The 43pl consortium operates a unit trust which facilitates participation in the CRCSI by a large number of small to medium sized enterprises. Founded in 2003, the CRC for Spatial Information (CRCSI) is a research organisation funded by Australia’s Cooperative Research Centre Program and by participant contributions.
SpaceStat 4.0 represents a major reworking of the underlying architecture of the application. Multithreading has been introduced improving the performance of many methods. A LePace-Sage estimator for spatial-error and spatial-lag analyses has been added to the spatial regression method.
Based on customer feedback, we have designed feature enhancements in SpaceStat 4.0 that improve the appearance, functionality and performance of maps and graphs. You will also find that the extensive help documentation has been updated, revised and expanded.
Additionally, we’ve responded to your requests for a SpaceStat virtual class by adding a series of tutorials to our website. Each tutorial comes with a SpaceStat project designed to get you started working with a specific concept, and provides a landing page with a description, time estimate and associated project links.
New LeSage-Pace Estimator
A LeSage-Pace estimator for spatial-error and spatial-lag analyses has been added to the spatial regression method.
“I am involved in developing and applying multiple regression models for the mass valuation of residential real estate properties. Modelers such as me are always seeking to find improved model accuracy. The spatial regression models in SpaceStat are of particular interest. The addition of the LeSage-Pace output makes it easier to compare to other methods. Incidentally the Spatial Error Model has been the best performer among all models I have tested lately. It is featured in a book I have written on spatio-temporal methods in mass appraisal to be published June 2014. Also thanks to BioMedware for making this change to the product.”
Richard A. Borst, PhD
Tyler Technologies, Inc.
Recently Published Research using SpaceStat…
Int. J. Environ. Res. Public Health 2014, 11(4), 3765-3786; doi:10.3390/ijerph110403765
Authors: Mahdi-Salim Saib, Julien Caudeville, Florence Carre, Olivier Ganry, Alain Trugeon and Andre Cicolella
“We used Spacestat to evaluate relationships between spatial data collected at different geographic scales. Spacestat is easy-to-use and provides powerful tools that make possible spatial data processing, exploratory analysis, and the quantification of spatial relationships in environmental health research. Spacestat is extraordinarily useful for stakeholders seeking to prioritize prevention actions in the context of environmental inequalities reduction.”
French National Institute for Industrial Environment and Risks (INERIS)
Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France
BMC Cancer.2014, 14:255. DOI: 10.1186/1471-2407-14-255
Authors: Nordsborg Baastrup Rikke, Meliker R Jaymie, Ersbøll Kjær Annette, Jacquez M Geoffrey, Poulsen Harbo Aslak, Raaschou-Nielsen Ole
A large proportion of breast cancer cases are thought related to environmental factors. Identification of specific geographical areas with high risk (clusters) may give clues to potential environmental risk factors. The aim of this study was to investigate whether clusters of breast cancer existed in space and time in Denmark, using 33 years of residential histories.
We conducted a population-based case–control study of 3138 female cases from the Danish Cancer Registry, diagnosed with breast cancer in 2003 and two independent control groups of 3138 women each, randomly selected from the Civil Registration System. Residential addresses of cases and controls from 1971 to 2003 were collected from the Civil Registration System and geo-coded. Q-statistics were used to identify space-time clusters of breast cancer. All analyses were carried out with both control groups, and for 66% of the study population we also conducted analyses adjusted for individual reproductive factors and area-level socioeconomic indicators.
In the crude analyses a cluster in the northern suburbs of Copenhagen was consistently found throughout the study period (1971–2003) with both control groups. When analyses were adjusted for individual reproductive factors and area-level socioeconomic indicators, the cluster area became smaller and less evident.
