structures of the urban heat island.’’ Int. J. Remote Sens., 18共14兲, 3039–3053.
Blaikie, P., Cannon, T., Davis, I. and Wisner, B. 共1994兲. ‘‘At risk: Natural
hazards, people vulnerability, and disasters,’’ Routledge, London and
New York.
Bridger, C. A., Ellis, F. P., and Taylor, H. L. 共1976兲. ‘‘Mortality in St.
Louis, Missouri, during heat waves in 1936, 1953, 1954, 1955, and
1966.’’ Environ. Res., 12, 38 – 48.
Bross, M. H., Nash, B. T., and Carlton, F. B. 共1994兲. ‘‘Heat Emergencies.’’ Am. Fam. Physician, 50共2兲, 389–396.
Buechley, R. W., Van Bruggen, J., and Truppi, L. E. 共1972兲. ‘‘Heat
island⫽Death island?’’ Environ. Res., 5, 82–92.
Center for Disease Control 共CDC兲. 共1984兲. ‘‘Heat-associated mortality—
New York City.’’ MMWR Wkly., 33共29兲, 430– 432.
Center for Disease Control 共CDC兲. 共1994兲. ‘‘Heat-related deaths—
Philadelphia and United States 1993–1994.’’ MMWR Wkly., 43共25兲,
453– 455.

Center for Disease Control 共CDC兲. 共1995兲. ‘‘Heat-related illnesses and
deaths—United States 1994 –1995.’’ MMWR Wkly., 44共25兲, 465– 468.
Center for Disease Control 共CDC兲. 共1996兲. ‘‘Extreme heat: A prevention
guide to promote your personal health and safety,’’ U.S. Dept. of
Health and Human Services, Washington, D.C.
Chan, N. Y., Stacey, M. T., Smith, A. E., Ebi, K. L., and Wilson, T. F.
共2001兲. ‘‘An empirical mechanistic framework for heat related illness.’’ Clim. Res., 16, 133–143.
Chandler, T. J. 共1965兲. The climate of London, Hutchinson & Co., London.
Cova, T. J., and Church, R. L. 共1997兲. ‘‘Modelling community evacuation
vulnerability using GIS.’’ Int. J. Geograph. Inf. Sci., 11共8兲, 763–784.
Croner, C. M., Sperling, J., and Broome, F. R. 共1996兲. ‘‘Geographic information systems 共GIS兲: New perspectives in understanding human
health and environmental relationships.’’ Stat. Med., 15共17–18兲,
1961–1977.
Cutter, S. L. 共1996兲. ‘‘Vulnerability to environmental hazards.’’ Prog.
Human Geogr., 20共4兲, 529–539.
Cutter, S. L., Mitchell, J. T., and Scott, M. S. 共2000兲. ‘‘Revealing the
vulnerability of people and places: A case study of Georgetown
County, South Carolina.’’ Ann. Assoc. Am. Geogr., 90共4兲, 713–737.
Dangermond, J. 共1991兲. ‘‘Application of GIS to the International Decade
for Natural Disaster Reduction.’’ Proc., 4th Int. Conf. on Seismic Zonation, Earthquake Engineering Research Institute, Stanford Univ.,
Palo Alto, Calif., 445– 468.
Davis, R. E., Knappenberger, P. C., Novicoff, W. M., and Michaels, P. J.
