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Using a GIS implementation to examine
Epidemiology of Autism in
New York, New Jersey and Florida
Rehab Uosef
Brooklyn College
Dr. Rebecca Boger
Thesis Research Report
Spring 2013
Table of Contents
Table of Contents .......................................................................................................................................... 2
Figures/Tables ............................................................................................................................................... 3
Introduction .................................................................................................................................................. 4
Background ................................................................................................................................................... 5
Overview ................................................................................................................................................... 5
GIS: What and Why Use ............................................................................................................................ 6
GIS applications in epidemiology .............................................................................................................. 8
Case studies: GIS and epidemiology ................................................................................................... 10
Study areas.......................................................................................................................................... 13
GIS and Autism ........................................................................................................................................ 14
Prevalence ........................................................................................................................................... 14
Methods ...................................................................................................................................................... 15
Autism GIS Prototype .............................................................................................................................. 15
Web based map development ................................................................................................................ 20
Results and discussion ................................................................................................................................ 22
Autism GIS Prototype results .................................................................................................................. 22
Conclusion and future work ........................................................................................................................ 23
References .............................................................................................................................................. 24
Figures/Tables
Figure 1: “Infection Watch live” areal map................................................................................................. 11
Figure 2: Easy-to-use interface ................................................................................................................... 12
Figure 3: Easy-to-use interface ................................................................................................................... 13
Figure 4: Link - WWW.UosefLabs.COM ...................................................................................................... 21
Table 1: 'Autism GIS Data Layers' ………………………………………………………………………………………………………… 20
Introduction
This thesis shows a web-based Autism GIS prototype that displays the epidemiology of
children with Autism using an online Geographic Information System (GIS). Autism, along with
Autism spectrum disorder (ASD), is a term used to define a particular group of disorders
associated with the brain. These disorders are recognized as developmental disabilities
diagnosed early on in childhood that may affect children’s social, communicative and
behavioral attributes. It is estimated that 1.5 million children in the US have Autism. Prevalence
of Autism is steadily on the rise1.
The Autism GIS prototype examines various layers in a unified GIS system called “GIS
Epidemiology - Infectious Disease”. This Autism GIS prototype and associated research are
available at http://bit.ly/1gNMwxh. This map is also embedded into the web site
(www.UosefLabs.com) for this Autism GIS prototype. The Autism GIS prototype allows the user
to extract data, query data, and construct tables. Relationships within and among data layers
can be examined, which can lead to further investigations. This Autism GIS prototype is
designed to be used by a variety of users including GIS professionals or by families who are
interested in viewing information visually. As a web-based tool, it can reach a wide audience.
The goal is to provide for an alternative way of viewing research, data and statistics in a webbased GIS system where visualization is an important factor.
In future iterations of this Autism GIS prototype, statistical tools can be applied such as
‘Google Trends’ which can help in further analyzing the data a user is exploring. Further details
on ‘Google Trends’ and other technologies that can be useful in future iterations will be
discussed in more detail below in the “GIS applications in epidemiology” section of this report.
Other iterations can include the ability for users to upload to the Autism GIS prototype site their
own map version; allowing multiple users to view and cross-reference maps with one another.
1
http://www.Autism-help.org/points-Autism-epidemic.htm
Background
Overview
This thesis shows a web-based geographic information system (GIS) Autism prototype
that displays the epidemiology of children with Autism using an online technology known as
ArcGIS. The ArcGIS technology is produced by a company called ESRI; ESRI works with maps
and geographic information to create a geographic information system known as ArcGIS. For
the purpose of this project ArcGIS was used and embedded in a website that includes other
relevant information on Autism. Autism, along with Autism spectrum disorder (ASD), is a term
used as a broad definition to generalize a particular group of disorders associated with the
brain. They are recognized as developmental disabilities diagnosed early on in childhood that
may affect children’s social, communicative and behavioral attributes. It is estimated that 1.5
million children in the United States (US) have Autism. Prevalence of Autism is steadily on the
rise2. After an examination of existing websites it was found that there is a lack of information
and tools that track the epidemiology of children with Autism in the United States while
integrating disease prevalence with socioeconomic and environmental factors such as income,
urbanization, population, hospital access, among others (see ‘GIS AND AUTISM’ section). For
this reason alone, the development of a tool like the one in this project is extremely valuable.
This GIS Autism GIS prototype can help to spread awareness and information about this steadily
growing disease.
