Maps and data esri health care 2012
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This document appears to be a presentation given by Tom Johnson at the Esri Health Conference in Scottsdale, Arizona on August 28, 2012. The presentation discusses how data and maps inform each other, with data being used to create maps and maps then guiding the collection of additional data. It also outlines four potential types of data/analytic variables that can be studied for any phenomenon: qualitative, quantitative, geographic, and timeline of change. The presentation argues that addressing complex health issues will require transdisciplinary collaboration and going beyond the traditional three-phase process of data in, analysis, and information out.
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Maps and data esri health care 2012
- 1. Data Makes the Maps; Maps Make the Data; Esri Health Conference Scottsdale, Arizona USA August 28, 2012 Tom Johnson Managing Director Inst. for Analytic Journalism Santa Fe, New Mexico USA t o m @ j t j o h n s o n . c o m@ j t j o h n s o n 1
- 2. Presented at Esri Health GIS Conference Scottsdale, AZ USA 28 August 2012 Presentation slides at www.slideshare.Net/jtjohnson Data Makes the Maps; Maps Make the Data by J. T Johnson is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License . 2
- 3. “ GIS: Unifying Theory/Methodology for Journalism and the Social Sciences?” J. T. Johnson GIS Center Prof. of Journalism San Francisco State University Krouzian Room tom@jtjohnson.com Bancroft Library Institute for Analytic Journalism 17 April 2003 3
- 4. 1 Important point All disciplines use same knowledge- making process 4
- 5. Fundamental process of all disciplines Data In Analysis Info Out •Info Arch. • Tools •Available skill •Sources • Available sets •Form/file type skill sets •Deliver the •Validity • Counselor data •Quality • Cost: time •Audience(s) •Cost & money •Updating? This 3-phase process is relatively traditional. So what’s changed? 5
- 6. • In dynamic infosphere, no individual can do all this: A team required • New management focus must be on coordinating cooperation/collaboration • Articulating objectives • Tools? • Training? • Project management 6
- 7. 2 Important point All phenomena possess the same four potential data sets/analytic variables 7
- 8. 4 aspects of data in ALL phenomena “Flurry of Photo ID Laws Tied to #1 Conservative Washington Group” Qualitative • Interview transcript • Field notes (notes taken in the field being studied) • Video • Audio recordings • Images • Documents (reports, meeting The flurry of bills introduced the last two years followed the 2010 midterm minutes, e-mails) Republicans took control of state legislatures in Alabama, election when • Images of types of qualitative Minnesota, Montana, North Carolina and Wisconsin. The same shift data occurred in the 2004 election in Indiana and Georgia before those states became the first to pass strict voter ID laws. 8
- 9. Aspects of data in ALL phenomena 1.Start by counting stuff 2.Build taxonomy(ies) Qualitative 3.Do basic statistics 4.“Hunches” about what’s going on #2 Quantitative 9
- 10. Aspects of data in ALL phenomena Qualitative Quantitative “External” Geography/geostatistics #3 Geographic 10
- 11. “Internal and Interior ” Geography Incidents in hospitals Qualitative Quantitative #3 Geographic Internal or interior Geostatistics 11
- 12. Aspects of data in ALL phenomena #4 Timeline of Qualitative change • Need trans-disciplinary Integrate timeline and geography skills to determine Quantitative Geographic which aspect is most important? • How to analyze? • How to present results 12
- 13. Center for Health Market Innovations
- 14. Staying a step ahead of diseases • Texas Pandemic Flu Toolkit • Web-based service that simulates the spread of pandemic flu through state • Forecasts the number of flu hospitalizations • Determines where and when to place ventilators to minimize fatalities. • Used in emergency situations for real-time decision-making • “Contact-network epidemiology” video 14
- 15. New toolkit demonstrates use of data-driven science to plan for future pandemics 15
- 16. Complexity and Social Network Analysis Computer experiments, along with real world data, generating new hypotheses and diagnostic and treatment applications. Source: http://www.youtube.com/watch?v=EvcgcffQxPc&feature=relmfu 16
- 17. 'Digital pill' with chip inside gets FDA green light • "ingestible sensor" invention. • The 1 square millimeter device -- roughly the size of a grain of sand -- can relay information about your insides to you, and if you choose, to your doctor or nurse. 17
- 18. Google Glasses 18
- 19. Big Challenges: Data In • Multiple ways to generate, retrieve and analyze health data • Health status precursors • Who sees it/them? • Status Indicators? Numbers, dials, spark lines, fever charts, • Services needed? • Location for services/patient needs? • Follow-up and status? 19
- 20. At the end of the day…. • Constant: Data In Analysis Info Out • Your profession probably won’t have direction or innovative answers about its future • Seek other -- or trans-disciplinary --methods and processes for insights • No more 8-hour work day. • 6 hrs “work,” 2 hrs. teach and learn 20
- 21. Data Makes the Maps; Maps Make the Data; Esri Health Conference Scottsdale, Arizona USA August 28, 2012 Tom Johnson Managing Director Inst. for Analytic Journalism Santa Fe, New Mexico USA t o m @ j t j o h n s o n . c o m@ j t j o h n s o n 21
Notas do Editor
- We usually think of making maps by marking things – data points -- on some often pre-determined two-dimensional surface called a map. Or in a more familiar term to geographers, a “Base Map.” That has been the tradition for literally millennia. But today’s technology for capturing data, putting it on a map is changing rapidly, to the point where making a map of our location on a cell phone is essentially instantaneous. At the same time, the disciplines of GEOSCIENCE and GEOSTATISTICS are making it possible to complete an If-Than command that results in more – and often unseen and unanticipated – data that generates yet new maps. This intellectual evolution – and a rapid one at that – has let me to reconsider some of my earlier conclusions about Geography as it can relate to multiple disciplines.
