ArcGIS Insights

Epidemiology and ArcGIS Insights - Part 1

I’ve spent most of my professional career working in spatial analysis and epidemiology.  These were terms that were often met with blank stares when I was asked what I did. But now, after years of having to explain what they mean, and furthermore, how GIS is related, during the COVID-19 pandemic previously specialist terms like ‘epidemic curve’ have entered the everyday language.  It therefore seems a perfect time for a quick blog on this topic.

Epidemiology sits at an intersection of a number of different disciplines and uses knowledge and methods from, for example, the fields of health, medicine and, statistics.  There are numerous disciplines even within the broad framework of epidemiology that focus on infectious disease, genetics, chronic disease, and environmental and spatial epidemiology.  While I could passionately write about environmental and spatial epidemiology in particular, I have tried to keep this blog a little more generic but thought I should declare my (spatial) bias upfront.  For consistency, during this overview I’ll demonstrate epidemiology using examples of COVID-19 from April 2020. I’ll also demonstrate how ArcGIS Insights provides a powerful, yet accessible solution for some of the analytical needs of the epidemiologist, how it can be used in unison with other epidemiological approaches widely used, and how it can help convey information to the general public and decision makers.

I have identified ten key topics that I will briefly explore, with examples.  These will be split between two blogs, just to keep them to coffee break length! In total, the two blogs identify ten major areas of epidemiological study and the scope of GIS to provide an analytical framework. In Part 1 I’ll outline the first five areas. In Part 2 I’ll round it up with a further five areas to ten.

Characteristics of health data

Even the simplest health event data will be collected, analyzed and reported in very different ways.  Total numbers of cases, and rate of health events are often used interchangeably, yet each convey very different information.

The total number of health events can be valuable for capacity planning and funding.  In times of health response, the number of health events such as death, birth and hospitalization are valuable to quantify the extent of any prevention measures required, or indeed, healthcare that may be needed.

In most other situations, the number of health events can only be understood with reference to the size of the population from which it is derived.  In epidemiology, a rate is the frequency of event occurrence in a defined population over a specified period of time.  Rates are, therefore, useful for comparing health events in different populations.

Mapping totals and rates also requires different techniques, most commonly using proportional symbols and choropleths respectively.  The projection used to display your map should also be a consideration, particularly with rates, when values are shown by area, and particularly with larger areas (i.e. smaller scales).

The number of confirmed cases of Covid-19, by county across the USA [April 16th]. In ArcGIS Insights, the base map can be changed, and your data is dynamically re-projected.
Using the same data as shown in the map above but normalizing by population total to create rates of confirmed cases, shows very different information and, therefore patterns. In ArcGIS Insights, the default map type, which is defined by the data type, can be set with a single click (for example, graduated symbols for counts and choropleth for rates/ratios).

Health data distributions

Prior to any modeling, data needs to be explored and well understood.  Many approaches require a number of assumptions to be met.  Health events are usually characterized by infrequent, sometimes recurring, events for example hospitalizations, that are non-normally distributed, highly positively skewed with a Poisson distribution (Poisson distribution is used to describe the distribution of rare events in a large population).  In most health analysis, there are often strong interrelationships, and data collinearity is an important consideration for some methods.

To understand data distributions, histograms and boxplots, together with statistics such as skewness and kurtosis, can be used.  Data correlations between variables can be evaluated using scatterplots and scatterplot matrices, while regression analysis can be used to estimate the strength and direction of the relationship between dependent and independent variables.  Spatial data distributions should also be analyzed to check for data gaps, patterns or skew.

The histogram of deaths for counties in New Jersey (April 16, 2020) shows the values sub-divided into 6 intervals. The first bar shows total deaths between 3 and 114 and the frequency reveals that 12 counties fall in this interval. The 5th interval ranges from 446-557 and shows no counties had a total number of deaths in this range. The 6th interval has a frequency of 2, with two counties with deaths between 557 and 668. This last interval could be considered an outlier since the data values differ significantly from other observations (given the lack of values in interval 5).

A histogram allows the distribution of numeric data to be explored.  They allow visual assessment of distribution shape, central tendency, data variation and gaps or outliers in data values.  Some statistics can be added to the histogram such as the mean, median and normal distribution.  Additional related statistics can also be calculated on the data and, in ArcGIS Insights, are automatically included on the back of the chart cards to quantify the chart.  A histogram with normal distribution is symmetrical and will have a skewness of 0.  The direction of skewness is shown by the tail of the distribution so if the tail on the right is longer (as shown above), the skewness is positive. If the tail on the left side is longer, skewness is negative.