This Autism GIS prototype focuses on three geographic areas in the United States – the
states of Florida, New Jersey and New York. Florida was chosen since it has low prevalence of
Autism, New Jersey has high and New York is in the middle. The datasets chosen and the data
mining techniques are discussed in the methods section. Some of the datasets are contextual in
that they enable the user to easily understand where the data are located; other datasets are
used to explore patterns and relationships within and among datasets. Output products (e.g.,
maps, tables and reports) can be used for many purposes such as exploratory research to
develop hypothesis for causal relationships pertaining to Autism, development of strategies to
lessen and eventually eradicate the disease, and general education.
The intention of this project is to enable a wide range of users online to have access to
information that may assist them in better understanding the epidemiology of Autism. The
design of the GIS tool and website is meant to be simple and easy to use by diverse audiences.
Both the Autism GIS prototype built for this project as well as the GIS tool case study both
protect the privacy of the patients by not displaying their identity. Instead, the Autism GIS
prototype displays each patient or position of data as a “point” or “area” of reference on the
map. It does not represent an address or specific location but rather an area or region.
2
http://www.Autism-help.org/points-Autism-epidemic.htm
This project includes the Autism GIS prototype, a website (WWW.UosefLabs.COM) that
includes the Autism GIS prototype available at http://bit.ly/1gNMwxh, a PowerPoint explaining
how to use the GIS tool data analysis that can be accessed via the website and the report
presented here. This thesis begins with a background on GIS that explains what GIS is and how
it is implemented, as well as how it makes an existing system such as a Management
Information System (MIS) or database more effective. Additionally, this section covers study
areas that examine the success of GIS in various other projects. Following the background, the
prevalence of Autism and statistics on its rate are discussed as well as connecting the use of a
GIS tool with the study of infectious disease; particularly Autism. This is followed by a review of
case studies, in particular, a study known as ‘Infection Watch Live’ which influenced the
development of this (Autism) Autism GIS prototype. The methods section discusses of the
techniques used to develop the Autism GIS prototype, including the code used to build the web
site, the application used to build the map and the datasets that were incorporated. The
analysis and outcomes of the Autism GIS prototype are discussed in the results and discussion
section. Lastly, the last section presents final conclusions and future work for the next
iteration of the Autism GIS prototype.
GIS: What and Why Use
Management information systems (MIS) is a method of gathering and analyzing data
that provides statistics, analyzes functions, and produces useful reports from its data. It can be
used to explore the epidemiology of a particular event or occurrence. Epidemiology looks at
patterns and characteristics of health related “events” that occur in particular populations and
analyzes what their causes might be, and what influences the society, environment, or other
surroundings may have on these “events”. Although MIS produces valuable information, this
method lacks the ability of capturing and displaying the information visually on a map so that
important information such as location, environmental conditions and population can be better
understood. This is where the success of implementing geographic information system (GIS)
comes into play.
GIS is used to capture, store and manipulate spatial data. Almost all types of data can
contain a geo-reference, which enables the data to be defined in physical space, and therefore
be used and integrated with other datasets in a GIS. These datasets can be reported, analyzed
and leveraged in almost all areas of study. These systems are interactive and easily interfaced
with other systems. In fact, the power of GIS is heightened because the existing
implementation of MIS can remain within the GIS. In other words, GIS can co-exist and interface
with other useful systems. GIS can also have built in MIS characteristics, eliminating the need
for two or more separate systems.
The GIS framework provides tools for data analysis not available in other types of
systems, and can enable the user to better interpret the data. It can search and bring together
multiple types of data in different formats. The map data can be filtered and queried, as well as
manipulated to produce outputs such as reports, charts, graphs, and tables. These different
data outputs show the versatility of a GIS system to integrate different types of systems, such
as MIS systems, expert systems, and reporting systems into one system with added features of
spatial visibility. This versatility provides for a better rate of success when analyzing data and
creating hypothesis.
The ability of a GIS to incorporate both tables of data (attributes) and spatial data
enables GIS to become a very useful tool when making critical decisions. It is useful in
answering questions such as “What is the geographical pattern of the disease rate of x in this
region and tell me why I should be confident there is no reason to be concerned about the high
rate here?” Gattrell and Loytonen (1995) explain that the public is more interested in the
onset and early stages of diseases, and in understanding patterns of disease spread. GIS is
useful for providing the public with an exploratory spatial analysis of a how a disease spreads
and its trends (Bailey and Gatrell, 1995).
A brief examination of how South Carolina Department of Health and Environmental
Control implemented a GIS illustrates how GIS can be used in health related applications. After
understanding their needs, this health organization implemented an ESRI GIS tool into a
website that would provide for more efficient distribution of health information for various
audiences. This implementation showed extremely positive results, Jared Shoultz, Informatics
Manager, Division of Biostatistics and Health GIS, PHSIS, SC DHEC shared that, “ESRI software
has helped us keep data current and accessible. Instead of doing this annually, we are able to
save a considerable amount of time and man-hours with GIS.”3
GIS is used for management and basic and applied research.4 It is used to improve the
rate at which health outbreaks and diseases are identified, tracked, and ultimately controlled.
