Importance of Understanding and Managing Landscape Paper 1 page and half total, other requirements, see attached files Response #5 Part one of Analytical H

Importance of Understanding and Managing Landscape Paper 1 page and half total, other requirements, see attached files Response #5
Part one of Analytical Hierarchy Process (AHP) explains how AHP can be used to determine
which criteria amongst several in the design are the most crucial, and how to use this knowledge to
select the best design. An example of a problem is given and solved through development of a matrix
and assignment of rankings. Part two discusses about consistency, and it includes calculating consistency
vector and consistency index. I feel like AHP is a useful tool when making decisions, and it is based on
decision theory. These two videos simplify AHP by using an example and thereby make it easy to
understand how it works. In addition, it might be a useful tool when I get into my real estate
development career in the future, to determine which house/building I should buy or the orientation of
a house/building I should develop.
The reading, Landscape and urban planning (2009) discusses about site selection, and it suggests
a criteria for selecting a site for carp farming. According to the article, GIS and MCE of physical resources
are used to identify suitable farming sites in an urban area in Bangladesh. The article therefore confirms
the importance of site selection and use of technology in identifying suitable sites for specialized
farming activities. The other reading, renewable and sustainable energy reviews (2015), also applies GIS
and Multi-Criteria Decision-Making (MCDM) in selecting a suitable site for setting up a renewable energy
project in Morocco. By use of Analytical Hierarchy Process (AHP), four criteria are analyzed including
climate, land use, orography, and location, and climate turns out to be the most crucial in selecting a
suitable site. After the past 4 years of study in landscape architecture, landscape architecture is really a
broad field. Projects varied from someone’s backyard to energy. These two readings present a some
these fields can be combined, and work together. Additionally, Landscape and urban planning reading
shows the importance of site selection in carp farming, and it suggests that urban water bodies (UWBs)
need to be divided into various zones based on their suitability for carp farming. This, according to the
article, will provide investors with firsthand information about high potential areas. In my opinion, it is
important to have this kind of information readily available for investors. What’s more, use of GIS makes
things easier. However, the criteria for evaluating these zones are not explained, I think. It also
mentioned AHP, which is applied to find the most suitable site for renewable energy project. Various
criteria are evaluated before climate is determined to be the most important. This reading proves more
useful because it employs decision theory tools.
Furthermore, the last reading, Analytic Hierarchy Process Expert Survey, presents more detailed
explanations of what AHP is, which includes its history and how it is used. The steps in using AHP are
given, and they include the construction of pairwise comparison matrices and calculating the weightings.
In illustrating how AHP is used, an example based on five criteria is used. I believe this survey is very
useful in terms of explaining how AHP is used. It provides a step by step guidance on how the model can
be used. Also, it uses an example and includes all the calculations and explanations. It is also simple to
understand because it uses simple wordings to explain concepts.
In conclusion, AHP is a very important decision-making tool. Grounded on decision theory, AHP
helps in the selection of the best criteria which will lead to the selection of the best design. In the
articles used, AHP has been applied in selecting the suitable site for renewable energy project. It allows
decision makers to decide on the criteria to use when selecting among competing alternatives. It has
also been used in an example to select between public or private transport based on several decision
criteria such as convenient and environment impact. Thus, when difficulties are faced when selecting
among alternatives, AHP comes in handy to provide the best criteria on which to base decisions.
However, all these information and technique may not be suitable for a project outside of the US. I plan
to pick a site in China for my comprehensive project, and China is very restrictive about their ecological,
geographical info.
Reference list:
Analytic hierarchy process.


Hossain et al. 2009. Integration of GIS and multicriteria decision analysis for urban aquaculture
development in Bangladesh. Landscape and Urban Planning
Tahriet al., 2015. The evaluation of solar farm locations applying Geographic Information System and
Multi-Criteria Decision-Making methods: Case study in southern Morocco. Renewable and
Sustainable Energy Reviews
Upshaw, M. 2015. Analytic Hierarchy Process Expert Survey. Duke University
International Journal of
Environmental Research
and Public Health
Article
Assessing Vulnerability to Urban Heat: A Study of
Disproportionate Heat Exposure and Access to Refuge
by Socio-Demographic Status in Portland, Oregon
Jackson Voelkel 1
1
2
*
ID
, Dana Hellman 1 , Ryu Sakuma 2 and Vivek Shandas 1, *
School of Urban Studies and Planning, Portland State University, Portland, OR 97201, USA;
jvoelkel@pdx.edu (J.V.); dhellman@pdx.edu (D.H.)
