‘ Online Consumer Geoinformatics Services’ and Geography 2.0 Video Break: Google Mapplets
Agenda
Application Examples and Resources (Selected Web links for later browsing/ downloading, including copy of this PPT)
Video Break: GIS for Disease Control and Real-time Surveillance (STC)
‘ Show Us A Better Way’ Group Activity, Discussion and Conclusions
Introduction: Location Matters
The concept that location can influence health is a very old one in medicine. As far back as the time of Hippocrates (c. 3rd century BC), physicians observed that certain diseases tend to occur in some places and not others.
Introduction: Location Matters
In fact, different locations on Earth are usually associated with different profiles: physical, biological, environmental, economic, social, cultural and sometimes even spiritual profiles, that do affect and are affected by health, disease and healthcare.
These profiles and associated health and disease conditions may also change with time (the longitudinal or temporal dimension).
Introduction: The Origins of Spatial Analysis
In 1854, a major cholera outbreak in London had already taken nearly six hundred lives when Dr John Snow, using a hand-drawn map, showed that the source of the disease was a contaminated water pump.
Introduction: The Origins of Spatial Analysis
By plotting each known cholera case on a street map of Soho district (where the outbreak took place), Snow could see that the cases occurred almost entirely among those who lived near the Broad Street water pump.
Introduction: The Origins of Spatial Analysis
This pump belonged to the Southwark and Vauxhall Water Company, which drew water polluted with London sewage from the lower Thames River.
The Lambeth Water Company, which had relocated its water source to the upper Thames, escaped the contamination.
Introduction: The Origins of Spatial Analysis
Snow recommended that the handle of this pump be removed, and this simple action stopped the outbreak and proved his theory that cholera is transmitted through contaminated drinking water.
Introduction: The Origins of Spatial Analysis
People could also see on this map that cholera deaths were not confined to the area around a cemetery of plague victims and were thus convinced that the infection was not due to vapours coming from it as they first thought.
This map is a digital recreation of Dr Snow’s hand-drawn map. The 1854 cholera deaths are displayed as small black circles. The grey polygon represents the former burial plot of plague victims. The Broad Street pump (shown in the centre of the map) proved to be the source of contaminated water, just as Snow had hypothesised
Introduction: The Origins of Spatial Analysis
By using a map to examine the geographical (spatial) locations of cholera cases in relation to other features on the map (water pumps and cemetery of plague victims), Snow was actually performing what is now known as spatial analysis .
< Dr John Snow (1813-1858), a legendary figure in the history of public health, epidemiology and anesthesiology
Health Geography
It is very useful and customary to divide the geography of health into two interrelated areas:
The geography of disease , which covers the exploration, description and modelling of the spatio-temporal (space-time) incidence of disease and related environmental phenomena, the detection and analysis of disease clusters and patterns, causality analysis and the generation of new disease hypotheses;
Health Geography
It is very useful and customary to divide the geography of health into two interrelated areas (Cont’d):
The geography of healthcare systems , which deals with the planning, management and delivery of suitable health services (ensuring among other things adequate patient access) after determining healthcare needs of the target community and service catchment zones.
Health Geography
Health geography plays a vital role in public health surveillance, including the design and monitoring of the implementation of health interventions and disease prevention strategies.
Geographical research into healthcare services can also help identifying inequities in health service delivery between classes and regions, and in the efficient allocation and monitoring of scarce healthcare resources.
Health Geography
Examples include allocating healthcare staff by region based on actual needs, and assisting in determining the best location and specifications for new healthcare facilities and in planning extensions to existing ones.
Video Break An old but informative ESRI promotional video introducing geographic information systems (Running Time: 4:52 min. - Source: ESRI, US)
Essentials of Geographic Informatics
Geographic Informatics, also known as geoinformatics or geomatics, is the science and technology of gathering, storing, analysing, interpreting, modelling, distributing and using spatially referenced (georeferenced) information.
It is multidisciplinary by nature. The underpinning technologies comprise a broad range of disciplines, including surveying and mapping, remote sensing, GIS (Geographic Information Systems), and the Global Positioning System (GPS).