The implementation of GIS to help improve the rate at which health outbreaks and diseases are
identified and controlled is more of a technological solution rather than a research based
solution.5 The technology can be leveraged for research purposes by gathering and integrating
data for analysis. It has helped in many areas where resources are limited for research and
disease outbreaks are at a rise.6
3
http://www.in.gov/gis/files/Ortho-LiDAR_Uses.pdf
4
http://www.esri.com/library/brochures/pdfs/gis-for-retail.pdf
5
http://www.who.int/tdr/publications/documents/seb_topic3.pdf
6
http://www.directionsmag.com/articles/gis-for-planning-and-community-development-solving-global-challenges/149245
GIS provides several advantages when used as an infectious disease management
system:7 These include:
1. Since GIS is a subset of MIS, GIS inherits the functions of MIS tools such as data mining,
saving, processing, and analyzing of data.
2. GIS makes it easier to monitor and control an epidemic event. Data of the population
on a map help to better understand trends more quickly and efficiently.
3. GIS makes the analysis of an epidemic event not only more efficient but much safer as
well. For instance, Ku and Wu (2010) explain that, “the department of disease control
could integrate each kind of infectious disease spread model to analyze the high
dangerous area, divide the dangerous area of infectious disease, and notify people of
the correct prevent method”.
4. GIS can be combined with the Web to create an interactive website. Ku and Wu (2010)
explain that, “By the Web GIS technology, we can announce the epidemic situation and
related statistical data on web, and then people know the present domestic epidemic
situation.”
The development of web-based GIS tools is rapidly evolving. There are a variety of tools
available, such as “OpenMap”8, that do not charge the user. Environmental Systems Research
Institute Inc (Esri©) offers both free-of-charge and purchased tools. Google maps and Google
analytics are other tools freely available for users to implement in their project. Google
software can be implemented alongside an ESRI project implementation or an OpenMap
project. This integration of tools provides powerful web-based applications.
The web-based tool used for the Autism GIS prototype is online ArcGIS created by ESRI.
ESRI is an organization that supplies desktop and web-based GIS tools, software and
informational documentations and databases. Online ArcGIS was chosen since this is a
framework available at Brooklyn College and it has a user-friendly interface and useful built in
tools to customize projects.
GIS applications in epidemiology
The ability to track diseases and their outbreaks is extremely important to prevent
further spreading while bringing the disease under control. GIS has advanced the surveillance
of tracking diseases, and is widely used throughout the world. Mapping diseases can be traced
back to 1854 when Dr. John Snow combined geospatial information on paper maps to analyze
7
“The Application of GIS for Infectious Disease” by Wen-Yuan Ku (System Engineer of GIS center, Feng Chia University in Taiwan) and Jing-Ming
Wu (Project Manager of GIS center, Feng Chia University in Taiwan (Aug. 11, 2010)
8
https://code.google.com/p/openmap/
the cholera deaths and found clusters around water pumps. Over the years, mapping of
diseases has expanded tremendously. To illustrate the pervasiveness of GIS in the
epidemiology, “s[S]ince 1993, WHO’s Public Health Mapping and GIS program has been leading
a global partnership in the promotion and implementation of GIS to support decision making
for a wide range of infectious disease and public health programs.”9 . In their research, Bindu
and Janak (2009),10 explain that GIS provides for better visualization and understanding of
relationships between different factors versus the more traditional methods of viewing data in
a MIS or other database.
The use of online GIS has been increasing due to the advancement in the tools including
ESRI’s ArcGIS, OpenMap’s free software and Google applications such as maps and analytics.
This section will explore how different projects have taken advantage of these advancements.
“Google trends”, as previously mentioned, is a technology which can be easily
implemented and extremely useful in future iterations of this Autism GIS prototype. For the
purpose of this Autism GIS prototype “Google trends” can allow data gathering of Internet
searches. This data keeps track of users search terms, phrases, and occurrences of searches
terms. This allows “Google trends” users implementing the technology the ability to track
trending topics of all kinds based on what users search for in the Google search engine. In
tracking what a user searches, the tool also tracks where the user was located when using the
search tool (not precise location, but area or region instead). This can provide for greater detail
about a trending topic occurring in a particular area. ‘Google trends’ is different from ordinary
Google Internet searches in that as mentioned ‘Google trends’ tracks number of occurrences a
particular search is entered by the user as well as its location. In using ‘Google trends’ a user
has more data available to them such as being able to view a chart or graph of all other users
who search for a particular thing. This is different from regular Google searches because all of
the additional data available in ‘Google trends’ is hidden from the user and not shown, because
the user is using the regular search engine to simply find something they are inquiring about.