Box plots can be grouped by a categorical variable, such as state, which allows for a comparison of distributions.  The data is plotted so that 50% of the data is inside the box between the lower (Q1) and upper (Q3) quartile and, the median is shown as a line.  Whiskers contain a further 25% of the data, above and below the interquartile range (IQR), which is the length of the box (Upper quartile – lower quartile).  Values that extend beyond 1.5 IQR are outliers.

This boxplot shows the distribution of deaths, by county, for those states with the highest number of cases (minus New York) [April 16 2020]. It shows some deaths have occurred in all of these states. If we focus on the New Jersey boxplot, we see the median is closer to the bottom of the box (the 2nd quartile is smaller than the 3rd quartile), and the lower whisker is shorter than the upper whisker, so the distribution is positively skewed. Compare this with other states, that again are all positively skewed. Many states, such as Illinois, have counties with zero deaths. In Illinois, the outliers span a huge range from 3-722 (in Cook County), which indicates that the deaths in Illinois are concentrated in only a few counties. In this example, we are, therefore, able to identify those states where deaths have occurred throughout the state, such as New Jersey, compared to others where deaths have been confined to specific counties, such as Illinois.

Visually exploring data is a key step of analysis and can mitigate modeling errors.  During modeling, data is often aggregated to ensure that there are enough data points in the analysis for it to have statistical robustness, but this step can hide missing data or data collection changes, such as changes in international classification of disease coding practices.

This chart shows the age-adjusted death rates for selected leading causes of death: United States, 1958–2017 Source: National Vital Statistics Reports, Vol. 68, No. 9, June 24, 2019. The rate of top conditions per 100,000 uses a log-scale. Different ICD (International Classification of Diseases) codes can impact studies that use data over many years. The apparent sudden appearance of Alzheimer’s disease in 1980 belies the fact that Dr. Alzheimer’s work was published in the early 1900s and it was only introduced with a specific ICD code with the ICD-9 classification in 1980.

Different visualizations will give a different perspective on data and being able to explore and visualize data in numerous ways can help with understanding many aspects of the study data.  The more involved the analysis, the more important it is to describe and visualize data before any modeling is carried out.

Temporal dimensions of health data

Time associations and patterns with epidemiological data are most commonly visualized using line graphs for continuous date/time data, and epidemic curves that traditionally use bars without gaps.

Epidemic curves graphically show the frequency of new cases compared to the date of disease onset.  An epidemic or epi curve shows date or time of illness onset among cases on the x-axis and vertically, the y-axis shows the number of cases.  The unit of time used is based on the incubation period of the disease and the time over which cases are distributed.  The overall shape of the curve can reveal the type of outbreak (for example, common source, point source or propagated).

‘Epi curve’ for confirmed cases of COVID-19, by state [April 1, 2020]. (Note: Data collection differs by state and is based on testing results, so this example does not use onset date as an epi curve should.) Only those states with over 15,000 confirmed cases are shown here. A 3-day rolling average was taken, to take some account of differences in data collection. The starting point for each state is the day that particular state had reached 10 total confirmed deaths.

Epidemiological analyses can involve data that spans long periods of time (to capture sufficient events or rare outcomes), within which there may have been many changes to the data collection methodology.  As part of the process of analysis, input data should be well understood, and limitations noted particularly for studies with complex interactions that may not be fully understood.  The same might be true for new diseases which, by definition, will be poorly understood.  Although past information and similar events will be used to understand potential patterns of disease spread over space and time, data reported in the early phases will be prone to unknown (and unquantifiable) error and uncertainty.  This uncertainty has the additional impact of making it difficult to understand if previous events are in fact similar and, therefore, comparable.

Visualizing temporal data on a timeline helps to reveal data gaps, for example, in data collection.  Analyzing data that may vary over space and time should not be done without evaluating the data prior to analysis, both temporally and spatially.

A lot of temporal analysis will use generic data, such as the results of decennial census surveys, to evaluate patterns among different population sub-groups.  However, the further you are from a census year, the more the accuracy of that data will reduce. Although this limitation must be accepted, exploring the temporal differences between the known data may help modeling and can certainly help interpretation.