Peace Winds Japan, Tokyo 151-0063, Japan; lyu.sakuma@gmail.com
Correspondence: vshandas@pdx.edu; Tel.: +1-503-725-5222

Received: 14 December 2017; Accepted: 14 March 2018; Published: 30 March 2018
Abstract: Extreme urban heat is a powerful environmental stressor which poses a significant threat
to human health and well-being. Exacerbated by the urban heat island phenomenon, heat events
are expected to become more intense and frequent as climate change progresses, though we have
limited understanding of the impact of such events on vulnerable populations at a neighborhood
or census block group level. Focusing on the City of Portland, Oregon, this study aimed to
determine which socio-demographic populations experience disproportionate exposure to extreme
heat, as well as the level of access to refuge in the form of public cooling centers or residential central
air conditioning. During a 2014 heat wave, temperature data were recorded using a vehicle-traverse
collection method, then extrapolated to determine average temperature at the census block group
level. Socio-demographic factors including income, race, education, age, and English speaking ability
were tested using statistical assessments to identify significant relationships with heat exposure
and access to refuge from extreme heat. Results indicate that groups with limited adaptive capacity,
including those in poverty and non-white populations, are at higher risk for heat exposure, suggesting
an emerging concern of environmental justice as it relates to climate change. The paper concludes
by emphasizing the importance of cultural sensitivity and inclusion, in combination with effectively
distributing cooling centers in areas where the greatest burden befalls vulnerable populations.
Keywords: urban heat; vulnerability; environmental justice; heat exposure; resilience
1. Introduction
Extreme heat poses a growing threat to human populations, with numerous implications for
public health, economic stability, and quality of life [1–3]. Past heat waves have had devastating,
deadly outcomes worldwide [4–6], and such events are expected to increase in intensity, frequency,
and duration as climate change progresses [7,8]. Although human settlements of any type may
experience the negative effects of extreme heat, these are and will continue to be most pronounced in
urban areas, the development practices of which are highly correlated with rising temperatures [9–11].
Currently, more than 50% of the world’s population is located in urban areas, and that figure is expected
to reach over 66% by 2050 [12]; with so many people potentially at risk of exposure, it is imperative
that local governments and planning practitioners recognize varying degrees of vulnerability among
urban residents.
Urban heat events—defined as those above the 90th percentile of historic temperatures [13]—are
an environmental stressor, placing economic, infrastructure, and human health burdens on society [14–16].
As a stressor, urban heat can create vulnerabilities, which may be understood as a combination of three
Int. J. Environ. Res. Public Health 2018, 15, 640; doi:10.3390/ijerph15040640
www.mdpi.com/journal/ijerph
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factors [17]: exposure, sensitivity, and adaptive capacity. Exposure refers to an individual’s contact with
a stressor, either from living, working, or spending time in an affected location. Sensitivity is the point
at which exposure becomes dangerous to an individual’s health [18]. Finally, adaptive capacity refers to
one’s ability to change exposure or sensitivity, or to cope with an extreme event. Regarding urban heat,
indicators believed to enhance adaptive capacity include high income, social cohesion, and knowledge
of hazardous environments [19,20]. Given these conditions, it may be reasonable to categorize extreme
heat exposure as an environmental justice issue.
The phenomenon central to this study is the urban heat island (UHI) effect, which has been
known to researchers since the mid-19th century, and indicates a strong correlation between urban
environments and high temperatures [21,22]. Impervious surfaces and anthropogenic activity within
cities portend rising temperatures, as does the relative scarcity of heat-ameliorating elements such as
trees and grasses [23–25]. A higher frequency of regional extreme heat events, as such, will amplify
temperatures [8], and generate UHI in areas that have greater amounts of heat absorbing surfaces.
