Rhode Island Health Geography - page 1

Health Geography Primer for Rhode Island Family Medicine

July 26, 2016, Dr. Joseph Holler

Contact email: joseph@josephholler.com Website: www.josephholler.com This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Contents Purpose ---------------------------------------------------------------------------------------------------------------------------------- 1

Introduction to Health Geography ------------------------------------------------------------------------------------------------ 2

Applying a Geographic Perspective: Childhood lead poisoning ------------------------------------------------------------ 5

Geographic literacy: reading thematic maps critically ----------------------------------------------------------------------- 9

Ethics and confidentiality in mapping ------------------------------------------------------------------------------------------ 22

Analyzing density and accessibility from points ----------------------------------------------------------------------------- 24

Mapping indices composed of numerous variables ------------------------------------------------------------------------ 26

Appendix A: Geographic data sources ----------------------------------------------------------------------------------------- 27

Appendix B: Geographic Information Systems Software ------------------------------------------------------------------ 28

Purpose This primer in health geography, cartography, and geographic information systems (GIS) is designed with the intended audience of Family Medicine physicians in the state of Rhode Island. The purpose of this primer is to provide family doctors with an introduction to thinking about health problems from a geographic perspective. First, we will work though an example of geographic analysis with lead exposure rates in children, housing age, urban development, race and poverty. Second, we will develop critical literacy in reading maps and atlases of health data, especially the sensitivity of cartographic products to decisions of cartographic scale, normalization, and classification. Finally, we will explore a variety of data sources and more advanced analysis and visualization techniques. By the end of the primer, family doctors will have improved geographic literacy to read health geography research, improved knowledge of Rhode Island health geography, and awareness of the contributions that geographic perspectives may contribute to family medicine.

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Introduction to Health Geography

Why Health Geography?

• Place the individual patient and family in the context of their community. • Recognize the structural social and environmental factors influencing and constraining

patients' health. • Identify spatial patterns of health inequalities, investigate causal processes creating

those patterns, communicate results in broadly legible and authoritative maps, and model future health scenarios.

• Where we live and work impacts our health, and these places are directly related to environmental and social inequalities. Important factors include: o Environmental Risks o Environmental Services o Social Risks o Social Services o Spatial Analysis and Epidemiology

Environmental Risks

• air pollution, e.g. ground-level ozone • water pollution, including contaminated groundwater, fisheries, beaches • climate change and urbanization creates increased risks of urban heat islands, and

storm water flooding basements and contaminating surface water • e.g. check maps and resources on air quality, water quality at public swimming areas,

and heat islands: o Air pollution: http://www.dem.ri.gov/programs/air/ and https://airnow.gov o Water quality and swimming: http://health.ri.gov/data/beaches ,

https://beaches.health.ri.gov/swim , and http://www.ri.gov/press/view/28104 o Heat Islands: http://www.nasa.gov/topics/earth/features/heat-island-sprawl.html and

https://environment.yale.edu/news/article/urban-heat-not-a-myth-and-worst-where-its-wet

Environmental Services and Amenities

• Tree canopies regulate temperature extremes, increase air quality, increase building

heating/cooling efficiency • Safe mobility for pedestrians and cyclists • Green spaces enable outdoor activity, reduce social stress, increase air and water

quality, and reduce flood risk • See programs, videos, and reports on green infrastructure in providence, e.g.:

o Green Infrastructure Coalition: http://www.greeninfrastructureri.org o Providence's Urban Forest Structure, Effects and Values:

https://www.providenceri.com/efile/5424 o Woonasquatucket River Watershed Council: http://www.wrwc.org

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Social Risks • High ratios of dependency of children & elderly on working-age adults • Liquor outlets and alcoholism • Social reproduction of poverty and drug addiction • Community stress indicated by high rates of crime, incarceration and gang activity • Availability of guns and other weapons • See, e.g.

