ArcGIS Insights

Epidemiology and ArcGIS Insights - Part 2

This is Part 2 of a two-part blog exploring the scope of epidemiology, how GIS provides an important analytical component, and what ArcGIS Insights can do to support epidemiological analysis. Part 1 of the blog should be read before you dive into this second part.

Just to recap, 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.  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.

Part 1 of the blog identified the first five of ten main areas of epidemiological study. Here, we round it up to ten with a further five areas of study and application.

Creating composite indicators

Deprivation indices are widely used in epidemiological research and for local and national government applications.  Composite measures can be created for specific purposes or existing indicator variables can be combined to create a situation specific index.  These indices are designed to capture a variety of socio-economic characteristics and ensure that multiple population characteristics, which may impact the outcome to the health event, are being analyzed.

To combine data that represents different factors uses different measures, such as unemployment rate and percentage of mobile home ownership, requires that data be standardized and possibly transformed.  Data can be standardized using Z-scores so that all variables have a mean of zero and a standard deviation of 1 and are, therefore, equally weighted when combined.  In some cases, input variables will have skewed distributions. These variables can be transformed to (near) normal distributions (for example, by taking the log transformation), again to ensure they can be combined with other variables to avoid skewing the final index.

The CDC Social Vulnerability Index (SVI) uses 15 census variables at the census tract level, for use in emergency management. Darker symbols indicate increased social vulnerability. Source: Flanagan, et al. (2011) "A Social Vulnerability Index for Disaster Management, " Journal of Homeland Security and Emergency Management: Vol. 8: 1, 3.

Indices are an excellent way to assess relative circumstances but the scale at which they are calculated can sometimes be problematic. For instance, an index calculated at a national scale can mask local conditions. In some cases, regional analysis can therefore benefit from indices that use variables and/or values more tailored to the local communities, such as local house prices or income rather than using national values.

Using ArcGIS Insights, data can be transformed using built in functions in the data table. Taking the log of population changes the data distribution from having a positive skewed to a near normal distribution, as shown by the overlaid red line.
Data can be combined into an index once the z-score has been calculated, again using a built-in function, so that each variable is using a relative value and data with different units can be combined.

Graph networks for health analysis

Network analysis and graph theory are well placed for both understanding the spread of contagions and influences of social interaction on population health.  Furthermore, they can be used to analyze the impacts of intervention strategies since social network dynamics can influence disease spread.

Using graph theory, a network is seen as a set of interconnected links and nodes from which relationships can be identified.  Typically, a node or vertex represents an entity, object, person or, place.  Links represent the relationships or connections between nodes.  Usually, nodes represent individuals, but they can also be groups or even geographic locations.  Centrality metrics can subsequently be calculated and used to determine the important nodes in the graph, and different measures can be determined from the analysis, such as finding the most connected individuals or, key ‘bridges’ in a network.

Assessing network structure, which is defined by transmission pathways, can be valuable to reduce the spread of infection. (for example, for contact tracing to identify connections and potential transmissions to contacts after exposure to an infected person).  At scale, contact tracing requires managing sensitive data at large-scale. Successful control of outbreaks through contact tracing is often related to a number of factors, such as number of initial cases, the onset period and the basic reproduction number (R0).  Contact tracing can also be used to understand disease etiology, such as infectiousness, and can play an important role in analyzing a novel virus.

This map shows geographic transmission with flow lines colored by the clades. ArcGIS Insights allows datasets to have multiple shape fields. Data with, for example, to and from locations can be mapped as flow maps, with directionality. Nodes can be sized, using centrality metrics and links can be weighted and colored by different variables. Source:

A graph database is traditionally used to store data with a structure of nodes, edges and, properties.  Technological advances now mean that linkages in a data set can be automatically created and visualized from data stored in flat files or relational databases, and which can then be used in conjunction with other analytical approaches.

Link charts can be used to show relationships, such as those between originating regions, countries and clades. During an epidemic, pathogens naturally exhibit random mutations to their genomes, often by the genome type. This information can, therefore, be used as an indicator of transmission. Source:

Epidemiological analysis needs to go beyond simply combining numerous variables by area and rather focus on understanding the population and interactions within those areas.

Integrate with custom models and open data science

Although many different approaches and technologies are used in epidemiology, there are also some very specific discipline techniques used for data collection, interpretation and analysis.  A variety of statistical methods and advanced statistical packages are used.  In many cases, standard epidemiological models are used, and in some cases, custom models are developed.

Numerous epidemiology models have been developed over time, many of which are freely available in R and to a lesser extent in Python.  R is both a programming language and software that enables simple to complex analysis.  The ability to explore and describe data both visually and quantitatively in addition to the active development and maintenance of code, in many cases by subject matter experts, makes it an invaluable tool for epidemiologists.