The breast cancer cluster area that persisted after adjustment might be explained by factors that were not accounted for such as alcohol consumption and use of hormone replacement therapy. However, we cannot exclude environmental pollutants as a contributing cause, but no pollutants specific to this area seem obvious.
by Geoffrey M. Jacquez1,2 and Robert Rommel2
1. Department of Geography, State University of New York at Buffalo, Buffalo, NY
2. BioMedware, Ann Arbor MI
Introduction: Perhaps one of the greatest challenges and limitations in environmental health and epidemiology is that of measurement of individual health outcomes, their causes, and correlates. Data on risk factors and exposures are often measured with imperfect instruments such as surveys that are inherently inaccurate and subject to recall and other bias. At present, biomarkers may provide reasonable estimates of exposures, but can be difficult to obtain and are available for only a small number of compounds. Ideally, epidemiologists and exposure assessment scientists would have timely information regarding measurements of individual-level physiology and ambient environment (e.g. at the human boundary layer), the specific times and locations where these measurements were collected, and in what settings.
We are at the beginning of a revolution in measurement that has the potential to transform environmental health and epidemiology (Swan 2013). We argue this transformation will require new ways of thinking about data sharing, consent, privacy and confidentiality, and the formation of an organization to foster the use of personal, crowd-sourced data for the common good. We begin with a working definition of the genome+, exposome and behavome, and examples of how technology is revolutionizing their measurement. Next, we consider technology trends in the quantifiable self, and where these will lead in the near future. We predict these trends will culminate in a new era for epidemiology and environmental health provided mechanisms are established to foster sharing of individual information from these new data streams. We close with a call for action to form an organization charged with governance, data security, and mechanisms for data sharing, with the ultimate mission of advancing epidemiology, the environmental health sciences, and human health.
Genome+, exposome and behavome: Consider a conceptual model of three important determinants of health (Figure 1). Both illness and well-being are treated as core outcomes, whose expression is influenced by the genome+, exposome and behavome. An individual’s “Genome +” is comprised of their genome (genetic composition), regulome (which controls gene expression), proteome (their compliment of amino acids and proteins) and metabalome (the basis of metabolism and homeostasis). Together, these constitute a good portion of an individual’s biological makeup. The exposome is defined as the totality of exposures over a person’s life course (Wild 2005). We define the behavome as the totality of an individual’s health-related behaviors over their life course. Wild’s definition of the exposome included behavioral determinants of exposure; we treat the behavome separately to clarify the role of human behavior in mediating the exposome (through health behaviors such as smoking, exercise, diet and so on), as well as interactions between the exposome and the genome+ (for example, many behaviors are now recognized to have a genetic component, such as a predilection to alcohol and substance abuse). These determinants of human health act through place, defined as the geographic, environmental, social and societal milieus experienced over a person’s life course.
Technology trends in measurement of the genome+, exposome and behavome: Measurement of the genome+, exposome and behavome is an enormous challenge. Nonetheless, the last few years have seen major advances in measurement. We now provide a few examples of such advances, before considering implications of these technology trends for environmental health and epidemiology.
Measurement of the genome+: Continued improvements in sequencing technology are dramatically reducing the cost of sequencing individual genomes. In 2000 the Human Genome Project was completed, having sequenced the first whole human genome, at a cost in excess of USD$2 billion (Davies 2010). In 2012 the 1000 genomes project released their phase 1 sequencing data (Pybus et al. 2014). This project is first to sequence the genomes of over 1,000 individuals, sampled to document human genetic variation across 25 populations from around the globe (The_1000_Genomes_Project_Consortium 2012). When it began, costs for fully sequencing an entire genome were high, and as a result only a portion of each genome was sequenced. But the cost of whole genome sequencing continues to drop, and the USD$1,000 whole sequence genome is now available (Hayden 2014). In medical practice and research whole genome sequencing is posing ethical challenges regarding the amount of information to disclose to the individual, especially given incomplete knowledge of the genetic basis of disease (Yu et al. 2013). Nonetheless, we expect whole genome sequencing for individuals to soon be a commodity available for USD$100 or less. That sequence data will support viable business models is being proven out by companies such as 23andme (see ISOGG.org for a list_of_personal_genomics_companies), which offer partial sequencing using saliva samples to explore ancestral origins and disease risks. These dramatic reductions in cost of measurement are also occurring in the exome, epigenome, and other constituents of the genome+ (Zentner and Henikoff 2012, Weinhold 2012, Meissner 2012, Mefford 2012). It is clear that measurements of the genome+ will soon be widely and inexpensively available, and will be incorporated into individual electronic health records, notwithstanding the informatics and ethical challenges posed by their integration (Kho et al. 2013, Flintoft 2014, Tarczy-Hornoch et al. 2013, Hazin et al. 2013).