共2001兲. ‘‘Decadal shifts in summer weather/mortality relationships in
the United States by region, demography, and cause of death.’’ Proc.
14th Conf. on Biometeorology and Aerobiology, American Meteorological Society, Boston.
DeMarrais, G. A. 共1961兲. ‘‘Vertical temperature difference observed over
an urban area.’’ Bull. Am. Meteorol. Soc., 42, 548.
Dow, K. 共1993兲. ‘‘Exploring differences in our common future共s兲: The
meaning of vulnerability to global environmental change.’’ Geoforum,
23共3兲, 417– 436.
Downing, T. E. 共1991兲. ‘‘Assessing socioeconomic vulnerability to famine: Frameworks, concepts, and applications.’’ Research Rep. The
Alan Shawn Feinstein World Hunger Program, Brown Univ., Providence, R.I.
Duckworth, F. S., and Sandburg, J. S. 共1954兲. ‘‘The effect of cities upon
horizontal and vertical temperature gradients.’’ Bull. Am. Meteorol.
Soc., 35共5兲, 198 –207.
Ellis, F. P. 共1972兲. ‘‘Mortality from heat illnesses and heat-aggravated
illness in the United States.’’ Environ. Res., 5, 1–58.
Ellis, F. P., and Nelson, F. 共1978兲. ‘‘Mortality in the elderly in a heat wave
in New York City.’’ Environ. Res., 15, 504 –512.
Fothergill, A., Meastas, E. G. M., and Darlington, J. D. 共1999兲. ‘‘Race,
ethnicity, and disasters in the United States: A review of the literature.’’ Disasters, 23共2兲, 156 –173.
Federal Emergency Management Agency 共FEMA兲. 共1994兲. ‘‘Assessment
of the state of the art earthquake loss estimation methodologies.’’
FEMA-249, FEMA, Washington, D.C.
Federal Emergency Management Agency 共FEMA兲. 共1996兲. ‘‘HAZUS, the
FEMA tool for estimating earthquake losses.’’ FEMA-287, FEMA,
Washington, D.C.
Frey, W. H., Abresch, B., and Yeasting, J. 共2001兲. America by the numbers: A field guide to the U.S. population, The New Press, New York.
Gallo, K. P., and Owen, T. W. 共2000兲. ‘‘Seasonal trends in urban and rural
temperatures of twenty-eight cities within the United States.’’ Proc.
3rd Symp. on the Urban Environment, August 14 –18, Davis, Calif.
American Meteorological Society, Boston, 14 –18
Gatrell, A.C., and Loytonen, M. 共1998兲. GIS and health, Taylor and Francis, London.
Glass, G. E., Schwartz, B. S., Morgan, J. M., Johnson, D. T., Noy, P. M.,
and Israel, E. 共1995兲. ‘‘Environmental risk-factors for lyme-disease
identified with geographic information systems.’’ Am. J. Public
Health, 85共7兲, 944 –948.
Greene, J. S., and Kalkstein, L. S. 共1996兲. ‘‘Quantitative analysis of sum-