‘Google trends’ provides the user with metrics, charts and GIS data visualization. ‘Google
trends’ allows the user to use existing tools built elsewhere, similar to plug-in tools, to broaden
the types of applications and products produced. For the Autism GIS prototype using “Google
trends” in a future iteration will help gather, share and analyze data trends and patterns across
a much larger audience that can interact with one another.
9
World Health Organization: WHO Public Health, Mapping and GIS Programme 2007. www.who.int/health_mapping/
10
http://www.academia.edu/1454694/Identifying_Malaria_Risk_Zones_Using_GIS-_A_Study_of_Vadodara_City
Case studies: GIS and epidemiology
This section will review several successful web-based GIS epidemiological applications as
well as discussing one, Infection Watch Live (http://www.isdsjournal.org/articles/3320.pdf) in
greater depth. Through this analysis, elements of the tools have been applied to the Autism GIS
prototype. The following implementations were chosen because they are web-based tools
which display how the implementation of a GIS tool provides for a more effective outcome.
Engest and Jensen of Geodata AS document a Le gionnaires outbreak in Norway. A
hospital alerted Norwegian national authorities about an outbreak of Legionnaires’ disease in
over 50 patients scattered over a large area. To identify connections among these spatially
dispersed patents, researchers applied ArcGIS software (ESRI). The analysis revealed that a
commercial air scrubber released infected water droplets into the air.11 This project used ESRI
products to create a web-based application. An important lesson learned from this research
was the inclusion of many different factors that could be connected to the disease outbreak. In
this particular project for Autism, uncommon factors such as population and other socioeconomic factors were examined. This project examined commercial air scrubbers that were
also present at the time the incidents occurred.
“Infection Watch Live” has strongly influenced this project since many of its
implementation strategies can be easily adopted for this project and others. In “Infection
Watch Live” GIS was successfully implemented to track cases of infectious disease in respiratory
and gastrointestinal patients admitted to selected area hospitals in Ontario, Canada. This
system uses a web-based GIS model that is accessible and easy to use by doctors, patients and
other city officials. It protects the privacy of the patients by not displaying their identity; rather
the system displays each patient as a point of reference on the map.
“Infection Watch Live” allows health service groups in Ontario to generate real-time maps
that display respiratory and gastrointestinal data reported in hospital emergency rooms.12 The
website gives the users a bird’s eye-view of expected spikes in reported illnesses. The maps
depicted with the data provide important information for decision makers such as family
physicians, long-term care facilities, school and childcare center administrators, public health
workers, and the general public, all who can be properly informed. It has become an invaluable
tool for public health to identify infectious disease risks early.13
11
http://www.esri.com/library/bestpractices/early-detection.pdf
12
“Early Detection and Response to Infectious Disease” http://www.esri.com/library/bestpractices/early-detection.pdf
13
http://www.cdc.gov/mmwr/preview/mmwrhtml/rr6005a1.htm
Figure 1: “Infection Watch live” area map
The developers of “Infection Watch Live” decided to track two specific variables,
respiratory and gastrointestinal complaints. These were chosen because they appeared to have
the highest burden (affected people) as well as the fastest transmission rates in community
health services. One of the many advantages of this system is its real-time capability. It collects
data from nine area hospitals in an adaptive technique whereby the system identifies patterns
and traits, analyzes data, and visually plots data on web-based maps. Background data layers in
the tool include satellite images and digital maps provided by the Canadian Geospatial Data
Infrastructure. This application also includes a special algorithm developed by a public health
epidemiologist to represent the seasonal patterns of respiratory and gastrointestinal infections
in the community.
Below are screen shots of the application and its easy-to-use interface. Its simplicity
makes it easily understandable, even for the less experienced users.
Figure 2: Easy-to-use interface
An easy-to-use interface to the Infection Watch Live Web page allows visitors to customize
maps of historic data on reported respiratory and gastrointestinal illnesses by date and region.
14
14
visit www.kfl apublichealth.ca/
Figure 3: Easy-to-use interface
An easy-to-use interface to the Infection Watch Live Web page allows visitors to customize
maps of historic data on reported respiratory and gastrointestinal illnesses by date and
region.15
The Autism GIS prototype includes many of the features used in ‘Infection Watch Live’. In
particular, this Autism GIS prototype has an easy mapping interface using ArcGIS and is
designed for a variety of users. Because of its diverse audience, the Autism GIS prototype
presents data in a variety of ways and enables the user to view a variety of datasets together.
Although the Autism GIS prototype does not include live updated data, it is a feature to
consider for future iterations. ’Infection Watch Live’ protects the privacy of users by not
displaying their exact identity or location.