Near real-time physiological measurement is expanding rapidly in clinical medicine as well as in the burgeoning “wearables” marketplace. In 2012 Qualcomm established their Tricorder XPrize, with the goal of creating a wireless handheld device that monitors and diagnoses a patient’s health conditions using personal health metrics. The top three entries are to be announced in May, 2014. Wearable and implantable wireless sensors for healthcare monitoring include cancer detection, glucose monitoring, seizure warning, cardiac rate and rhythm, and heart attack detection, among others (Darwish and Hassanien 2011). Google is now testing a glucose monitor incorporated into a contact lens (Landen 2014), and “smart” t-shirts for monitoring pulse, respiration and stress levels will soon be on the market (omsignal.com). Activity sensors such as those from Jawbone and fitbit are currently available, priced at around USD$100, and provide a record of daily activity (e.g. steps taken, distance traveled, time asleep). Data from wearable sensors is already being used to monitor physical activity levels in pediatric patients (Yan et al. 2014). At the 2014 Consumer Electronics Show wearables were prominent and recognized as an emerging market segment (Rowinski 2014). While still small, the digital health market segment received $1.9B in venture funding in 2013, and posted 39% growth. It has more than doubled since 2011 (RockHealth 2014). Even so, the marketplace is nascent and fragmented, it is unclear what will succeed and what will not, and we have only an imperfect understanding of what people will adopt (Figure 2). It seems reasonable, however, to assume that wearables as a technology will rapidly evolve and that their adoption and use will expand quickly. This potentially poses a great opportunity for measurement in the environmental and health sciences.
Measurement of the exposome: The challenge for quantification of an individual’s exposome is measurement of the ambient environment at the human boundary layer – the epidermis, mouth, mucosa, and nasal passages – where contaminants and pathogens enter the body (Balshaw and Kwok 2012). This requires wearable sensors integrated into clothing (e.g. smart shirts, pants and shoes), jewelry and bracelets, or wearable on the lapel ( see (Windmiller and Wang 2013) for a review). Recognizing their importance for quantification of the exposome, the National Institutes of Health has funded several initiatives to develop such “environmental tricorders” (see for example grants.nih.gov/grants/guide/rfa-files/RFA-ES-09-005.html). Environmental tricorders are already on the market, although the environmental factors they monitor are somewhat limited, including Volatile Organic Compounds, dust, light, sound, ionizing radiation, carbon dioxide and others. Examples include products from Valarm and Sensorcon, among others. The Knight foundation recently funded prototyping of the “Global Sensor Web”, whose objective is to create an online platform for aggregating geo-tagged data sets from public and personal data sources (www.knightfoundation.org/grants/201347663/), although these are not necessarily data from wearable sensors. As noted below, there currently is a distance between sensors of sufficient quality, accuracy and precision to be of immediate use in environmental epidemiology, and the low-cost sensors currently being adopted in the consumer marketplace.