These, in turn, draw from a wide variety of other fields and technologies, including computational geometry, computer graphics, digital image processing, multimedia and virtual reality, computer-aided design (CAD), database management systems (DBMS), spatio-temporal statistics, artificial intelligence, communications and Internet technologies amongst others.
Essentials of Geographic Informatics
Likewise, the specific applications of geographic informatics draw on a broad range of additional disciplines like the different fields of medicine, public health, and environmental sciences among many others in the case of health and healthcare-related applications.
Essentials of Geographic Informatics
Essentials of Geographic Informatics—GIS
The US FGDC* defines GIS as “computer systems for the input, storage, maintenance, management, retrieval, analysis, synthesis, and output of geographic or location-based information. In the most restrictive usage, GIS refer only to hardware and software. In common usage (by organisations), they include hardware, software, and data. For some, GIS also imply the people and procedures involved in GIS operation.”
* Federal Geographic Data Committee
Essentials of Geographic Informatics—GIS
GIS favours an interdisciplinary approach to the solution of problems.
Going beyond conventional spreadsheet and database tables, it helps us discover and visualise new data patterns and relationships that would have otherwise remained invisible.
Essentials of Geographic Informatics—GIS
GIS achieves this through its unique way of classifying multifaceted, real-world data coming from disparate sources into map layers (coverages or themes), each covering a single aspect of reality, then linking these layers by spatially matching them, and querying and analysing them together to produce new information and hypotheses.
This can be considered one form of data-mining, and is especially useful in the context of aggregated health records.
It is possible, for example, to overlay and integrate the following data to perform different types of health-related analyses:
population data, e.g., census, socio-economic data/geodemographics and lifestyle data;
environmental and ecological data, e.g., monitored data on pollution and vegetation (satellite pictures);
topography, hydrology and climate data;
land-use and public infrastructure data, e.g., schools and main drinking water supply;
Essentials of Geographic Informatics—GIS
It is possible, for example, to overlay and integrate the following data to perform different types of health-related analyses (Cont’d):
transportation networks (access routes) data, e.g., roads and railways;
health infrastructure and epidemiological data, e.g., data on mortality, morbidity, disease distribution, and healthcare facilities and workforce; and
other data as needed to perform different types of health-related analyses.
Essentials of Geographic Informatics—GIS
As a modelling and decision support tool, GIS can help determining the geographical distribution and variation of diseases (e.g., prevalence, incidence) and associated factors, analysing spatial and longitudinal trends, mapping populations at risk and stratifying risk factors.
Essentials of Geographic Informatics—GIS
GIS can also assist in assessing resource allocation and accessibility (health services, schools, water points), planning and targeting interventions, including simulating (predicting) many “what-if” scenarios before implementing them, forecasting epidemics, and monitoring diseases (surveillance) and interventions over time.
GIS provides a range of extrapolation techniques, for example, to extrapolate sentinel site surveillance to unsampled regions.
Other important GIS applications include routing functions and emergency dispatch systems.
Essentials of Geographic Informatics—GIS
GIS and Data Confidentiality Issues
Confidentiality constraints often preclude the release of disaggregate data about individuals, which limits the types and accuracy of the results of geographical health analyses that could be done.
Access to individually geocoded (disaggregate) data often involves lengthy and cumbersome procedures through review boards and committees for approval (and sometimes is not possible).
GIS Data Confidentiality-preserving Solutions
The use of statistical and epidemiological methods to mask the geographic location of data in a way that can still permit meaningful analysis, e.g., special types of spatial and temporal aggregation of data like aggregation of individuals’ point data to whole administrative regions; and
GIS Data Confidentiality-preserving Solutions
The creation of secure (networked) physical environments with limited and multiple levels of access (to confidential data) in which public health researchers can be carefully monitored to ensure protection of individual and household confidentiality.
These solutions either lack flexibility or yield less than optimal results (because of confidentiality-preserving changes they introduce to disaggregate data), or both.