o Providence Crime Map: http://www.providenceri.com/police/crime-map o Providence Gun Violence: http://www.providencejournal.com/breaking-

news/content/20130630-providence-gun-violence-rises-with-the-heat.ece o Million Dollar Blocks: http://c4sr.columbia.edu/projects/million-dollar-blocks

Social Services and Amenities

• Quality of schools and libraries • Social institutions of mutual assistance and community-based organizations, especially

within enclaves of recent immigrants or refugees • Health care accessibility • Child care accessibility • Public transportation • Accessibility of healthy foods and food assistance: the inverse of a "food desert" • Employment opportunities within reasonable commuting time • See, e.g. the Zuni Youth Enrichment Project community assets:

http://www.josephholler.com/zunimap/

Analyze neighborhood accessibility of employment, health, and conveniences with Walk Score

• Go to Walk Score at https://www.walkscore.com • Think of 4 neighborhoods: Pawtucket, Central Falls, Mt. Hope and

Blackstone o Which neighborhood has the best public transportation for working in downtown

Providence? For going to Memorial Hospital? o Which neighborhood is the most walkable, i.e. has the most amenities within

easy walking distance?

• Answer the first question by comparing the Transit Time maps for each neighborhood, and the second question by comparing the Walk Scores.

• The patented Walk Score method assigns a maximum score to amenities within 0.25 miles (5 minute walk), declining to a score of 0 for any amenity beyond 1.5 miles (30 minute walk) and aggregates the scores for several common amenities into a single score, which is then averaged for locations in a neighborhood. The distance is based on walking routes (network distance), rather than straight lines (Euclidean distance), and is therefore dependent on the neighborhood's network of roads and paths as well as the spatial distribution of services.

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Spatial Analysis and Epidemiology

Dr. John Snow’s map of 1854 Cholera outbreaks in London

• Dr. John Snow's cholera maps of London: thematic map of cases of cholera and water sources convinced authorities that cholera was a water-borne disease and to shut off the contaminated Broad Street water pump at the geographic center of the epidemic.

• The 2014 Ebola outbreak in West Africa demonstrates the need for health care to detect and contain disease at first point of care, in villages adjacent to dense forest

• See https://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak • Ebola in West Africa: http://www.nytimes.com/2014/12/30/health/how-ebola-roared-

back.html (watch the Path of the Outbreak video)

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Applying a Geographic Perspective: Childhood lead poisoning

Scenario: new family on patient panel with children with high levels of exposure to lead

• A new family comes into the family care clinic. Screening reveals asthma, obesity,

high levels of lead in the children. What are you thinking about as their primary care provider?

• In the primary care clinic, your knowledge of patients' communities and environments is largely based on the histories you take and the examinations and tests you administer, accumulating longitudinally with repeated visits.

• Let's take a step back from the micro-scale you're accustomed to working in and study why these problems are so persistent for some communities in Rhode Island, using the example lead poisoning in children.

Lead Poisoning • KidsCount has tabulated lead poisoning data from Rhode Island Department of Health

surveys of Kindergarten-eligible children by township. • Lead poisoning is considered a blood lead level ≥ 5 mcg/dL.

• Q: Which townships have the highest lead poisoning rates?

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Lead Poisoning and Housing Age

• A primary source of lead poisoning is old housing stock with lead-based paint, so let's

map the percentage of housing units built before 1980. Federal law banned the use of lead paint in 1978, but the American Community Survey tabulates housing units by decade, so 1980 is our best approximation of new housing free of lead paint.

• Q: Which towns have the oldest housing stock? • Q: Where are lead poisoning rates and housing age strongly correlated? • Q: Are there exceptions where housing stock is old but lead poisoning rates are low? • Overall, trends of lead poisoning are in decline in Rhode Island, but in 2014, only one

in five buildings were in compliance with Rhode Island lead paint laws in the townships of Providence, Pawtucket, Woonsocket and Central Falls . o http://health.ri.gov/data/childhoodleadpoisoning/ o http://www.providencejournal.com/article/20140707/NEWS/307079986

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Housing Age, Class and Race

• So lead poisoning and old housing are correlated, but not everywhere. Why? Are

there correlations between old housing stock, class, race, and lead poisoning? Let's investigate with three additional questions and maps: o Q: Do children in poverty tend to live in areas with old housing and high levels of

lead poisoning? o Q: Do minority populations tend to live in areas with old housing, high levels of

lead poisoning, and high poverty rates?