Measurement of the behavome: We think of the behavome as separate from Wild’s exposome, as health behaviors are key mediators of exposures. Behavioral recognition methods for assessing what an individual is doing has for decades been an important topic of health research. With the advent of sensors in residences, health care facilities, and wearable on patients, the issue of multisensor data fusion for activity recognition has become an important topic. These technologies are already being deployed and assessed in nursing home and assisted living facilities. Recent research has demonstrated these methods can identify risky behaviors with good accuracy and low deployment costs (Palumbo et al. 2013). The “internet of things” including smart homes, smart cars and smart workplaces, is in the early phase of what many predict to be explosive growth (Ashton 2009). In 2008 the number of devices on the Internet exceeded the number of people, and in 2020 will exceed 50 billion devices (Swan 2012). Information on when, where and how we use appliances, electronic devices, machinery and environmental controls in home and workplace settings, and while commuting, have yet to be used to quantify the behavome. The value of near real-time data on ambient temperatures and how often and when we use the refrigerator may have enormous value for quantifying, for example, personal energy budgets. A variety of different approaches for assessing health behaviors have been suggested using technologies such as inertial sensors, Global Positioning System, smart homes, Radio Frequency IDentification and others. Most promising is the sensor fusion approach that combines data from several sensors simultaneously (Lowe and ÓLaighin 2014). To our knowledge technologies such as Google Glass have yet to be used for capturing video images to chronicle dietary intake and other health-related activities. Other potential applications include quantification of personal energy budgets, individual walkability (e.g. (Mayne et al. 2013)), and documentation of other personalized environmental metrics. Once health-related behaviors are known, the possibility of using gamification (Whitson 2013) and other approaches to encourage salubrious behaviors become possible (Schoech et al. 2013).
Where will these measurement trends lead? At present there are two domains for measuring the quantified self, the high-end approach focused on measurement accuracy and precision, and the quantified self as a commodity that is focused on capturing the consumer market (refer to Figure 3). We see the possibility of a future convergence and emergence of low-cost sensors of sufficient quality to support a common good – high quality environmental health research, made possible by volunteered information provided by enlightened citizen scientists. But achievement of this goal likely will require the establishment of appropriate mechanisms of data sharing, oversight and governance, and the creation of a user community of sufficient market mass to influence the development of COTS sensors of sufficient quality to support research in environmental health and epidemiology.
What benefits might be realized? There is a growing recognition that new ways of measuring ourselves require new ways of understanding what “normal” means (McFedries 2013). We know amazingly little about the ambient environments individuals experience through the course of their daily lives. Similarly, we know very little about the local environments experienced by the members of local communities across the US and around the world. How much temporal and spatial variability is there in air quality at the human boundary layer for individuals in diverse communities? What are the exposure profiles of children as they move about their daily lives in our neighborhoods and schools? A national baseline environmental assessment, incorporating data on the quantified self collected by citizen scientists, may begin to address such questions. Important issues will need to be addressed regarding data quality, data sharing, sampling design and governance, but these do not appear to be insurmountable (Goldberg et al. 2013).
A call for action. It seems reasonable to the authors to assume the technology trends in figure 3 will indeed result in more accurate measurement of individual-level physiology, exposures and genetics at decreased costs. In fact, the commoditization of the quantifiable self is rapidly taking place, as demonstrated by products such as Google Glass, fitbit, the advent of environmental tricorders from sensorcon, Valarm and others. The data collected by these technologies is a highly valued business asset, and as such is not likely to be shared with research scientists for advancing epidemiology and public health. This balkanization of data for measuring the quantifiable self may be ameliorated by appealing directly to the individual to act both in their own self interest and also for the common good. We must provide an alternative for the highly vested, motivated citizen scientist, so they may choose to share their personal measurements for the good of all. This will require the formation of an organization charged with governance, data sharing, data security, and oversight of research and data use, with the overall mission of advancing human health. Rather than a balkanization of measurements on the quantifiable self into isolated information silos under the control of corporations, we envision the enlightened sharing of such personal measurements that will lead to safer neighborhoods, workplaces and communities. We believe our professional organizations, the AAAS, Sigma Xi, ISEE, and the AAG, are best positioned to take a leadership role in addressing this need. We encourage readers to consider bringing this to the attention of their professional organizations.
Research sensors, such as those funded by grants from NIH and Qualcomm’s Xprize challenge, strive for accuracy and high quality, but are expensive. The emergence of the wearables marketplace is resulting in sensors as commodities, COTS (commercial off the shelf) sensors that are relatively inexpensive but not necessarily of sufficient quality (e.g. accuracy and precision of measurement) to support environmental health research. We see a trend towards higher quality, low cost sensors (Future bubble) that may be suited for baseline environmental and population-level exposure assessment.
Ashton, K. 2009. That ‘Internet of Things’ Thing. In RFID Journal.