Study areas
To develop this Autism GIS prototype, data and research was selected from three states
that have reported high, medium, and low levels of Autism in the United States. New Jersey
represents a state with one of the highest rates of Autism; Florida has one of the lowest rates of
Autism; New York was also included as a state with a fairly medium rate of Autism. These three
particular states were chosen because in two separate well documented studies conducted in
2008 by the ADDM group under the CDC and by the IDEA, New Jersey showed a ratio of Autism
15
visit www.kfl apublichealth.ca/
prevalence of about 4.7 per 1000 cases, New York, 4.0 per 1000 cases and Florida 3.4 per 1000
cases examined in the population. Within those three states the Autism GIS prototype displays
environmental and socio-economic data. Several of the layers used are available online and
being used elsewhere for other purposes while other layers in the Autism GIS prototype were
developed for this Autism GIS prototype.
GIS and Autism
Prevalence
In a recent study conducted by the ADDM network under the CDC, it is reported that 8year-old children, specifically, were examined because most Autism is diagnosed by that age.
Health and school records were checked to see which children met the criteria for Autism. The
2002 report findings estimated that about 1 in 150 children that age were autistic. In 2006
new data was released revising that figure to about 1 in 110. Similarly the estimate released in
2012 (based on 2008 findings) show the numbers to be 1 in 88. 16 This study also found that
most children diagnosed with Autism were not categorized as having an intellectual disability;
that the majority of the population, about three-fourths actually had average or above average
IQ’s. This contradicted a past assumption about most autistic children having IQ’s below 70,
which would be considered an intellectual disability.17 Boys were also seen to be 5 times more
likely than girls to be diagnosed with Autism. Another alarming finding in this study is that 1 in
49 8-year-olds in New Jersey were diagnosed with Autism.18
An estimated 1.5 million individuals in the U.S have Autism19. Furthermore, Autism prevalence
rates are growing at the fastest rate when compared with other childhood diseases such as
mental retardation, Down syndrome, and cystic fibrosis. This increase may be attributed to
better methods of identifying Autism, heightened awareness, and the disease itself being on
the rise. Despite the high rate of increase, Autism is the least funded childhood disease. The
U.S. spends over $137 billion per year in costs for Autism alone.20 In comparison to the costs of
other diseases, the increasing costs of Autism puts it as one of the most costly diseases to care
for and at its rate will be the most expensive in the future.21
16
http://www.nola.com/health/index.ssf/2012/03/Autism_rates_up_due_to_wider_s.html
17
http://www.intellectualdisability.info/diagnosis/Autism
18
http://www.cdc.gov/ncbddd/Autism/states/ADDM-New-Jersey-fact-sheet.pdf
19
http://www.cdc.gov/ncbddd/Autism/data.html
20
http://www.Autismspeaks.org/science/science-news/Autism%E2%80%99s-costs-nation-reach-137-billion-year
21
http://www.ncbi.nlm.nih.gov/pubmed/17690969
Methods
Autism GIS Prototype
Online ArcGIS was the GIS tool used to create the interactive map that would become
imbedded in a website for this project. Desktop and online ArcGIS are widely used in
educational institutions as well as in professional settings for viewing querying, analyzing and
visualizing spatial data. It was chosen because of its availability at Brooklyn College and it has an
easy and flexible interface (as well as being implemented by the ‘Infection Watch Live’ project).
To start an interactive map, an empty project is created followed by a basemap onto
which other data layers are added. The basemap used for this Autism GIS prototype is a generic
world topographic map that contains geographic features, such as mountains, plains, valleys
and water bodies. After the basemap was selected, several data layers were added. Existing
datasets were searched on the Internet and added via ArcGIS online tools. Other layers were
constructed based on data points in a spreadsheet and added via a Comma separated values
(CSV/.csv) excel file. The format of the spreadsheet requires two columns for longitude and
latitude coordinates along with the attribute data being depicted on the map.
ArcGIS framework allows the user to include many data formats including database files
(.csv, .dbf), shapefiles (an ESRI format), image files (.gif), and among other file types. Data layers
were chosen (see below) to provide insight into the region, its demographics and
environmental characteristics. Many layers were publicly accessible via the Internet and in
ready-to-use formats for online ArcGIS. Other data layers required searching the Internet
outside of the online ArcGIS framework, extracting datasets from websites and literature, and
then creating datasets in appropriate formats on desktop softwares, that were then uploaded
onto the online ArcGIS project. The formats used for this Autism GIS prototype were .csv files
(e.g., Autism prevalence dataset layers) and shapefiles which were all of the other layers
obtained via the public ArcGIS layer search engine.