156 / NATURAL HAZARDS REVIEW © ASCE / AUGUST 2004

Nat. Hazards Rev., 2004, 5(3): 147-158

Downloaded from ascelibrary.org by Arizona State Univ on 05/10/22. Copyright ASCE. For personal use only; all rights reserved.

mer air masses in the Eastern United States and an application to
human mortality.’’ Clim. Res., 7, 43–53.
Guest, C. S., et al. 共1999兲. ‘‘Climate and mortality in Australia: Retrospective study, 1979–1990 and predicted impacts in five major cities
in 2030.’’ Clim. Res., 13, 1–15.
Hjalmars, U., Kullforff, M., and Gustaffson, G. 共1996兲. ‘‘Childhood leukemia in Sweden: Using GIS and spatial scan statistics for cluster
detection.’’ Stat. Med., 15共7–9兲, 707–715.
Holm, D. M., Maslia, M. L., Reyes, J. J., Williams, R. C., and Aral, M.
M. 共1995兲. ‘‘Geographic information systems: A critical resource in
exposure assessment.’’ Superfund XVI Conf. and Exhibition Proc., 2,
Washington, D.C., 860– 866.
How, C.-K., Chern, C.-H., Wang, L.-M., and Lee, C.-H. 共2000兲. ‘‘Heat
stroke in a subtropical country.’’ Am. J. Emerg. Med., 18共4兲, 474 – 477.
Howard, L. 共1833兲. Climate of London deduced from meteorological observations, 3rd Ed., Harvey and Darton, London.
Intergovernmental Panel on Climate Change 共IPCC兲. 共2001兲. Climate
change 2001: The scientific basis, J. T. Houghton, et al., eds., Cambridge University Press, New York.
Jacquez, G. M. 共1998兲. ‘‘GIS as an enabling technology.’’ GIS and health,
A. C. Gatrell, and M. Loytonen, eds., Taylor and Francis, London,
17–28.
Jones, T. S., et al. 共1982兲. ‘‘Morbidity and mortality associated with the
July 1980 heat wave in St. Louis and Kansas City, MO.’’ JAMA, J.
Am. Med. Assoc., 247共24兲, 3327–3331.
Kalkstein, L. S. 共1991兲. ‘‘A new approach to evaluate the impact of climate on human mortality.’’ Environ. Health Perspect., 96, 145–150.
Kalkstein, L. S. 共1995兲. ‘‘Lessons from a very hot summer.’’ Lancet, 346,
857– 859.
Kalkstein, L. S. 共2000兲. ‘‘Saving lives during extreme weather in summer.’’ Br. Med. J., 231, 650– 651.
Kalkstein, L. S., and Greene, J. S. 共1997兲. ‘‘An evaluation of climate/
mortality relationships in large U.S. cities and the possible impacts of
a climate change.’’ Environ. Health Perspect., 105共1兲, 84 –93.
Kalkstein, L. S., Jamason, P. F., Greene, J. S., Libby, J., and Robinson, L.
共1996兲. ‘‘The Philadelphia hot weather-health watch/warning system:
Development and application, Summer 1995.’’ Bull. Am. Meteorol.
Soc., 77共7兲, 1519–1528.
Karl, T. R., and Knight, R. W. 共1997兲. ‘‘The 1995 Chicago heat wave:
How likely is a recurrence?’’ Bull. Am. Meteorol. Soc., 78共6兲, 1107–
1119.
Kattenberg, A., et al. 共1996兲. ‘‘Climate models—projections of future climate.’’ Climate change 1995. The science of climate change, J. T.
Houghton, et al., eds., Cambridge University Press, New York.
Kim, H. H. 共1992兲. ‘‘Urban heat island.’’ Int. J. Remote Sens., 13共12兲,
2319–2336.
Kitron, U., Pener, H., Costin, C., Orshan, L., Greenberg, Z., and Shalom,
U. 共1994兲. ‘‘Geographic information system in malaria surveillance—
mosquito breeding and imported cases in Israel.’’ Am. J. Trop. Med.
Hyg., 50共5兲, 550–556.
Landsberg, H. E. 共1970兲. ‘‘Man-made climatic changes.’’ Science,
170共3964兲, 1265–1274.
Landsberg, H. E. 共1981兲. The urban climate, Academic, San Diego.
Li, P. W., and Chan, S. T. 共2000兲. ‘‘Application of a weather stress index
for alerting the public to stressful weather in Hong Kong.’’ Meteorol.
Appl., 7, 369–375.
Lillesand, T. M., and Kiefer, R. W. 共1994兲. Remote sensing and image
interpretation, 3rd Ed., Wiley, New York.
Lo, C. P., Quattrochi, D. A., and Luvall, J. C. 共1997兲. ‘‘Application of
high-resolution thermal infrared remote sensing and GIS to assess the
urban heat island effect.’’ Int. J. Remote Sens., 18共2兲, 287–304.
Lowry, W. P. 共1967兲. ‘‘The climate of cities.’’ Sci. Am., 217共2兲, 15–23.
Marmor, M. 共1978兲. ‘‘Heat wave mortality in nursing homes.’’ Environ.
Res., 17, 102–115.
Martinez, B. F., Annest, J. L., Kilbourne, E. M., Kirk, M. L., Lui, K.-J.,
and Smith, S. M. 共1989兲. ‘‘Geographic distribution of heat-related
deaths among elderly persons.’’ JAMA, J. Am. Med. Assoc., 262共16兲,
2246 –2250.
Mattern, J., Garrigan, S., and Kennedy, S. B. 共2000兲. ‘‘A community-