While early studies focused on the comparative temperatures between urban and non-urban regions,
the emergence of mobile sensors, highly accurate global positioning systems, and computational
software allows for the comparison of intra-urban spaces, measuring variation in temperature
distribution across a single city [26,27]. Past research has utilized infrared satellite data for this
purpose [10,28,29], though vehicle-based traverse measurements (used in this study) can provide
a detailed representation of heat exposure at a smaller scale [30–33].
From an environmental justice point of view, the existing research emphasizes point source
pollution [34–36], though it has become clear that climate change affects communities differentially
and creates novel impacts never before witnessed in traditional environmental research. As such, we
argue that climate change is catalyst for injustice. While some of the effects can be easily observed
at the national level, particularly in the world’s poorest countries [37,38], there is relatively little
understanding of the impact at a more granular scale. Needed are approaches—methodological,
conceptual, and pragmatic—that help us to identify those communities disproportionately affected
by UHI, and strategies that can help to reduce vulnerabilities. This study provides new evidence of
disproportionate exposure to climate change at a local level, as well as access to refuge, an understudied
facet of adaptive capacity.
Previous studies indicate a relationship between socio-demographic factors and heat-related
morbidity/mortality [39,40]. Income is quite predictive of vulnerability (inverse relationship) [41–43],
though it is possible that other indicators also play a role. If so, urban heat exposure may be framed
as an environmental justice or ‘climate justice’ [44] issue, disproportionately affecting marginalized
socio-demographic groups with limited adaptive capacity. This study aims to identify such populations
in Portland, Oregon by assessing (1) disproportionate heat exposure among socio-demographic groups;
and (2) disproportionate access to refuge (either public refuge facilities or residential central air
conditioning), resulting in heightened or lowered adaptive capacity. An in-depth statistical and spatial
analysis will reveal significant, inequitable relationships, validating the application of an environmental
justice lens in addressing urban heat resilience.
2. Materials and Methods
2.1. Study Area
Our assessment occurs in a Pacific Northwest city of the United States. The City of Portland,
Oregon is located at approximately 45.5? North, 122.6? West, at the confluence of the Willamette and
Columbia Rivers. The city covers approximately 345 square kilometers, with of a population of nearly
640,000 as of 2016 [45]. The City of Maywood Park, Oregon is located within northeast Portland
and, though an enclave of the City of Portland, has been excluded from the study. Due to the fact
that summer temperatures have an average monthly highs of 22.7 ? C, 26.4 ? C, and 26.7 ? C for June,
July, and August, respectively [46], Portland offers several advantages to conducting an assessment
Int. J. Environ. Res. Public Health 2018, 15, 640
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of disproportionate effects of urban heat. First, historical high temperatures have rarely exceeded
35 ? C, which reduces public concern for heat related illnesses. Second, as of 2013, fewer than 35% of
households had air conditioning [47], and communities may not have immediately available private
residences to consider refuge from heat waves. Finally, Portland Climate Action Plan (2015) explicitly
addresses the importance of reducing disproportionate exposure to urban heat waves, yet few actions
have materialized.
2.2. Data
Three main data types were used in this study, all at the U.S. census block group level:
socio-demographic indicators, distribution of ambient temperatures in the study location, and refuge
availability. Socio-demographic data used include: income (percent of the population below 50% of
the poverty line); race (percent of the population who do not self-identify as white); education
(percent of the population over 25 years old without a high school degree or equivalent); age
(percent of the population over 65 years old that lives alone); and English speaking ability (percent
of the population that claims to have poor or no English skills). Obtained from the U.S. Census
Bureau’s American Community Survey 5-year estimates, 2009–2013 [47], these data reflect categories
into which individuals have self-identified. Another, non-census piece of data used to highlight
socio-demographic status is presence of affordable housing, obtained from Oregon Metro’s Regional
Land Information System [47]. In order to differentiate “low” and “high” categories used in the analysis,
a model-based clustering algorithm was used to split each variable [48].