- Datasphere = environment holding all conceptual data of interest to humans Datasphere = similar to biosphere, except resources not depleted or transformed, merely copied Journalist: one species in the Datasphere Environment changes: Species either evolve or die =================================== Dataesfera = entorno que comprende todos los datos conceptuales de interés para los humanos Dataesfera = similar a la biosfera, con la excepción de que los recursos no se agotan o se transforman, simplemente son copiados Periodista:una especie de la Dataesfera Cambios en el entorno: las especies evolucionan o mueren
- Highway Africa 2001 Nearly a decade ago, I gave a lecture at UC-Berkeley on GIS and related disciplines [ click ] I was wrong! Today, I’ve expanded my perspective a bit. But first, let’s consider the process of not only journalism, but what we all do in ALL disciplines/professions/occupations. [click]
- The methodology determines the value of the data set and your story I’m suspicious of -- and reluctant to use – sweeping generalities and Adjectives, but in this case…. Appropriateness of method ALWAYS determines the validity of the analysis, though the method(s) (i.e. analytic tools) may vary depending on your objectives. Methods used to create a data set ALWAYS determine the validity and functionality of the data set Ergo, before we start crunching data and data mining, we need to recognize and know…. The methods used to create the data set determine: The reliability of the data set The functionality (for multiple audiences) of the data set (e.g. who called for the creation of this data set, when and why? Who is to use it for what ends? What is its “measured” value for original users and for our readers? Knowning and understanding those “methods of creation” determines the value of your analysis and, hence, your story.
- Data In Sources Form/type Validity Quality Cost Analysis Tools Available skill sets Counselor – a non-partisian rabbi to review your work Cost: time & money Info Out Info Architecture Available skill sets Deliver the data Audience(s) Updating? This process, in the Digital Age, drives multiple changes in organizations and management. [CLICK] In dynamic infosphere, no individual can do all this: A team required New management focus must be on coordinating collaboration
- Most [all?] data sets are living things . A data base, may look to be just a static matrix of text or numbers, but there are living, breathing dynamic forces at work in and around any data set that can provide an interesting context of understanding for journalists. And they have a pedigree, a genealogy. If we don’t understand that genealogy, we can’t evaluate – or properly use – that DB Data sets live in a dynamic environment. All data sets “live” in a context, in an environment in the datasphere that is constantly changing in terms of the validity of the data, who is collecting/updating/editing the data, who is using the data for what purposes and how often? How is Data Set A (or parts of it) related to DS B and C and G. And how do the administrators/analysts of the secondary data measure the quality of the data they are getting from DS A, if they do it at all? Understand the DB ecology See how the data set relates to other sets of data, agencies and users.