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Explaining the processes behind the pattern

• Urban geography theory on urban structure and gentrification helps. The theories

postulate that: • Wealthy classes continuously escape the grime and congestion of city centers by

moving outward and building new luxurious housing at the edges of the city. • As the housing stock and neighborhoods vacated by the wealthy gradually ages and

loses value, it is filled in by middle class, then working class, and finally by new immigrants.

• From the perspective of new migrant populations, they tend to settle in the cheapest, oldest sections near the city center and gradually move outward into wealthier neighborhoods in a process of residential succession. o See an application of Park and Burgess's theory (in The City, University of Chicago Press,

1925)of concentric zones applied to modern Chicago housing ages here: http://www.geographicsociety.org/urban-dendrochronology-concentric-zones-of-building-age-in-chicago

• An area will gentrify when its potential rent value in a new functional use (e.g. conversion from low-income apartments and tenements into high-value condos or retail) exceeds its current rent value and redevelopment costs. Within existing federal and state law, the lead paint risk will be mitigated during this reinvestment and renovation. For example, Historic properties in the wealthy East Side neighborhood are marketed by real estate agencies as having "modernized" interiors. Wealthy enclaves near city centers also tend to be populated with young professionals and few children. o See Neil Smith (1979) Toward a Theory of Gentrification A Back to the City Movement by

Capital, not People. Journal of the American Planning Association, 45:4, 538-548

• Therefore, we should expect that for any urban environment in the United States, low-income populations, especially new migrants to the city, will tend to live in areas of old housing stock. As renters and low-income homeowners in old housing, they will be at a high risk for lead exposure. Elsewhere, renovation and gentrification will remediate the lead paint problem as capital moves back into old parts of the city. Through structural patterns of urban development and redevelopment (gentrification), housing and migration, lead paint exposure is tied to class and race.

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Geographic literacy: reading thematic maps critically

• The line of questioning in the lead poisoning analysis was explicitly spatial. We first

identified patterns of single variables (lead poisoning, housing age, childhood poverty, and race), and then identified correlations and exceptions between variables based on spatial relationships. Finally, we considered social processes to explain the spatial patterns and correlations.

• Now, we need to learn what GIS technology can offer, explore some breadth of the geographic data available for health analysis, and gain some literacy in critically visualizing and interpreting that geographic data.

Geographic Information Systems and Health

• Spatial data related to health is collected, processed, and transformed into map

through geographic information systems, or GIS. • GIS is a system of software, data, and hardware to manage spatial information. It is

capable of acquiring, storing, analyzing and visualizing spatial information in order to identify patterns and relationships.

• GIS provides the ability to analyze multiple layers of data simultaneously, e.g. o race, poverty, housing age, and lead poisoning, or o arsenic groundwater contamination and households using well water.

• Read more: http://www.cdc.gov/gis/what-is-gis.htm and http://www.esri.com/what-is-gis

What makes data geographic?

• A surprisingly large amount of data has a geographic component. If you want to map

health data, look for these types of location-based information associated with data: • Latitude and longitude coordinates (e.g. geographic tags added to tweets and

photographs in smartphones, apps that track outdoor activity, locations of point-source pollution from the EPA).

• Addresses (e.g. City of Providence database of recorded crimes, addresses of service providers or patients).

• Zip codes or names of counties, municipalities, school districts, etc. can be joined to their respective geographic data.