Balshaw, D. M. & R. K. Kwok (2012) Innovative Methods for Improving Measures of the Personal Environment. American Journal of Preventive Medicine, 42, 558–559.
Darwish, A. & A. E. Hassanien (2011) Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring. Sensors, 11, 5561-5595.
Davies, K. 2010. The $1,000 Genome: The Revolution in DNA Sequencing and the New Era of Personalized Medicine. New York: Simon and Schuster, Inc.
Flintoft, L. (2014) Phenome-wide association studies go large. Nature Reviews Genetics, 15.
Goldberg, D. W., G. M. Jacquez, W. Kuhn, M. G. Cockburn, D. Janies, E. Pultar, T. A. Hammond, C. Knoblock & M. Raubal. 2013. Envisioning a Future for a Spatial-Health CyberGIS Marketplace. In Second International ACM SIGSPATIAL Workshop on HealthGIS (HealthGIS’13). Orlando, FL, USA: ACM SIGSPATIAL.
Hayden, E. C. (2014) Is the $1,000 genome for real? Nature News.
Hazin, R., K. B. Brothers, B. A. Malin, B. A. Koenig, S. C. Sanderson, M. A. Rothstein, M. S. Williams, E. W. Clayton & I. J. Kullo (2013) Ethical, legal, and social implications of incorporating genomic information into electronic health records. Genetics in Medicine, 15, 810–816.
Kho, A. N., L. V. Rasmussen, J. J. Connolly, P. L. Peissig, J. Starren, H. Hakonarson & M. G. Hayes (2013) Practical challenges in integrating genomic data into the electronic health record. Genetics in Medicine, 15, 772–778.
Landen, R. (2014) Google lens for monitoring glucose has hurdles to clear before hitting market. Modern Healthcare. (link)
Lowe, S. A. & G. ÓLaighin (2014) Monitoring human health behaviour in one’s living environment: A technological review. Medical engineering & physics.
Mayne, D., G. Morgan, A. Willmore, N. Rose, B. Jalaludin, H. Bambrick & A. Bauman (2013) An objective index of walkability for research and planning in the Sydney Metropolitan Region of New South Wales, Australia: an ecological study. International Journal of Health Geographics, 12, 61.
McFedries, P. (2013) Tracking the quantified self [Technically speaking]. Spectrum, IEEE, 50, 24-24.
Mefford, H. C. (2012) Diagnostic Exome Sequencing — Are We There Yet? New England Journal of Medicine, 367, 1951-1953.
Meissner, A. (2012) What can epigenomics do for you? Genome Biology, 13, 420.
Palumbo, F., P. Barsocchi, C. Gallicchio, S. Chessa & A. Micheli. 2013. Multisensor Data Fusion for Activity Recognition Based on Reservoir Computing. In Evaluating AAL Systems Through Competitive Benchmarking, eds. J. Botía, J. Álvarez-García, K. Fujinami, P. Barsocchi & T. Riedel, 24-35. Springer Berlin Heidelberg.
Pybus, M., G. M. Dall’Olio, P. Luisi, M. Uzkudun, A. Carreño-Torres, P. Pavlidis, H. Laayouni, J. Bertranpetit & J. Engelken (2014) 1000 Genomes Selection Browser 1.0: a genome browser dedicated to signatures of natural selection in modern humans. Nucleic Acids Research, 42, D903-D909.
RockHealth. 2014. Digital Health Funding: A Year in Review 2013.
Rowinski, D. (2014) CES 2014: Connected Home And Wearables To Take Center Stage. readwrite. (link)
Schoech, D., J. F. Boyas, B. M. Black & N. Elias-Lambert (2013) Gamification for Behavior Change: Lessons from Developing a Social, Multiuser, Web-Tablet Based Prevention Game for Youths. Journal of Technology in Human Services, 31, 197-217.
Swan, M. (2012) Sensor Mania! The internet of things, wearable computing, objective metrics, and the quantified self 2.0. Journal of Sensor and Actuator Networks, 1, 217-253.
Swan, M. (2013) The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data, 1, 85-99.