Once added, layers can be turned off or on so that a user may select which to view and
compare. It can be difficult to see patterns between layers since these layers are stacked one
on top of the other. This can make layers further down the stack hidden by the layers above. A
technique commonly used to see relationships between layers is to adjust the transparencies
and colors of layers, enabling greater possibilities of simultaneous viewing of data layers within
the same map. Additionally, features within a single layer can be displayed by color gradations.
For example, the population density of US states can be displayed so that those states with
higher population densities can be displayed with a darker color than those with lower
densities. Online ArcGIS allows several ways to display data and it is up to the user to decide
which way is an effective way to convey the information being presented.
The user can then perform various spatial tasks such as filtering and overlaying to and
observe and compare trends and patterns within a single layer and between multiple layers.
Filtering the data is based on random or non random relationships the user would like to test.
Filtering allows the user to examine possible relationships between and within data layers.
From this examination, the user can develop hypothesis explaining the observed patterns.
For the Autism GIS prototype the ‘Autism GIS Data Layers’ Table 1 shows the layers that
were developed and integrated:
NAME OF
DATA LAYER
DEFINITION
Autism data
This dataset is key in
from 2008understanding how the trend of
2011 for ages Autism prevalence correlates
3 – 22
with the trends of other data
provided.
Shows Autism statistics for a
range of children and young
adults, ages 3 to 22, and by race
between years 2008 through
2011 in Florida, New Jersey and
New York
Autism data
Shows Autism statistics for a
from 2008range of children, ages 3 to 5,
2011 for ages and by race between years 2008
3–5
through 2011 in Florida, New
Jersey and New York.
Similar to
“AutismData08_11threeTOtwen
tytwo”, this layer displays a
representation for each year of
REASON FOR INCLUDING
SOURCE –
WHO/WHA
T CREATED
IT/YEAR
PUBLISHED
This layer displays a
representation for each year
of Autism prevalence in
Florida, New Jersey and New
York. From this layer we can
see per year the Autism
prevalence by ages 6 through
21, as well as total prevalence
of a range of ages 3 to 22. We
can also see within this age
group the prevalence of
Autism based on different
races, which include:
American Indian/Alaska
Native, Asian Pacific Islander,
Black, Hispanic, White,
Latino/Hispanic, American
Indian, Asian, Native Hawaiian
http://www
.autismmap
s.org/
It was important to represent
this layer for the same reason
it was as important to
represent the
“AutismData08_11threeTOfiv
e”. This dataset explores how
trends of Autism prevalence
correlates with the trends of
other data provided.
http://www
.autismmap
s.org/
Site
currently
archived
Site
currently
archived
Autism prevalence in Florida,
New Jersey and New York. From
this layer we can see per year
the Autism prevalence by ages 3
through 5. The races are the
same categories as the previous
datalayer.
rainfall2008
Shows rainfall accumulation in
2008 in Florida, New Jersey and
New York
Data analysis of rainfall
metrics
http://wate
r.weather.g
ov/precip/
rainfall2009
Shows rainfall accumulation in
2009 in Florida, New Jersey and
New York
Data analysis of rainfall
metrics
http://wate
r.weather.g
ov/precip/
rainfall2010
Shows rainfall accumulation in
2009 in Florida, New Jersey and
New York
Data analysis of rainfall
metrics
http://wate
r.weather.g
ov/precip/
rainfall2011
Shows rainfall accumulation in
2011 in Florida, New Jersey and
New York
Data analysis of rainfall
metrics
http://wate
r.weather.g
ov/precip/
Hospital
locations in
New York
State Hospital_loca
tions_in_Ne
w_York_Stat
e
This data file contains
information on all acute care
facilities licensed by the New
York State Health Department
and covered by NYS Article 28.
This data was integrated in
the Autism GIS prototype to
show what the availability of
hospital care facilities in the
respective state versus the
rate at which Autism
prevalence was trending. For
instance, were the states with
the highest prevalence rate
reported also having the most
hospitals present?
http://servi
ces.arcgis.c
om/jDGuO8
tYggdCCnUJ
/arcgis/rest
/services/H
ospital_loca
tions_in_Ne
w_York_Sta
te/FeatureS
erver/0
Florida_Rural
_Hospitals_a
nd_Districts
29 Florida Rural Hospitals,
addresses, including US
Congressional, Florida House
and Florida Senate District
Numbers
This data was integrated in
the Autism GIS prototype to
show what the availability of
hospital care facilities in the
respective state versus the
rate at which Autism
prevalence was trending. For
instance, were the states with
the highest prevalence rate
http://servi
ces.arcgis.c
om/9Jk4Zl9
KofTtvg3x/a
rcgis/rest/s
ervices/Flor
ida_Rural_H
ospitals_an
d_Districts/
reported also having the most
hospitals present?