• Census block groups IDs or tract IDs can be joined to the Census geographic data

Digital Geographic Data Format

• Geographic data is represented as layers of points, lines, polygons, or grids. • Each layer should represent one theme, e.g. Rhode Island GIS provides a point layer

for recreation facilities, a line layer for roads, and a polygon layer for school districts. • An attribute table of data is associated with each layer. Think of the attribute table as

a spreadsheet in which every row corresponds to one point feature on the map. o Interactive web maps often show you a geographic feature's attribute data as

you hover the mouse over the feature. E.g. on the Walk Score map, hovering over the point for a house for sale displays the attributes for the house.

o Any attribute in the attribute table can be used to select subsets of a layer or to control how the layer is represented in symbols.

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Primary sources of social data: The U.S. Census and American Survey

• The U.S. Census is a decadal complete survey of (almost) the entire population • The American Community Survey (ACS) is an annual product based on surveys of a

sample of the population, other ancillary data, and methods for estimation. Its indicators come with a confidence interval of possible values based on the sampling and/or estimation methods.

• Both decadal census data and ACS indicators can be visualized and downloaded with the American FactFinder http://factfinder.census.gov/ or the subscription-based SocialExplorer http://www.socialexplorer.com/

• The Census is less frequent, but more reliable. The ACS is more frequent and offers more variables, but some indicators are incomplete or have a high degree of uncertainty.

Hierarchies of Enumeration Areas

• Both the Census and ACS are tabulated by

enumeration areas arranged in a hierarchy (see https://www.census.gov/geo/reference/hierarchy.html and diagram below) o The enumeration areas are provided by the

Census as polygon geographic data. o Census and ACS data are separately provided

in tables that can be joined to the geographic data and mapped.

• At the lowest level, Rhode Island contained 17,644 census blocks with people in the 2010 census (and thousands more unpopulated blocks). These are aggregated into 812 Block Groups, then 241 Tracts, then 5 Counties (shown at right), and finally, the State of Rhode Island

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The Census Hierarchy, with counts for Rhode Island's geography at left

Census Geography: blocks

• Census blocks are the smallest unit of census

enumeration areas. • The Census only provides basic demographic

data for blocks because the areas are so small that confidentiality is impossible. For example, there is only one household in the yellow-highlighted census block (right) on the southern end of Prudence Island!

• Much more data is available for the next levels in the hierarchy: census Block Groups and Tracts. It's most straightforward to download state-wide data at the tract level.

Choosing the right enumeration areas and scale

• Your choice of enumeration area and scale has profound impacts on your analysis.

o If enumeration areas are too small, you might compromise patients' confidentiality, and statistics may be derived from very small sample populations.

o If enumeration areas are too large, they obscure significant variations and inequalities.

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Illustrating the importance of enumeration areas and scale with child dependency

• To illustrate, I have made a series of maps of the same variable, dependency rate of

children (age under 18) to 100 working-age adults (age 18 to 65) from the 2010 census. All the maps are classified with the same quantile method (more on classification next)

• While reading the maps, think about these questions: o Q: How are the classes' ranges changing, and why? o Q: Which areas of the map are most egregiously misrepresented by aggregating

to a higher level of enumeration areas? o Q: Which level of census enumeration areas do you think is most useful for

identifying communities with high child to adult dependency ratios? o Q: Based on your knowledge of Rhode Island, can you explain the patterns in this

data? Which types of communities have the highest and lowest child dependency ratios?

17,644 Blocks with population

812 Block Groups with population

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241 Tracts with population

39 Townships / Municipalities (sub-county divisions)

5 Counties

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Other Common Enumeration Areas

• Aside from the nested census hierarchy, it is common to find data collected by

Municipality (Sub-County Divisions), School District, or Zip Code (see maps below) • Personal data can be easily aggregated into zip codes while preserving confidentiality

by tabulating records with the zip code and omitting the remainder of the address. o How many times have you been asked for your ZIP when making a purchase?

Companies do this to track the geographies of their customer base.

• Using school districts, municipalities, or counties as enumeration areas has the added benefit of linking data and results to local governments and policymakers.

• U.S. Congressional Districts are also commonly used, but Rhode Island only has two districts.