Tarczy-Hornoch, P., L. Amendola, S. J. Aronson, L. Garraway, S. Gray, R. W. Grundmeier, L. A. Hindorff, G. Jarvik, D. Karavite, M. Lebo, S. E. Plon, E. V. Allen, K. E. Weck, P. S. White & Y. Yang (2013) A survey of informatics approaches to whole-exome and whole-genome clinical reporting in the electronic health record. Genetics in Medicine, 15, 824–832.
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Weinhold, B. (2012) More Chemicals Show Epigenetic Effects across Generations. Environ Health Perspect, 120.
Whitson, J. R. (2013) Gaming the Quantified Self. Surveillance & Society, 11, 163-176.
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Yan, K., B. Tracie, Marie-, #xc8, M. ve, #xe9, H. lanie, B. Jean-Luc, T. Benoit, M. St-Onge & L. Marie (2014) Innovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk Factors. International Journal of Pediatrics, 2014, 9.
Yu, J.-H., S. M. Jamal, H. K. Tabor & M. J. Bamshad (2013) Self-guided management of exome and whole-genome sequencing results: changing the results return model. Genetics in Medicine, 15, 684–690.
Zentner, G. & S. Henikoff (2012) Surveying the epigenomic landscape, one base at a time. Genome Biology, 13, 250.
This week I am attending the meetings of the North American Association of Central Cancer Registries that is being held in Austin, Texas. The topic of this year’s conference is “Thinking big, the future of cancer surveillance”, and I’m involved in two activities. The first was a series of workshops that occurred on Saturday and Sunday titled “Evaluation of Homomorphic Cryptography for Geospatial Studies with Human Subjects”. This workshop was convened as part of a grant funded by National Library of Medicine that is evaluating the feasibility of using homomorphic cryptography to accelerate the pace of research and discovery for studies that use human subjects data. “Homomorphic” means mathematical operations can be conducted on encrypted data (e.g. in the encrypted space), greatly reducing the risk to privacy of confidential data.
My co-organizer, Dr. Khaled El Emam of Privacy Analytics and the University of Ottawa e-health laboratory were very happy with the recommendations that came out of the working group. These are being written up as a BioMedware report to the National Library of Medicine, and will be available in our Publications when they are ready. But here is a preview of some of the “low-hanging fruit” that homomorphic cryptography may make possible.
First, increased data security greatly enhances data sharing, and hence participation in all manner of activities where data sharing plays an important role. It turns out a key bugaboo in the processing of disease registry data is deduplication; the removal of duplicate data records that may appear in several data bases. This arises, for example, when snowbirds flit between Michigan and Florida, yet have records of cancer tumor treatment in both States. The data providers must be very satisfied that the potential for unintentional release of their highly confidential patient records is absolutely minimal, meaning, in practice, that two data providers may be reluctant to share data to search for record duplicates. Homomorphic encryption solves this by having deduplication take place in the encrypted space – hence even if the data security is breached the records appear as complete gibberish.
Second, increased data sharing means data aggregation across data providers becomes far less of a concern. Hence activities that involve pooling data, such as determining the number of cases anticipated in projected enrollment reports for NIH grant applications, suddenly becomes very easy.
Other opportunities were identified – keep checking back for our release of the workshop report!
The CRCSI Health Program has established research activities in Australia and New Zealand and plans to take a lead role in developing research activities with international partners. In support of their expected growth, and to ensure that the science objectives of the program are being appropriately addressed, the Health Program Board has appointed Dr. Jacquez to a 2-year term as Co-Science Director of the CRCSI Health Program.
The CRCSI program is an Australian Government initiative to bring together researchers from universities, and other government organisations, and private industry or public sector agencies in long-term collaborative arrangements that support research and development and education activities to achieve real outcomes of national economic and social significance. Further information about the CRC Program is available from www.crc.gov.au
The first annual Western New York GIS Day was held August 15, 2012, at the Roswell Park Cancer Institute in Buffalo, New York. The meeting’s objective was to advance the use of GIS and spatial analysis to improve public health, health services, health planning and health research, and was the product of a collaboration between the State University of New York at Buffalo (UB) and Buffalo State University.