FeatureServ
er/0
HSIP New
Jersey
Hospitals HSIP_New_Je
rsey_Hospital
s
Hospitals in New Jersey The
term "hospital" ... means an
institution which-(1) is primarily
engaged in providing, by or
under the supervision of
physicians, to inpatients
This data was integrated in
the Autism GIS prototype to
show what the availability of
hospital care facilities in the
respective state versus the
rate at which Autism
prevalence was trending. For
instance, were the states with
the highest prevalence rate
reported also having the most
hospitals present?
http://servi
ces.arcgis.c
om/inqEhO
w82Jo3lvRf
/arcgis/rest
/services/H
SIP_New_Je
rsey_Hospit
als/Feature
Server/0
USA
Population
Younger than
Age 18
This thematic map identifies
locations of the population
younger than age 18 in the
United States in 2010. The age
classification is based on the age
of the person in complete years.
Integration of this data in the
Autism GIS prototype can
provide insight into the ratio
of the “minor” population
relative to the prevalence of
Autism. This layer is also
important because not only
does it show a representation
of the state but it goes deeper
in analyzing areas within the
state which may have a high,
medium or low volume of a
condensed “minor”
population. This can also
provide information into
understanding if a particular
area with more minors are
relatively closer to hospital
care facilities and what
relationship does the Autism
prevalence show per the total
population of minors present.
http://servi
ces.arcgison
line.com/Ar
cGIS/rest/s
ervices/De
mographics
/USA_Perce
nt_Under_1
8/MapServ
er
USA Average
Household
Size
This thematic map presents the
average household size in the
United States in 2010.
The average household size
for the U.S. in 2010 is 2.59.
The 2010 Average Household
Size is the household
population divided by total
households.
http://serve
r.arcgisonlin
e.com/ArcG
IS/rest/servi
ces/Demogr
aphics/USA
_Average_H
ousehold_Si
ze/MapServ
er
USA Median
Home Value
This thematic map illustrates the The 2010 median home value
is an estimate of home value
median value of houses in the
based on total owner
United States in 2010.
occupied units.
http://serve
r.arcgisonlin
e.com/ArcG
IS/rest/servi
ces/Demogr
aphics/USA
_Median_H
ome_Value
/MapServer
USA
Population
Density
This thematic map illustrates the
population density in the United
States in 2010. Population
density is the number of people
per square mile.
This layer is important in
understanding population
density compared to that of
the “USA Population Younger
than Age 18” layer.
Understanding the differences
between the entire
populations versus the
population specific to children
under 18 can provide for a
good ratio that will help in
understanding the overall role
population plays in Autism
prevalence rates.
http://serve
r.arcgisonlin
e.com/ArcG
IS/rest/servi
ces/Demogr
aphics/USA
_Population
_Density/M
apServer:
This layer provides insight into
trends observed based on the
different diversities versus the
Autism prevalence for those
different diversities. For
instance, is the Autism
prevalence greater in the
Asian community because
that particular region or area
is mainly populated with an
Asian race? This layer helps
understand those questions
and find correlations between
different diversities and
Autism prevalence.
http://serve
r.arcgisonlin
e.com/ArcG
IS/rest/servi
ces/Demogr
aphics/USA
_Diversity_I
ndex/MapS
erver:
USA Diversity This thematic map summarizes
Index
racial and ethnic diversity in the
United States. The Diversity
Index shows the likelihood that
two persons chosen at random
from the same area belong to
different race or ethnic groups.
USA Median
Household
Income
This thematic map illustrates the Median household income for
the U.S. in 2010 was $54,442
U.S. median household income
in 2010.
http://serve
r.arcgisonlin
e.com/ArcG
IS/rest/servi
ces/Demogr
aphics/USA
_Median_H
ousehold_I
ncome/Ma
pServer:
Topographic
This topographic map is
designed to be used as a
basemap and a reference map.
The map has been compiled by
Esri and the ArcGIS user
community from a variety of
best available sources. The map
is intended to support the
ArcGIS Online basemap gallery.
http://servi
ces.arcgison
line.com/Ar
cGIS/rest/s
ervices/Wor
ld_Topo_M
ap/MapSer
ver
This layer is the basic layer
used in the background of the
Autism GIS prototype; it is a
base layer in which all other
layers are integrated on top
of. This layer outlines the
United States, mainly, for the
purpose of the Autism GIS
prototype and is configured to
focus on the three states
being analyzed, Florida, New
Jersey and New York.