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Using Health Care Service Areas to Define Enumeration Areas

• The Dartmouth Health Atlas presents data according to service areas defined by

Medicare and Medicaid patient behavior. o This method has the benefit of analyzing the populations using a particular set of

health service providers, and the health outcomes for those populations. o The Dartmouth Health Atlas provides data focusing on services and outcomes.

But their service areas are based on census enumeration areas, so any census or ACS data can easily be aggregated into comparable areas.

• Primary Care Service Areas (PCSAs) are created from census tracts wherein the majority of patients receive care from the same area.

• Hospital Service Areas (HSAs) are created from zip codes wherein the majority of patients are admitted to the same hospital area (usually a single hospital).

• Hospital Referral Regions (HRRs) are created from Hospital Service Areas where the majority of patients are referred for neurosurgery or major cardiovascular surgery. A single HRR encompasses all of Rhode Island, plus areas of Connecticut and Massachusetts represented on the Hospital Service Areas map.

• The Dartmouth Health Atlas notes that most HSAs have only one hospital. With recent trends of

consolidation of health care systems and closures of small hospitals, many rural and community HSAs no longer have their own hospitals. For purposes of data continuity, the atlas will continue to represent these orphaned HSAs.

• See http://www.dartmouthatlas.org/

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Choosing the right normalization

• Most maps of statistical data are choropleth maps: those using a gradient of colors to

represent classes of values. It is visually deceiving to map totals on choropleth maps unless all the enumeration areas have equal geographic sizes. Therefore, you should always use percentages, rates, or densities for choropleth maps, and be cautious when interpreting choropleth maps of totals. The following maps illustrate totals, rates, and densities with the theme of the population of children.

Totals without Normalization

• This map of the total number of children per census track is very difficult to interpret

because tracts vary in geographic size and total population.

Rates or Percentages Normalized by other variables

• A better option is the child dependency ratio, or the total of children normalized by

the total of working age adults, calculated as follows: (children under 18) / (adults 18 to 65) * 100

• Other possible rates or percentages include children per household, percentage of

households with children, or percentage change of child population.

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Densities normalized by area

• This map of children per square mile is a density in which the variable is normalized

by land area: (children under 18) / (area in square miles)

• Density maps of social variables are not very interesting unless the variable does not strongly correlate with total population density.

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Choosing the right classifica-tion method

• Interactive maps online now commonly offer a choice of two or more classification

methods, and this choice can also profoundly alter your results. The next series of maps illustrates the four most common automated classification methods: quantile, equal interval, standard deviation, and natural breaks.

• Q: As you look compare the following classifications of child dependency rates, consider: o Which classification method you would you choose if you wanted to

demonstrate a need for more state assistance for child services in Central Falls? o Why does the method have the intended visual impact?

Quantile (Equal Count) Method

• Quantile: This method ranks all the records and divides them into classes with the

same number of records in each class. It is also called an equal count classification. • 4 quantile classes are quartiles, or 5 classes are quintiles. • You can think of the method as making equally-sized percentiles. In the example

below, out of 241 total census tracts, 49 tracts fall in the first class, and 48 tracts fall in each of the remaining classes.

• When you interpret the five color classes on this map, you are conceptualizing how the census tracts are ranked in relation to each other, as opposed to their real values.

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Equal Interval Method

• Equal Interval: This method divides the data in to equal intervals, such that the

range of values in each class is identical. In the example below, each class spans just under 12.2 children per 100 working-age adults.

• When you interpret the five color classes on this map, you are conceptualizing intervals of values of each census tract.

• The classification is sensitive to outliers, and depending on the data distribution, it is possible that some classes will remain empty.

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Standard Deviation Method

• Standard Deviation: This method also makes classes with equal intervals, but

the intervals are calculated based on the standard deviation (or variability) of the dataset. The classification starts with the mean (or average) of the data. Classes are measured in their distance from the average in terms of standard deviations.

• When you interpret these classes, you are conceptualizing how far each tract falls above or below average.