GIS Data Confidentiality-preserving Solutions
A better solution has been proposed based on software agents with the potential of providing flexible, controlled (software-only) access to unmodified confidential disaggregate data and returning only results that do not expose any person-identifiable details (Boulos et al., 2006).
GIS Data Confidentiality-preserving Solutions
Individuals’ point data are not morphed, distorted, masked or aggregated in any way and only software agents (not any humans) are able to see the disaggregate data.
The solution is thus appropriate for micro-scale geographical analyses where no person-identifiable details are required in the final results (i.e., only aggregate results are needed).
Tomlinson’s 10-stage GIS Planning Methodology Free first module of ‘Planning for a GIS’ by Roger Tomlinson: http://campus.esri.com/acb2000/showdetl.cfm ?DID=6&Product_ID=574 Needs Assessment Conceptual Design Physical Design Implementation
GIS-related Technologies: Remote Sensing
In 1970, in an article titled “New eyes for epidemiologists: aerial photography and other remote sensing techniques”, Cline predicted that remote sensing (RS) will be used in detecting and monitoring disease outbreaks; this proved correct in the following years.
Remote sensing is gathering geographical data from above, usually by aircraft or satellite sensors.
GIS-related Technologies: Remote Sensing
It is a major source of GIS data and can rapidly cover large areas of the Earth with relatively low cost per ground unit.
Moreover, additional data from parts of the electromagnetic energy spectrum that are not visible to the human eye can provide very useful information that would have otherwise remained unknown.
GIS-related Technologies: Remote Sensing
For example, thermal infrared sensors pick up subtle temperature differences and display them on film or electronic devices. This is useful in thermal pollution monitoring, allowing industrial effluence to be analysed in terms of heat characteristics.
GIS-related Technologies: The Global Positioning System
The (US) Global Positioning System (GPS) consists of 24 Earth-orbiting satellites that transmit signals to special receivers on the ground, either hand-held units or more sophisticated vehicle-mounted and stationary equipment, for accurate determination of positional co-ordinates.
Some receivers can also display digital maps, and plot the positional co-ordinates on them.
GIS-related Technologies: The Global Positioning System
GPS can also provide data on elevation, velocity (while moving) and time of measurement.
Ground crew workers use GPS in collecting accurately positioned (georeferenced) field data to create and update GIS coverages.
Other positioning systems exist, e.g., Galileo (EU; satellite nav) and non-satellite-based positioning technology.
GIS-related Technologies: The Global Positioning System
GIS-related Technologies: The Global Positioning System
GPS technology is also used to dispatch police cars, ambulances and fire fighters in emergency situations.
Ground emergency units receive signals from GPS receivers mounted in moving emergency vehicles to determine, track and guide the vehicle nearest to an emergency.
GIS-related Technologies: The Global Positioning System
GPS can be also combined with real-time GIS to ensure efficient routing of ambulance trips by finding the shortest and quickest routes, and avoiding routes with traffic congestion (based on live traffic maps). This can dramatically reduce the response time in emergency situations and help saving more lives.
GIS-related Technologies: The Global Positioning System
Furthermore, FCC rules (Federal Communications Commission - http://www.fcc.gov/911/enhanced/ ) have accelerated the incorporation of GPS receivers into mobile phones like the Nokia N95, thus helping ambulance or rescue teams to precisely and quickly locate and track people who are in a medical emergency, injured or lost but cannot give their precise location.
Internet GIS
With the advent of Internet GIS and mapping, GIS information products (output) have become accessible to a much wider audience of decision makers who have no GIS skills (they just need to be able to use any standard Web browser).
Internet GIS applications include:
Those that help identifying services (including routing applications), or accessing health and related data sets based on location; and
Internet GIS applications include (Cont’d):
Those enabling the integration ( mashing-up ) and interactive visualisation of health indicators/ outcomes, services and statistics from multiple sources through thematic mapping and other visualisation techniques.
Online Consumer Geoinformatics Services and Geography 2.0
GIS have always shared many of the foundational ethea (plural of ethos) of Web 2.0, (even before the latter became known as a distinct entity), namely data sharing, remixing and repurposing, and collaboration.