- So, when we consider the DataIn step, it turns out there are some more theoretical aspects to consider, but which work to our advantage: 4 factors of ALL phenomena, i.e. potential stories ====================================================== Qualitative Data? Qualitative data are forms of information gathered in a nonnumeric form. Common examples of such data are: Interview transcript Field notes (notes taken in the field being studied) Video Audio recordings Images Documents (reports, meeting minutes, e-mails) Images of types of qualitative data Such data usually involve people and their activities, signs, symbols, artefacts and other objects they imbue with meaning. The most common forms of qualitative data are what people have said or done. What is Qualitative Data Analysis? Qualitative Data Analysis (QDA) is the range of processes and procedures whereby we move from the qualitative data that have been collected into some form of explanation, understanding or interpretation of the people and situations we are investigating. QDA is usually based on an interpretative philosophy. The idea is to examine the meaningful and symbolic content of qualitative data. For example, by analysing interview data the researcher may be attempting to identify any or all of: Someone's interpretation of the world, Why they have that point of view, How they came to that view, What they have been doing, How they conveyed their view of their situation, How they identify or classify themselves and others in what they say, The process of QDA usually involves two things, writing and the identification of themes. Writing of some kind is found in almost all forms of QDA. In contrast, some approaches, such as discourse analysis or conversation analysis may not require the identification of themes (see the discussion later on this page). Nevertheless finding themes is part of the overwhelming majority of QDA carried out today. ======================================================================= Qualitative Source: http://votingrights.news21.com/article/movement/ “ A growing number of conservative Republican state legislators worked fervently during the past two years to enact laws requiring voters to show photo identification at the polls. “ Lawmakers proposed 62 photo ID bills in 37 states in the 2011 and 2012 sessions, with multiple bills introduced in some states. Ten states have passed strict photo ID laws since 2008, though several may not be in effect in November because of legal challenges. “ A News21 analysis found that more than half of the 62 bills were sponsored by members or conference attendees of the American Legislative Exchange Council (ALEC), a Washington, D.C.-based, tax-exempt organization. “ ALEC has nearly 2,000 state legislator members who pay $100 in dues every two years. Most of ALEC’s money comes from nonprofits and corporations — from AT&T to Bank of America to Chevron to eBay — which pay thousands of dollars in dues each year. “ I very rarely see a single issue taken up by as many states in such a short period of time as with voter ID,” said Jennie Bowser, senior election policy analyst at the National Conference of State Legislatures, a bipartisan organization that compiles information about state laws. “It’s been a pretty remarkable spread.”
- 4 factors of ALL phenomena, i.e. potential stories http://en.wikipedia.org/wiki/Main_Page Anything can be counted or turned into a measure. Quantitative research refers to the systematic empirical investigation of social phenomena via statistical, mathematical or computational techniques. [1] The objective of quantitative research is to develop and employ mathematical models , theories and/or hypotheses pertaining to phenomena. The process of measurement is central to quantitative research because it provides the fundamental connection between empirical observation and mathematical expression of quantitative relationships. Quantitative data is any data that is in numerical form such as statistics, percentages, etc. [1] In layman's terms, this means that the quantitative researcher asks a specific, narrow question and collects numerical data from participants to answer the question. The researcher analyzes the data with the help of statistics . The researcher is hoping the numbers will yield an unbiased result that can be generalized to some larger population. Qualitative research , on the other hand, asks broad questions and collects word data from participants. The researcher looks for themes and describes the information in themes and patterns exclusive to that set of participants. Qualitative Quantitative Geographic Timeline capsule Challenge to journalists? Having the skills to find, retrieve and an alyze the data to determine which of the three +#4 to emphasize
- 4 factors of ALL phenomena, i.e. potential stories Geostatistics is a branch of statistics focusing on spatial or spatiotemporal datasets . Developed originally to predict probability distributions of ore grades for mining operations, [1] it is currently applied in diverse disciplines including petroleum geology , hydrogeology , hydrology , meteorology , oceanography , geochemistry , geometallurgy , geography , forestry , environmental control , landscape ecology , soil science , and agriculture (esp. in precision farming ). Geostatistics is applied in varied branches of geography , particularly those involving the spread of diseases ( epidemiology ), the practice of commerce and military planning ( logistics ), and the development of efficient spatial networks . Geostatistical algorithms are incorporated in many places, including geographic information systems (GIS) and the R statistical environment .
- 4 factors of ALL phenomena, i.e. potential stories Geostatistics is a branch of statistics focusing on spatial or spatiotemporal datasets . Developed originally to predict probability distributions of ore grades for mining operations, [1] it is currently applied in diverse disciplines including petroleum geology , hydrogeology , hydrology , meteorology , oceanography , geochemistry , geometallurgy , geography , forestry , environmental control , landscape ecology , soil science , and agriculture (esp. in precision farming ). Geostatistics is applied in varied branches of geography , particularly those involving the spread of diseases ( epidemiology ), the practice of commerce and military planning (logistics), and the development of efficient spatial networks. Geostatistical algorithms are incorporated in many places, including geographic information systems (GIS) and the R statistical environment.