• This classification often uses a divergent color scheme, e.g. starting with a neutral grey or white at the mean and diverging to red on the negative end and green on the positive end.

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Natural Breaks (Jenks) Method

• Natural Breaks (also known as Jenks): This method uses an algorithm to

optimally allocate all the records into a set number of classes, maximizing difference between classes and minimizing difference within each class.

• If there are distinct clusters of data values with breaks in between (e.g. the gap from 14 to 16), this method will divide classes at those 'natural' breaks. Just a few outliers will often encompass their own classes. KidsCount exclusively uses this method.

• The range in each class may vary significantly, but the map reader should be able to assume that each class is relatively homogeneous.

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Ethics and confidentiality in mapping

Ethics and Mapping Small Numbers

• The CDC atlas of sexually-transmitted diseases provides this caution for its maps:

"The query tool contains data at the national, state, and county level. The disease selected will determine the years and demographic variables available. Data at the state or county level data may contain suppressed cases and rates to protect against a situation in which a person could potentially be identified (e.g., a small number of cases)."

• The CDC suppressed rates of HIV diagnoses in Washington, Newport, and Bristol counties for 2014. The database indicates that counties with less than 5 cases are suppressed, constituting 2493 counties, leaving only 731 counties with recorded data. o See http://gis.cdc.gov/grasp/nchhstpatlas

• The Dartmouth Health Atlas suppresses data for records with less than 12 cases. • The National Center for Health Statistics requires users to digitally sign an agreement

before accessing data on infant deaths. Users must agree to: o Suppress of records with 9 or fewer cases o Make no attempt to identify individuals or establishments in the data o Make no disclosure of personally-identifiable information in the data, and report

the problem to a Confidentiality Officer. o More on Ethics and geography:

http://www.aag.org/cs/about_aag/governance/statement_of_professional_ethics

Anecdote on Ethics

• In Fresh Fruit, Broken Bodies: Migrant farmworkers in the United States (2013), Seth

Holmes, PhD, MD, describes the health clinic in San Miguel, Oaxaca, Mexico. In the waiting area of the Centro [de Salud] hang three large posters for everyone to see. One is a map of the small town which each family's house hand-drawn and marked for the presence of such sicknesses as tuberculosis, diabetes, miscarriage, and malnutrition. One poster is titled, '10 Rights of Patients', among them, 'receive dignified and respectful attention; Decide whether or not to give your consent for risky procedures; Be treated with confidentiality; Receive medical attention in the case of urgency.' Of note, the first poster's public announcement of sicknesses directly counteracts the claim of the second poster to the right of 'confidentiality'."

• The breach of confidentiality here is obvious, where a highly detailed large-scale map of a small village area enables any local reader to identify individual households and their ailments. However, be aware that with GIS, it is trivial for analysts to digitize and geocode locations on a printed map, so extra care must be taken to suppress sensitive information.

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Expand time-frame of sampling

• One strategy to mitigate the problem of small numbers and ethics is to expand

sampling in time rather than in space. • For example, Rhode island firearms related deaths for 2014 only includes data for

Providence County (map at left below), but expanding the time-frame to 2008-2014 allows Kent and Washington counties to be mapped as well (map at right below). o The maps were generated by the Health Indicators Warehouse

http://www.HealthIndicators.gov

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Analyzing density and accessibility from points

• Much health-related geographic data is created in the form of points. There are

three common ways to analyze and visualize these data: 1) create a density service, 2) create a buffer or proximity analysis, or 3) summarize by enumeration area.

Density Surfaces or Heat maps

• A density surface (commonly known as a heat map) simultaneously visualizes

proximity and frequency or intensity. For example, crime maps are concerned about both proximity to violent crimes and the frequency of crime.

• In GIS, this is done with a density, kernel density, or heat map function. • The choice of a radius (i.e. the distance over which a point influences the surface) has

significant influence over the result. A long radius will produce a smooth surface, averaging out peaks and gaps. It would make crime seem more universally widespread. A short radius produces a more variable, patchy surface. It would make crime seem very intense in some neighborhoods and absent from others.