Table 1 - Autism GIS Data Layers
Web based map development
To make the GIS tool more accessible and reach a larger audience, a website was
created to enable users to use the GIS tool to view maps, and access research, references and
other information that may be useful. For the website html5 and css programming language
were used along with the use of jquery functions and scripts to control particular actions on the
site. Figure 4 is an image of the website the link to the website is WWW.UosefLabs.COM.
Figure4: Link - WWW.UosefLabs.COM
The Autism GIS prototype with the data layers is placed in the center of the wireframe
(image of the website. In order to make this map available on a website, it had to be
embedded into the site in a particular way. An iFrame was created using HTML which
contained necessary properties of the map.
This code uses the ArcGIS server to retrieve the map developed online. Pirobox functionality
was used to make the experience of viewing all layers as well as images as seamless as possible.
Pirobox is implemented using JQuery and it allows for the layers or images to be presented as a
list of items, clicking on the pirobox allows for a more seamless navigation between the layers
or images when a user selects a particular layer or image for viewing.
Results and discussion
The Autism GIS prototype used in this report examines various layers in a unified GIS
system is called “GIS Epidemiology - Infectious Disease”. The site for the Autism GIS prototype
along with documentation can be accessed viawww.uoseflabs.com. This Autism GIS prototype
and associated research is available via http://www.arcgis.com/. The link to this map
ishttp://bit.ly/1gNMwxh. This map is also embedded into the web site for this Autism GIS
prototype.
This same map can also be viewed larger in a new tab as well as downloaded to your
desktop for offline access. This site also allows the user to view layers individually, as seen in
the bottom pane of the website image.
A PowerPoint presentation was created to show how to use the Autism GIS prototype to
explore relationships within and among datasets. To view ‘Layer Analysis’ select ‘Layer Analysis’
menu item located on the left of project site:
http://www.uoseflabs.com
Autism GIS Prototype results
This project demonstrates an application of web-based GIS tools to examine patterns of
the prevalence of Autism. Data analysis and visualization was achieved via online ArcGIS while a
project website was the venue to share the data and other relevant information. Sharing data
helps spread ideas and can lead to the discovery of new patterns and data interpretation
through user iterations. The Autism GIS prototype can extract data and construct tables and
queries in a way that a MIS system does, although a GIS framework allows the examination of
more relationships of many data layer combinations, which can then lead to further
investigations.
This Autism GIS prototype demonstrates how data can be uploaded and shared making
it accessible via a website. Also, data and product outputs can be downloaded and used offline
with the ArcGIS desktop or other GIS desktop software and shared. This exchange of data
provides for vital information sharing which can be helpful in infectious disease prevention and
emergency planning.
Many features of this Autism GIS prototype were based on those displayed in the
“Infection Watch Live’ project. There were features that this Autism GIS prototype was able to
implement and other features that hopefully can be implemented in future iterations. Similarly
to ‘Infection Watch Live’ this Autism GIS prototype used ESRIs ArcGIS mapping tools and
tracked infectious disease of the prevalence of Autism. From a user point of view this Autism
GIS prototype has one general interface for all users to use unlike the ‘Infection Watch Live’
which allows different types of users to select their viewing ability. This Autism GIS prototype
was also able to implement a web-based tool to allow for all internet capable users to access
the site and view the information. There are many more features in the ‘Infection Watch Live’
project that I would like to include in the future for the Autism project, such as the ability to
view multiple maps side by side; I feel that this creates a better user-experience for the user.
Conclusion and future work
A disease outbreak has many underlying factors which contribute to its spread. Factors
include pollution, global warming, extreme weather events, rapid urbanization and growing
populations in regions.22 A GIS implementation can aide in educating the public awareness of a
disease. It can be used as a method of surveillance and data can be distributed around the
world making planning policies, emergency evacuations, and vaccine distribution easier and
more manageable. Furthermore, GIS is an extremely useful and successful implantation when
applied to a variety of areas of study as seen in the study of Autism. Its relatively simplistic
features allows for a wide range of communication across political divides and end users.
Although not implemented for this project’s specific GIS Autism tool, ‘Google trends’ is
definitely something worth looking into implementing for future iterations. For instance, if
there is an outbreak or disease occurrence, Google trends could provide useful querying tools
to better understand the disease event. Visualization tools enable the users to see where
infections are occurring and spreading, and what types of factors may be contributing to the
disease event.
This Autism GIS prototype provides a template for future work. The next steps would be
to 1) develop an application that can allow users to pick and choose the datasets that they
would like to analyze or cross reference, 2) add the ability to add their own data sets as
variables in which they would like to examine, and 3) develop additional tools to generate
queries and reports; to enable user to view/print charts and graphs based on their queries, and
4) develop smart algorithms to automatically identify known trends and patterns.
22
http://www.nrdc.org/health/climate/disease.asp
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