based assessment of heat-related morbidity in north Philadelphia.’’
Environ. Res. Sec., 83, 338 –342.
McGeehin, M. A., and Mirabelli, M. 共2001兲. ‘‘The potential impacts of
climate variability and change on temperature-related morbidity and
mortality in the United States.’’ Environ. Health Perspect., 109共2兲,
185–189.
McMichael, A. J., Haines, A., Slooff, R., and Kovats, S. 共1996兲. Climate
change and human health, World Health Organization, Geneva, Switzerland.
Mitchell, J. M.Jr. 共1961兲. ‘‘The temperature of cities.’’ Weatherwise,
24共6兲, 224 –230.
Morrow, B. H. 共2000兲. Targeting households at risk for storms. Storms, R.
A. Pielke Sr., and R. A. Pielke Jr., eds., Routledge, London.
Myrup, L. O. 共1969兲. ‘‘A numerical model of the urban heat island.’’ J.
Appl. Meteorol., 8, 908 –918.
NOAA Coastal Service Center. 共1999兲. ‘‘Community vulnerability assessment tool. New Hanover County, North Carolina case study.’’ NOAA,
Silver Spring, Md. 具http://www.csc.noaa.gov/products/nchaz/
startup.htm典
NOAA National Weather Service Hydrometeorological Prediction Center.
共2002兲. ‘‘Mean heat index forecasts.’’ NOAA, Silver Spring, Md.
具http://www.hpc.ncep.noaa.gov/heat – index.shtml典
National Weather Service, Office of Climate, Water, and Weather Services. 共2001兲. ‘‘Natural Hazard Statistics.’’ 具http://www.nws.noaa.gov/
om/hazstats.htm#典 共April 2001兲.
Ngo, E. B. 共2001兲. ‘‘When disasters and age collide: Reviewing vulnerability of the elderly.’’ Nat. Hazards Rev., 2共2兲, 80– 89.
Nunez, M., and Oke, T. R. 共1977兲. ‘‘The energy balance of an urban
canyon.’’ J. Appl. Meteorol., 16, 11–19.
Oechsli, F. W., and Buechley, R. W. 共1970兲. ‘‘Excess mortality associated
with three Los Angeles September hot spells.’’ Environ. Res., 3, 277–
284.
Oke, T. R. 共1978兲. Boundary layer climates, Wiley, New York.
Oke, T. R. 共1997兲. ‘‘Urban climates and global environmental change.’’
Applied climatology: Principles and practice, R. D. Thompson, and
A. Perry, eds., Routledge, London, 273–287.
Palecki, M. A., Changnon, S. A., and Kunkel, K. E. 共2001兲. ‘‘The nature
and impacts of the July 1999 heat wave in the midwestern United
States: Learning from the lessons of 1995.’’ Bull. Am. Meteorol. Soc.,
82共7兲, 1353–1367.
Patz, J. A., et al. 共2000兲. ‘‘The potential health impacts of climate variability and change for the United States: Executive summary of the
report of the health sector of the U.S. national assessment.’’ Environ.
Health Perspect., 108共4兲, 367–376.
Perry, R. W., Greene, M., and Mushkatel, A. H. 共1983兲. ‘‘American minorities in disasters.’’ Final Rep., Battelle Human Affairs Research
Centers, Seattle, Wash.
Ramlow, J. M., and Kuller, L. H. 共1990兲. ‘‘Effects of the summer heat
wave of 1988 on daily mortality in Allegheny County, PA.’’ Public
Health Rep., 105共3兲, 283–289.
Richards, F. O. 共1993兲. ‘‘Uses of geographic information systems in control programs for onchocerciasis in Guatemala.’’ Bull. Pan Am. Health
Organ., 27, 52–55.
Rothman, K. J., and Greenland, S. 共1998兲. Modern epidemiology, 2nd Ed.
Lippincott-Raven, New York.
Rushton, G. 共1998兲. ‘‘Improving the geographic basis of health surveillance using GIS.’’ GIS and health, A. C. Gatrell, and M. Loytonen,
eds., Taylor and Francis, London.
Schuman, S. H. 共1972兲. ‘‘Patterns of urban heat-wave deaths and implications for prevention: Data from New York and St. Louis during July,
1966.’’ Environ. Res., 5, 59–75.
Semenza, J. C., et al. 共1996兲. ‘‘Heat-related deaths during the July 1995
heat wave in Chicago.’’ N. Engl. J. Med., 335共2兲, 84 –90.
Shipley, S. T., Graffman, I. A., and Ingram, J. K. 共2000兲. ‘‘GIS applications in climate and meteorology.’’ Proc., ESRI Annual User Conf.,
San Diego, ESRI, Redlands, Calif. 具http://www.esri.com/library/
userconf/proc00/professional/papers/PAP159/p159.htm典
Smoyer, K. E. 共1998兲. ‘‘A comparative analysis of heat waves and assoNATURAL HAZARDS REVIEW © ASCE / AUGUST 2004 / 157