Buffer or Proximity Analysis

• If proximity within a specified distance

is more important than intensity, create a buffer of straight-line distance or network-based travel time around them, e.g. to indicate proximity to any grocery store. In GIS, this is done with a buffer, proximity, distance, or service area function.

• For example, the analyses of Food Deserts are only concerned with the presence of any grocery story with 4 or more employees within a specified distance, not with how many grocery stores are nearby.

• The interactive public transportation map on Walk Score is an example of a service area calculated using networks.

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Summarize by Enumeration Area

• You can also count or otherwise summarize attributes of points within polygon

enumeration areas. For example, if you are interested in studying a correlation between rates of childhood obesity and access to outdoor recreation facilities, you might want to count the number of facilities in each school district.

• In GIS, this is done with a spatial join or a points in polygon tool. The result would be displayed in a choropleth map, as shown below.

• For comparison, I have also provided a 5-minute walk (1/4 mile) buffer analysis of the same data.

o The data source for these maps is RIGIS: http://www.rigis.org/

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Mapping indices composed of numerous variables

Combining Multiple Layers into an Index

• In its simplest form, an index or overlay analysis results in a map where all of the

component criteria are met. One example is the USDA Food Access Research Atlas, which combines three factors: o 1. Urban or rural: urban areas have much lower threshold for distance to

grocery stories than rural areas, with the expectation that people drive in rural areas

o 2. Poverty o 3. Distance to grocery store with at least 4 employees o See http://www.ers.usda.gov/data-products/food-access-research-atlas o Q: Do you think this atlas provides a useful guide to understand access to

healthy foods in the U.S.? What does it not explain?

• Index where each component criteria is scored and then combined in either a simple sum or average or a weighted average. One example is the CDC Social Vulnerability Index, indicating the likelihood of neighborhoods to sustain negative health outcomes in the event of a natural hazard. o Ranks census tracts on 14 social factors, including poverty, lack of vehicle access,

crowded housing, etc. o Aggregates factors into four themes: Socioeconomic Status, Household

Composition/Disability, Race/Ethnicity/Language, and Housing/Transportation o See and http://svi.cdc.gov/ its interactive map http://svi.cdc.gov/map.aspx and fact

sheet: http://svi.cdc.gov/Documents/FactSheet/SVIFactSheet.pdf

• Keep in mind: if an index or its cartographic representation is based on ranks or quintiles, then there will always be an equal number of high and low ranking areas, therefore you should examine the unranked data for comparison between regions or over time.

On your Own • This introduction has aimed to introduce you to the fundamentals of GIS and

cartography for health applications. The primer and appendices include several interactive web applications on-line to provide a sense of the breadth of data available. Practically, these tools are sufficient to allow you to search for data and quickly visualize and download it.

• Think of your patient populations and scholarly projects, and consider whether researching spatial patterns and processes can help you better understand and represent problems in your practice and research.

• If you find data useful for your research or publication, call on a geographer to assist you in the final analysis and cartographic design.

• In your future positions, support efforts to collect and analyze spatial health data.

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Appendix A: Geographic data sources • Rhode Island Geographic Information System: http://www.rigis.org/ • City of Providence Open Data Portal: https://data.providenceri.gov/

o 2010 Census Data: http://www.rigis.org/data/censusSF1_2010 o Boundary Data (school districts, congressional districts, zip codes, etc.) http://www.rigis.org/data/bnd o And much more...

• Census Data Mapper o http://datamapper.geo.census.gov/map.html o Allows quantile, equal interval, and manual classification o Limited set of variables (population, age & sex, race, family/nonfamily and renting/owning), only uses

counties

• American FactFinder -- http://factfinder.census.gov/ o download tables of data for enumeration areas as low as blockgroups o Go to Guided Search, then jump to 3: Geographies. Choose block groups, tracts, etc. and the state of

Rhode Island. Select Topics to narrow the scope of final search results. o Click "create a map" to make a map, and then choose which variable to map by clicking on any of the

table cells in the row you want to map. o Change transparency & colors, choose from natural breaks, quantile, equal interval, or manual

classification. o Tool just maps the variable you chose from the table, so it's best to choose a normalized variable if

available, and interpret results with caution otherwise. For best results, download the data and make your own map in GIS.

