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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 References 1. Gregory E. Glass, Joan L. Aron, J. Hugh Ellis and Steven S. Yoon: “Applications of GIS Technology to Disease Control”. Feb 1993 2. “The Application of GIS for Infectious Disease” by Wen-Yuan Ku and Jing-Ming Wu. 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