GIS enable remixing and repurposing of data by “mashing-up” various data and map layers or themes from multiple sources into one study/map (with multiple layers covering same locations superimposed like onion's skin).
Geography 2.0 – Cont’d
Now with the advent of Web 2.0 technologies, the democratisation and participatory nature of GIS have never been more possible or powerful.
Free online consumer geoinformatics services (a term coined by Boulos, 2005) and geo-data provided by Google, MSN (Microsoft), Yahoo! and others, coupled with easy-to-use mash-up technologies and APIs (Application Programming Interfaces) from the same providers have unleashed GIS for use by the masses.
Free base geodata, including satellite imagery, for the masses!
Geography 2.0 – Cont’d
Yahoo! Pipes and Microsoft Popfly, for example, let non-programmers create geo-mashups and novel map visualisations without the need to programme, combining data feeds and filters with almost Lego-like simplicity.
Google KML (Keyhole Markup Language) is an XML-based language and file format used to display geographic data feeds in an Earth browser such as Google Earth, Google Maps, and Google Maps for mobile.
Geography 2.0 – Cont’d
With Google Mapplets, anyone can tap into, remix and reuse third-party mini-applications for Google Maps (known as Mapplets) from a rapidly expanding catalogue maintained by Google, to create and share very powerful personal maps.
Video Break Google Mapplets (Running Time: 1:16 min. - Source: Google, US)
Geography 2.0 – Cont’d
Today, many online mapping applications exist where people can even annotate and add their own individual data to a shared Web map, e.g., ‘Who is Sick?’ http://whoissick.org/sickness/
Google Earth has become like a layered 3-D Wikipedia of the planet that anyone can edit and add text/markers and rich media (photos, videos, 3-D models) to it, what Boulos (2005) calls the ‘ultimate “wikification” of maps and GIS’.
Geography 2.0 – Cont’d
And not just this, but also now users can dynamically overlay on similarly shared maps their own current position on Earth, and also view the position of others who have likewise shared their position, all in *real-time* over the Web, if they have, for example, a low-cost USB GPS mouse receiver or similar device connected to their PC or built into their mobile gadgets.
Geography 2.0 – Cont’d
GPS-enabled mobile phones and GPS-enabled cameras are enabling millions of people every day to collectively annotate the Earth in ways never done before besides opening up many mobile location-based service possibilities and opportunities!
The Nokia N96 GPS- and Internet-enabled Multimedia Computer phone >>
Health-related Application Examples of Geographic Informatics Video Break: GIS for disease control and real-time surveillance (Running Time: 9:00 min. - Source: Scientific Technologies Corp., US) http://www.ij-healthgeographics.com/ http://www.health-geomatics.co.nr/ More examples resources, papers and a copy of this PPT: http://tinyurl.com/3t595u
Activity and Discussion
The UK government has recently launched a data mashup competition to find innovative ways of using the masses of data it collects: http://www.showusabetterway.co.uk/call/
The government is hoping to find new uses for public information in the areas of criminal justice, health and education, and is opening up gigabytes of information for this purpose from a variety of sources.
Activity and Discussion
The sources include mapping information from Britain’s Ordnance Survey, medical information from the NHS, and neighbour-hood statistics from the Office for National Statistics. (None of the data is personal information.)
Activity and Discussion
Now visit the ‘Examples’ and ‘All your ideas’ sections at http://www.showusabetterway.co.uk/call/ (there are already some health-related ideas submitted to the site) and try to come up with your own geo-mashup ideas (you have 30 minutes for this). Who knows, you may even end up winning £20,000!
Conclusions
Understanding the relationship between location and health can greatly assist us in understanding, controlling and preventing disease, and in better healthcare planning, with more efficient and effective resource utilisation. This should ultimately lead to better healthcare outcomes and improved health for everyone.
Conclusions
However, for geographic informatics to become one day a mainstream technology in the health sector like today’s spreadsheet and database packages, we still need to combat many data availability/quality barriers, as well as cultural and organisational barriers, including “spatial illiteracy” among healthcare workers, while making the tools cheaper and much easier to learn and use.