Nat. Hazards Rev., 2004, 5(3): 147-158

Downloaded from ascelibrary.org by Arizona State Univ on 05/10/22. Copyright ASCE. For personal use only; all rights reserved.

ciated mortality in St. Louis, Missouri—1980 and 1995.’’ Int. J. Biometeor., 42, 44 –50.
Smoyer, K. E., Rainham, D. G. C., and Hewko, J. N. 共2000兲. ‘‘Heatstress-related mortality in five cities in Southern Ontario: 1980–
1996.’’ Int. J. Biometeor., 44, 190–197.
Steadman, R. G. 共1979兲. ‘‘The assessment of sultriness. Part I: A
temperature-humidity index based on human physiology and clothing
science.’’ J. Appl. Meteorol., 18, 861– 873.
Steadman, R. G. 共1984兲. ‘‘A universal scale of apparent temperature.’’ J.
Clim. Appl. Meteorol., 23, 1674 –1687.
Sunborg, A. 共1950兲. ‘‘Local climatological studies of the temperature
conditions in an urban area.’’ Tellus, 2, 222.
Taha, H., Akbari, H., Rosenfeld, A., and Huang, J. 共1988兲. ‘‘Residential
cooling loads and the urban heat island: The effects of albedo.’’ Energy Environ., 23共4兲, 271–283.

U.S. Census Bureau. 共2000兲. ‘‘Population projections.’’ Washington, D.C.
具http://www.census.gov/population/www/projections/popproj.html典
Vicente, G. A., and Maynard, N. 共2002兲. ‘‘Integrated system for health
applications of earth science remote sensing data.’’ Proc., IAPRS
Commission II Symp., 36共2兲.
Wartenberg, D. 共1992兲. ‘‘Screening for lead exposure using a geographic
information system.’’ Environ. Res., 59, 310–317.
Wilhelmi, O. V., and Brunskill, J. C. 共2003兲. ‘‘A meeting summary on a
GIS in the Atmospheric Sciences.’’ Bull. Am. Meterol. Soc., 84共10兲,
1409–1414.
Wilhelmi, O. V., and Wilhite, D. A. 共2002兲. ‘‘Assessing vulnerability to
agricultural drought: A Nebraska case study.’’ Natural Haz., 25共1兲,
37–58.
Xue, Y., Volz, P. A., and Cracknell, A. P. 共1999兲. ‘‘Human health applications of geographic information systems and remote sensing.’’
Michigan Academician XXXI, 31, 325–344.

158 / NATURAL HAZARDS REVIEW © ASCE / AUGUST 2004

Nat. Hazards Rev., 2004, 5(3): 147-158