Epidemiology, like many other disciplines and specializations, often uses accepted tools and software that have a long history of use for analysis in those areas.  Equally, GIS offers a number of methods and tools that are invaluable. It is important that these functions are easily accessible to those who may not be able to work solely in GIS, but rather can be used to compliment and strengthen other analysis.

Insights supports connecting to Jupyter's Kernel Gateway version 2.1.0 (an open source web server distributed through conda-forge and other repository channels). A kernel gateway can be set up and deployed using Anaconda or Docker (click the image above to be redirected to the GitHub site that describes the process). When using R or Python, data can be passed through from Insights or results passed back to Insights using a magic command. Additionally, visualizations can be added as cards into Insights. Crucially, ArcGIS Insights supports epidemiologists in providing them a way to build on top of what they already do in R and Python, not trying to replace it.

Document and re-run workflows

Any analysis should ensure methods are transparent, and the more involved the modelling undertaken, the more important this becomes.  Distilling complex pathways through data inevitably requires some form of decision making, aggregation/generalizations or even using proxy variables. Ensuring the reader can interpret the findings with some knowledge of the steps taken means that informed choices can be made. Going one stage further, spatial analysts are likely poorly positioned to properly understand and run epidemiological analysis. Collaboration is often the key to high quality spatial epidemiology.

Epidemiological analysis is rarely run once. For example, during disease outbreaks, analyses will have to be re-run as data changes and, in some cases, as understanding grows.  In some situations, there may be a need to evaluate outcomes based on intervention approaches. Models that can easily be re-run are, therefore, highly desirable, although this is complicated by the fact that, in many cases, multiple tools with data from various, often unrelated sources, are used.

ArcGIS Insights automatically captures your analysis and creates a model. At any point in your analysis, the steps required to reproduce are documented in this analysis view. The model can be shared and you or, others can hydrate it with other data and re-run the workflow. The workflow diagram, as shown here, also forms part of the workbook making the overall analysis steps transparent.

Effectively communicate results

It takes skill to bring together results from numerous sources- ̶ many of which are the output of involved modeling ̶ and use data that cannot be shared until anonymized in some way.  Unfortunately, this work is often undertaken in a compressed timeframe to suppport the immediate need to disseminate findings and provide information for policy, and for the public.  Furthermore, those who carry-out the analysis are heavily embedded in the work and, intimately know the data, analysis shortcomings and overall findings. Most readers will not have the background knowledge the analysts have developed, yet may be under pressure to make rapid decisions that have consequences for response and resourcing.

Often, the first consideration when communicating results is the need to maintain confidentiality.  A number of techniques exist, from aggregating results to suppressing small numbers (which could lead to identification), and the details of each differ by communication type and format.  In most cases, results will be accompanied with confidence intervals, which are an integral part of the findings and should be considered an important aspect of decisions making.  It is this detail that will drive effective response and planning, and also communicate an element of uncertainty in reported results to alleviate the risk of people seeing the analysis findings as incontrovertible fact.

Epidemiological studies are rarely definitive but are based on careful and complex analysis that advances with ever increasing understanding.  The desire (and need) for categorical answers is not easily fulfilled by epidemiological analysis that is often looking for ‘a needle in a haystack,’ and levels of uncertainty are important to also describe.

ArcGIS Insights is being used by many organizations to help them understand and plan response during the COVID-19 situation. The State of Georgia compares results from different prediction models, including the CHIME model, IHME model and, a model created by Georgia Tech. Workbook pages are used to show different analyses. Capacity planning considers bed and ventilator surplus or shortage, compared to the expected peak. Tactical analysis shows how many ventilators are in use, by hospital and region. Locations and numbers of the vulnerable such as senior citizens are a key component in decision making.
Many demographic variables will be made available with an associated margin of error. In other cases, models will output results to include measures of uncertainly, such as confidence intervals or credibility intervals. Including this information visually on maps and charts can help readers to better understand the results. Accepting that models can help guide, rather than define response, ensures that decisions include discussion among stakeholders and experts. In many cases, ‘local knowledge’ can be a vital component in this dialog.


In this, two-part blog, I have outlined ten topics in relation to epidemiology GIS, and demonstrated the value of combining the two using ArcGIS Insights, with examples using COVID-19 data.

Now, more than ever we have the opportunity to embrace many of the challenges that epidemiological analysis presents.  Sourcing data is no longer the problem it once was and, better yet for many of us, it is routinely geocoded.  Technology gives us the ability to analyze the data.  Advanced functions are readily accessible.  Effective communication is made possible through numerous visualization techniques.

It is still a challenging area of analysis, combining skills from many disciplines, requiring thoughtful and careful analysis.  It is this though, in a large part, that can make epidemiological analysis so rewarding, and which can reap real benefits for individuals and society more general.

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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