Professional education and hands-on training courses in geographic informatics are extremely important in achieving this goal.
Examples of Health and Healthcare Applications of Geographic Informatics Maged N Kamel Boulos E-mail: [email_address]
The Bio/Medical/Health/Public Health Informatics Continuum
Applications Using Remote Sensing for Data Acquisition
Since 1985, CHAART (Centre for Health Applications of Aerospace Related Technologies, US - http://web.archive.org/web/20060109083802/http://geo.arc.nasa.gov/esdstaff/health/chaart.html ) has been involved in a number of projects on the application of RS and GIS technology to human health problems.
Among these projects was a study of the spatial patterns of filariasis in the Nile Delta, Egypt, and prediction of villages at risk for filariasis transmission in the Nile Delta. Landsat Thematic Mapper data coinciding with epidemiological field data were converted into vegetation and moisture indices and classified into land-cover types. Statistical analyses were used to correlate these land-cover variables with the spatial distribution of microfilaria in 201 villages.
Applications Using Remote Sensing for Data Acquisition
Another study investigated Lyme disease in Westchester County, New York, US to develop a satellite remote sensing/GIS model for prediction of Lyme disease risk, which can help public health workers in their efforts to reduce disease incidence.
Similarly, a third study of schistosomiasis in China aimed at developing a hydrological model that could be used to identify risk factors for disease transmission.
CHAART has also been involved in two malaria surveillance projects carried in California, US and Chiapas, Mexico as part of NASA’s Global Monitoring and Human Health programme. The field research focused on the relationship of Anopheles mosquito to environmental variables associated with regional landscape elements, including larval habitats (flooded pastures and transitional wetlands), blood-meal sources (cattle in pastures) and resting sites (trees). The remote sensing research involved identifying and mapping these and other landscape elements using multi-temporal Landsat Thematic Mapper data.
Applications Using Remote Sensing for Data Acquisition
Left: Landsat TM images of Mexico Coastal Plain from July 1991 showing the wet season, and the landscape is mostly green. Right: Landsat TM images of the same Mexico Coastal Plain from March 1992. In the spring season, much of this area is dry and is purple in this image (right)
Applications Using Remote Sensing for Data Acquisition
The MALSAT (Environmental Information Systems for Malaria - http://web.archive.org/web/20030801193813/http://www.liv.ac.uk/lstm/malsat.html ) team is another group of researchers, based at the Liverpool School of Tropical Medicine, UK, who are investigating the eco-epidemiology of vector-borne diseases, including malaria in sub-Saharan Africa, using GIS and RS.
Studies in The Gambia have demonstrated how satellite-derived data can be used to explain variation in malaria transmission, while the value of such data in predicting malaria epidemics is being examined in other parts of Africa.
The group is now involved in another project titled “Forecasting meningitis epidemics in Africa” to develop a climate-driven model for predicting outbreaks of meningococcal meningitis in Africa.
Applications Using GPS for Data Acquisition
In Kenya, researchers from the Division of Parasitic Diseases of the Centres for Disease Control and Prevention (CDC, Atlanta, Georgia, US) work with the Kenya Medical Research Institute to study malaria and means of preventing it.
These researchers use GPS to collect positions and data in the field, and then edit and analyse this data in GIS.
One study region had its last map made in the late 1960s, and researchers needed an updated map for their study. GPS helped them update the old map features to reflect the current status of the land.
Applications Using GPS for Data Acquisition
The GPS mapping team hired local fishermen to row them in small fishing boats to map the shore of the lake. Roads were mapped by driving cars along them while a team member captured location data with GPS. Once they had an updated map of the region, they could begin using their GIS and create maps to help them in their malaria studies.
Examples of Health/Public Health Applications
WHO (World Health Organisation) GIS Programmes
HealthMap ( http://www.who.int/csr/mapping/en/ ) is a joint WHO/UNICEF GIS Programme that was initially created in 1993 to provide GIS support for the management and monitoring of the Guinea Worm Eradication Programme. But since 1995, the scope of the work has been expanded to cover other disease control and public health programmes.