• Kids Count Datacenter: http://datacenter.kidscount.org/ o Most data is provided by State, for geographic data within RI, the only useful data is by school district or

county subdivision (In RI, these are municipalities / townships). o Web maps classify with natural breaks. o Select the state of RI o In the LOCATIONS panel, select either By School District -or- By County Subdivision o Ability to print, save map images, and download raw tabular data

• Dartmouth Atlas of Health Care http://www.dartmouthatlas.org/ o Data based on Medicare and Medicaid patients, focusing on health care provided o Data by Region: Look up data/statistics by region interactively o Ability to print, save map images, and download raw tabular data o Atlas of Children's Health Care in Northern New England:

http://www.dartmouthatlas.org/downloads/atlases/NNE_Pediatric_Atlas_121113.pdf

• Environmental Protection Agency: https://www.epa.gov/geospatial/epa-geospatial-applications o Includes web tools for air quality, watersheds, beach closures, toxic releases, environmental justice,

and environmental impact assessments. o Data can also be downloaded at https://www.epa.gov/geospatial/epa-geospatial-data

(but this section of the website seems to be in redesign currently)

• Google Earth Pro o Download the program from http://www.google.com/earth/download/gep/agree.html o The license code is GEPFREE

Rhode Island Health Geography - page 28

o Google Earth Pro is a limited GIS program, but also provides plenty of access to data. For example, search Pharmacies or Groceries, save the results to My Places, and export the results as a KML file for use in GIS.

• USDA food deserts atlas: http://www.ers.usda.gov/data-products/food-access-research-atlas/

• CDC Interactive Maps: http://www.cdc.gov/gis/interactive-applications.htm o Social Vulnerability Index (census tracts): index of vulnerability to disasters, by census tract, based on

social indicators o Heart Disease and Stroke (counties) o Cancer atlas (states): cancer types, rates & outcomes, segment by gender & ethnicity. o SchoolVaxView (states): vaccination rates o FluVaxView (states): flu vaccination rates o Interactive National Environmental Public Health Tracking Network http://ephtracking.cdc.gov/

o Explore Tracking data allows you to run queries and filter by “children only” o After testing several indicators, they all seem to be at state or county level o Info by Location generates infographics for a specific county or zip code

o WONDER databases (counties or states): mortality, STDs, cancer, climate, etc. o Disability and Health Data System (states): disability estimates, demographics, health risks/behaviors,

prevention & screenings, barriers & costs of care, general and chronic health conditions, mental/emotional health, expenditures

o NCHHSTP Atlas one-stop-shop for STD maps! (states or counties, depending on the disease) http://gis.cdc.gov/grasp/nchhstpatlas

o Diabetes Surveillance System (states and counties) http://www.cdc.gov/diabetes/data/ County data on diagnoses, rates, obesity, leisure-time activity

• Health Indicators Warehouse (state, county, and hospital referral regions): http://www.HealthIndicators.Gov 242 indicators at county level, find by filtering by geography o Hospital referral regions seem like the Dartmouth Health Atlas regions, in which one region

encompasses RI plus SE CT and some parts of MA

• Walkscore: https://www.walkscore.com/ • List of more data sources: http://www.ncemch.org/guides/datadbs.php

Appendix B: Geographic Information Systems Software • QGIS, a free open source desktop GIS software package: http://www.qgis.org/ • Google Earth, a free virtual earth GIS from Google. The Pro version is now free with the GEPFREE license

key, available here: http://www.google.com/earth/download/gep/agree.html • A GPS-enabled mobile device with the free PDF Maps App: http://www.avenza.com/pdf-maps