In ArcGIS Insights, timelines are automatically created from date/time data. Additionally, the date/time components are automatically calculated and added on data import. A logarithmic scale (as shown above) can make comparisons easier if the relationship between time and the number of cases is exponential or if data has a wide range of values. A log scale can also be used to show the relative rate of change.

Dealing with different health geographies

Intervention and response areas can differ to those used for epidemiological analysis, with each having very different requirements.  Response needs may be driven by health regions, for example, whereas analysis tends to be more closely aligned to census areas due to ancillary data availability and (often assumed) socio-economic homogeneity of those areas.

Spatial analysis can be used to define the study area(s).  Filtering the data can be done by selecting areas from the map or using additional boundary datasets.  This can be valuable to sub-divide data into exposed populations or cases and non-exposed or control populations.  Most of the data used for analysis will be aggregated based on administrative boundaries, whereas exposed populations not defined by administrative areas.

In some cases, when the dataset contains spatial units as a data field, data can be analyzed non-spatially by different geographic boundaries.  In other cases, when the data needs to be ‘shifted’ to geographic areas not contained in the dataset, spatial location can be used to ‘move’ the data to different areas. In these cases, the data can be available as individual counts or even total by area.  Reapportionment of data between different geographies permits the translation of data between very different geographies and, thus, allows reporting of aggregated data at different boundaries.

Traditionally, there have been marked socio-economic differences between urban and rural populations. Although this trend is starting to change, spatial data accuracy and precision are often linked to population density, with rural areas tending to cover large areas that can encompass marked social and economic differences. These differences can result in disparities between urban and rural areas.  Incorporating spatial analysis ensures that data can easily be stratified, for example by urban/rural areas for epidemiological modeling.

Health analysis often uses data aggregated by area. The boundaries can be very different, for example between US counties, US Postal service zip codes and urban areas. Yet, analysis may require analyzing data from all three, together.

Different types of data joins for health analysis

Traditionally, a GIS stores spatial data as a feature by location.  The data may be raster, using regular cells, or vector, using points, lines or polygons (areas).  At each location there may be one or more associated pieces of information (for example, population by administrative area).  However, in epidemiology, almost all analysis must include multiple components by location (for example, population by age and gender breakdown).  Technically, this requires a one-to-many (feature to health and demographic variables) relationship.

To overcome these different data structures, data can to be joined as a step of the analysis so that each location, be that point, line or area, can be associated with multiple attributes or rows of information.  This is a crucial step in ensuring that spatial and epidemiological analysis can be successfully integrated.  Furthermore, in some cases, compound joins (for example, using location and time) are needed.

In ArcGIS Insights, data can be joined in multiple ways. A number of different relationships can be selected, and joins can use attributes or location. Compound joins can be used, for example to combine data spatially and temporally. In many cases, epidemiological data will be stored in relational databases and, ArcGIS Insights allows direct connections to be setup to a number of different databases and, wherever possible, computations are sent directly to the database to ensure efficient processing.


This blog has briefly outlined five topics of consideration in epidemiology and how ArcGIS Insights can be used as part of the analysis solution.

Many of these topics are far more involved and, as with all analytical work, effective analysis requires reliable data, in tandem with sound knowledge of previous relevant studies.  An epidemiologist should be well versed in dealing with a lack of either and often, this is where true expertise lies.

Complex models and effective communication of results are a key part of the process.  In Part 2 of this blog, we will explore those topics amongst others.

About the author

Linda Beale

Dr Linda Beale is the Group Lead for Location Analytics at Esri, with an interest in sharing the value of spatial analysis with an audience ranging from those new to the discipline to those who are seeking fresh approaches and techniques. A geographer by training, Linda gained her PhD in GIS, statistics and modelling, and led the geospatial health group in the Small Area Health Statistics Unit at Imperial College London. Linda has extensive experience in the field of spatial epidemiology and has worked closely with Health Departments, the World Health Organisation and Center for Disease Control. She developed the award winning Rapid Inquiry Facility program for chronic disease modelling and was co-author on the landmark Environment and Health Atlas for England and Wales. Linda is the author of the first Esri MOOC, Going Places with Spatial Analysis, and she has published numerous peer-reviewed papers, book chapters, and been invited to keynote, present and deliver workshops at national and international conferences. Linda has worked at Esri since 2011, where her experience helps shape location analytics to provide the community with better and more powerful tools, and where she helps teach best practices and sharing of knowledge to develop understanding across the wider community.


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