Following an established protocol [13], we collected approximately 60,000 temperature readings
during one day of an extreme heat event on 25 August 2014, in Portland, Oregon, when the average
temperature during the hottest hour of the day was in the 90th percentile of 30-year historic daily
temperatures for the study region. We sampled temperatures for one hour at 3 times during the day
(6 a.m., 3 p.m., and 7 p.m.) using vehicle traverses (cars with a mounted temperature sensor and
global positioning system (GPS)) in six predetermined sections of the city. The temperature sensor
consisted of a type T-fine (30 gauge) thermocouple in a plastic shade tube (12 cm in length and 2.5 cm
in diameter) mounted on the passenger-side window approximately 25 cm above the roof of each
of 5 vehicles deployed. Each temperature sensor was connected to a data-logging device with an
estimated system accuracy of ±0.5 ? C and a 90% response time of less than 60 s in 1 m/s airflow.
A GPS unit on each vehicle paired temperature measurement and location.
Based on the results from the temperature collection and subsequent modeling, we created three
separate heat surfaces, which are continuous descriptions of temperature variation across the study
region, corresponding to the three time periods. The resulting maps consisted of a 32-bit floating
point 1-meter raster format and contain 449,359,188 pixels for each of the three time periods [13,49].
These three urban heat models were created using random forest machine learning on temperature
data collected using vehicle-based traverse measurements. Multiple land uses are included in the model
(e.g., tree cover, building volume), and the temperatures derived are representative of the underlying
urban form. Earlier research suggests that evening temperatures can have the greatest impact on
human health, in part due to the exposure overnight, when physiological responses [50–52] can be
acute among those with pre-existing health conditions. As a result, this study utilizes the evening
temperature model in an attempt to identify areas with prolonged exposure to high temperatures.
The 7 pm model has an R2 of 0.9715 and an RMSE of 0.2078. Using “zonal statistics” in ArcGIS (ESRI,
Redlands, CA, USA), average UHI temperature was calculated for each census block, ranging from
26.4 to 30.1 ? C. This aggregation method simplifies the UHI dataset, however this alteration of the raw
data is deemed worthwhile in order to assess relationships with demographic data. Additionally,
this is a common practice in geographic analysis [15,53,54].
In the context of this study, “refuge” refers either to public cooling facilities, or availability of
central air conditioning in one’s home; in other words, the availability of coping mechanisms. Public
heat refuge data were obtained from the Multnomah County Office of Aging, Disability and Veterans
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Services [55]. These include three County cooling centers; 33 places to play in the water; 59 libraries;
and 73 community centers. The heat refuges were geocoded using Google Earth. Nine out of 33 places
to play in the water are not free for personal use, but are treated as such for the purpose of this
study. Residential Central Air Conditioning (CAC) data were obtained from the Multnomah County
Assessment Office [55].
2.3. Analysis
This study assessed multiple facets of vulnerability through the use of mixed spatial and
statistical methods, with the aim of identifying not only those hottest areas of the city, but also
trends of socio-demographic disparity. Elements considered include exposure of sensitive populations,
as well as their ability to cope with heat by accessing refuge.
First, heat exposure was determined by mapping UHI data at the census block group (CBG)
level. Using the “raster” package in R statistical software, the mean of all pixels falling geographically
within an individual CBG polygon was appended onto that polygon’s data table, resulting in a visual
representation of spatial temperature distribution. This indicated areas of the city most exposed to
extreme heat.
Second, the relationship between various socio-demographic groups and high-exposure areas
was assessed using the Student’s t-test method, where ? = 0.05 for all tests. This method reveals
which sensitive groups, if any, are disproportionately exposed to extreme heat conditions. In each
case, two groups are compared: those with low adaptive capacity characteristics, and those with
high adaptive capacity characteristics. Indicators included in this analysis, bifurcations of each,
and hypotheses tested are as follows.
H0: Average of low income population ? Average of high income population = 0
H1: Average of low income population ? Average of high income population > 0
where:
Average of low income population = Mean temperature of low income block group
Average of high income population = Mean temperature of high income block group
H0: Average of non-white populati…
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