Speakers included Gale R. Burstein, MD, and Congresswoman Kathy Hochul, with keynote addresses by William F. Wieczorek, PhD, Director and Professor, Center for Health and Social Research, Buffalo State College, and Geoffrey M. Jacquez, PhD, Professor, Department of Geography, University at Buffalo and President, BioMedware.
From Directions Magazine: Meet Your Colleagues: Geoffrey Jacquez Ph.D.
“This week, we introduce you to Geoffrey Jacquez, president of BioMedware, whose geohealth-focused scientific methods and software tools are used by public health departments around the globe.” Read more.Directions Magazine
Others have blogged on the directions for Esri technology, the emerging role of GIS-in-the-cloud, crowd-sourcing data, and the potentials for social networking in geohealth. I attended the recent Esri International User Conference (UC) and realized that, while enormous strides are being made in technology development, geohealth still seems to be an emerging market segment. Why? Ready accessibility of health-related geospatial data of all types is a key need. Another key need is a clear vision of what “health analysis” means within a geospatial framework.
Data – first key need in geohealth: Todd Park, Chief Technology Officer of Health and Human Services, is advancing the precepts of open health data, and is making an increasing amount of geospatial health-related data accessible across government agencies. (Here’s a description of MedMap 2.0.) Nonetheless, geohealth analysis is often constrained by the 85/15 rule; spend 85% of your time finding and massaging the data, and 15% undertaking the analysis. Esri’s development of Community Analyst holds promise as a source for ready-to-use geospatial data, making available hundreds of thousands of data layers. Released in June, 2011, the utility of Community Analyst has yet to be demonstrated in applied geohealth studies, but it holds enormous promise.
Health analysis vision – second key need in geohealth: Geohealth is a sprawling topic area. At the Esri UC, I met with Bill Davenhall and we discussed the need for a well articulated vision of what comprises a geohealth analysis. What are the common themes? What questions are addressed, and what activities are undertaken in a geohealth analysis workflow? These issues have driven our development of SpaceStat software, and I would like to present a vision for geohealth analysis as I see it.
Geohealth – Definition and questions addressed: Geohealth assesses relationships between dynamic local environments and human health outcomes from individual- to population-level scales. It seeks to address questions such as these:
- Is a given health intervention appropriately targeted and effective?
- What neighborhoods have high cancer disparities?
- Where are the foci of spread of infectious diseases?
- Are vaccines being distributed in an equitable and efficient fashion?
- Where are “at risk” markets underserved by needed prescription products?
Geohealth activities in SpaceStat: What analysis activities may be undertaken in SpaceStat to address these and other questions? My list of favorite activities follows, each of which can be undertaken completely within SpaceStat with time-dynamic data.
- Describe data using descriptive statistics and statistical graphics
- Visualize data through time using time plots, synchronized windows and statistical graphics animation
- Visualize data geographically using maps, cartographic brushing and map animation
- Identify statistical, spatial and temporal outliers using boxplots, histograms, variogram clouds and LISA statistics
- Transform variables using the normal score and z-score transformations with time-slice and time-weighted means
- Evaluate rate stability to determine whether adjustment for the “small numbers” problem is needed
- Stabilize rates using empirical Bayes and Poisson kriging
- Interpolate data using nearest neighbor, distance and kriging methodsIdentify sub-populations with significant health disparities
- Identify clusters and undertake disease surveillance accounting for human mobility, known risk factors and covariates
- Quantify and model spatial dependencies using global and local spatial autocorrelation analysis
- Quantify and model spatial dependencies using variogram analysis
- Make predictions using aspatial regression through time (linear, Poisson, logistic)
- Make predictions using spatial lag, spatial error, geographically weighted, and multi-level regression
- Make predictions using geostatistics
- Analyze model residuals through time
- Compare model results to identify parsimonious models with the greatest explanatory power.
- Reduce dimensionality using PCA and other multivariate approaches
SpaceStat’s methods provide a comprehensive suite of tools that address a host of salient questions in geohealth. Send me a message with your vision for geohealth analysis and let me know what you think!