- 4 factors of ALL phenomena, i.e. potential stories Qualitative Quantitative Geographic Timeline capsule Challenge to journalists? Having the skills to find, retrieve and an alyze the data to determine which of the three +#4 to emphasize
- A variety of organizations – local and international – driving development of Hardware and Software Data-capture tools the DataIn Analytic and presentation tools the Analysis and Information Out technologies
- DON’T SHOW VIDEO: Just for audience reference Source: http://santafe.edu/news/item/staying-step-ahead-diseases/ Physorg Few people think of flu season as much more than sniffles and sleepless nights. For SFI External Professor Lauren Ancel Meyers, it’s a chance to study how human epidemics develop -- and try to head them off. Working with the Texas Department of State Health Services and a team of University of Texas researchers, Meyers led the development of the Texas Pandemic Flu Toolkit, a web-based service that simulates the spread of pandemic flu through the state, forecasts the number of flu hospitalizations, and determines where and when to place ventilators to minimize fatalities. The toolkit can be used in emergency situations for real-time decision-making. Public health officials might use the forecaster tool to determine when a pandemic might peak and what kind of magnitude they might see in terms of infections and hospitalizations. It might also be used to develop scenarios of probable pandemics and to see how they may impact different locations, age groups, and demographics. Various interventions, such as antivirals, vaccines, and public health announcements, can be input into the forecasts to determine their effect at different stages in the pandemic's evolution. Read the article in Physorg (June 7, 2012) Read the article in the SFI Update (March-April 2012) Watch Meyers describe the toolkit (SFI video presentation, 57 minutes) “ The spread and control of infectious diseases in human populations is an enormously complex system, driven by non-trivial interactions between continually evolving pathogens, diverse host immune systems, and individual and organizational decision-making,” says Meyers. In 2009 she helped track the emerging H1N1 pandemic, and worked with the CDC and other public health agencies to mathematically model the virus’s movement through the population. “ Understanding the dynamics of human contact networks and health-related behavior is critical to making good predictions and designing effective interventions,” she says. Meyers has been developing an approach called contact network epidemiology. In her models, individuals or susceptible populations are represented by nodes, which are connected by edges that represent contacts that can lead to disease transmission. The network models can account for varying social behaviors and varying levels of vulnerability, and can even help reveal the likely efficacies of intervention strategies such as vaccinations, quarantines, and distributing antiviral medications. “ We’re learning a lot about infectious diseases from the growing volumes of data produced by surveillance systems and high throughput laboratory methods,” Meyers says. “Innovative modeling techniques have become indispensable to this interdisciplinary field, as we seek to advance in our understanding of epidemics and improve public health.” Filed in: Research
- Source: http://money.cnn.com/2012/08/03/technology/startups/ingestible-sensor-proteus/index.htm The chip works by being imbedded into a pill. Ingest it at the same time that you take your medication and it will go to work inside you, recording the time you took your dose. It transmits that information through your skin to a stick-on patch, which in turn sends the data to a mobile phone application and any other devices you authorize. The system's goal is to overcome our forgetful impulses, says Andrew Thompson, the CEO and cofounder of Proteus.
- Multiple ways to generate, retrieve and analyze health data Health status precursors [How, when, who lays down the individual – and the community’s – baseline of health status Who sees that data? Status Indicators should we use? Same for all cultures, ages, genders, etc? And how will those metrics be presented [the “InfoOut” aspect]? Numbers, dials, spark lines, fever charts ? Services needed by the individual, family, community? Location for services/patient needs? Face-to-face visit or telemedicine? How to make appointment Follow-up and status?
- Data In Analysis Info Out Process applies to all disciplines/professions Your profession probably won’t have direction or answers about its future Seek other- or trans-disciplinary methods and processes. Example: Esri UC and Special Libraries Assoc meetings No more 8 hr work day. 6 hrs “work,” 2 hrs. teach and learn
- We usually think of making maps by marking things – data points -- on some often pre-determined two-dimensional surface called a map. Or in a more familiar term to geographers, a “Base Map.” That has been the tradition for literally millennia. But today’s technology for capturing data, putting it on a map is changing rapidly, to the point where making a map of our location on a cell phone is essentially instantaneous. At the same time, the disciplines of GEOSCIENCE and GEOSTATISTICS are making it possible to complete an If-Than command that results in more – and often unseen and unanticipated – data that generates yet new maps. This intellectual evolution – and a rapid one at that – has let me to reconsider some of my earlier conclusions about Geography as it can relate to multiple disciplines.