The HealthMap project has successfully contributed to the surveillance, control, prevention and eradication of many communicable diseases, including Guinea worm, onchocerciasis, lymphatic filariasis, malaria, schistosomiasis, intestinal parasites, blinding trachoma and HIV.
The programme has developed its own HealthMapper application ( http://www.who.int/health_mapping/tools/healthmapper/en/ ) and is providing it at no cost to developing countries. This is a database management and mapping system that simplifies the collection, storage, retrieval, management, spatial and statistical analyses, and visualisation of public health data through its user-friendly interface.
WHO (World Health Organisation) GIS Programmes
WHO (World Health Organisation) GIS Programmes
The WHO is also using GIS technology in its Leprosy Elimination Programme (LEP - http://web.archive.org/web/20050207172746/http://www.who.int/lep/Monitoring_and_Evaluation/gis.htm ).
The WHO Regional Office for the Americas (PAHO - Pan American Health Organisation - http://www.paho.org/english/sha/SHASIG.htm ) has developed its own GIS in Health project for the Americas (SIG-EPI).
GIS in Malaria: The MARA/ARMA Initiative
The MARA/ARMA collaboration (Mapping Malaria Risk in Africa / Atlas du Risque de la Malaria en Afrique ) is funded by the International Development Research Centre of Canada (IDRC), the South African Medical Research Council (SAMRC), the UK Wellcome Trust, the Swiss Tropical Institute and the UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (TDR).
MARA/ARMA aims at providing a GIS atlas of malaria risk for Africa, by integrating spatial environmental and malaria datasets to produce maps of the type and severity of malaria transmission in different regions of the continent.
The project attempts to define malaria risk categories (environmental strata) in terms of non-malaria data, e.g., environmental and climatic data, and to develop a mask layer of factors that exclude malaria (a no-risk category), e.g., absence of population, high altitude, deserts, etc.
Areas of no data are highlighted during the course of the project with the possibility of using geographical modelling to extrapolate to such no-data areas, based on the defined environmental stratification rules.
http://www.mara.org.za
By spatially defining the African continent into regions of similar type and severity of malaria transmission, appropriate control measures can be tailored to each region according to its needs, thus maximising the potential and outcomes of available control resources (human, financial and technical).
The MARA/ARMA maps should be of great value to research on malaria transmission dynamics.
MARA/ARMA can also serve as a model for the study and control of other diseases, and all non-malaria-specific information gathered during the course of the project can be reused in a similar manner.
HealthQuery: An Example of a Healthcare Services/Access Application
HealthQuery ( http://web.archive.org/web/20040614192746/http://www.healthquery.org/ ) is a collection of Web-based public domain tools designed to assist California residents and health organisations in making more informed health decisions. (Now discontinued, as there are many better online services for the same purpose, but still mentioned here as a classic basic example.)
It is a collaborative project of many US organisations and end-users including the Good Hope Medical Foundation, California Department of Health Services — Centre for Health Statistics, the National Health Foundation (NHF), a Los Angeles-based, public benefit organisation, and three companies: ESRI, Oracle and Sun Microsystems.
The included Health Facility Finder tool allows users to locate the hospitals, clinics and emergency rooms that are nearest to them (within a user-defined radius) and provides them with detailed driving directions from their current locations to matching facilities.
HealthQuery also has plans to develop other tools to model and simulate the supply and demand for healthcare services into the future and allow users to compare the current supply and demand for these services.
HealthQuery: An Example of a Healthcare Services/Access Application
In this screenshot, we searched for the nearest hospitals within a 5-mile radius around 92373 (Zip code, CA, US). HealthQuery found 4 locations.
HealthQuery: An Example of a Healthcare Services/Access Application
In this screenshot, we asked HealthQuery to give us detailed driving directions from near 92373 (Zip code, CA, US) to one of the facilities located in the previous figure (Redlands Community Hospital).
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