Session 9 Spatial Decision Support Systems (SDSS)

Transcription

1 Session 9 Spatial Decision Support Systems (SDSS) Lead facilitator Dr. Gerard Kelly Learning Objectives The aim of this session is to review and understand the applications of a GIS-based spatial decision support system (SDSS) to locate and map the spatial distribution of confirmed malaria cases, rapidly classify active transmission foci and guide targeted responses in elimination zones. Theory Introduction to GIS Introduction to Spatial Decision Support Systems (SDSS) SDSS for malaria elimination Practical demonstration of SDSS-Solomon Island example Practical Producing an interactive baseline map in QGIS Linking and Mapping Malaria Data Basic Cartography and Exporting Maps Wrap-up of Practical Activities and Questions Independent participant activities using Program Data;

2 Spatial decision support system (SDSS) Introduction to Geographic Information Systems (GIS) Angela Maria Cadavid

3 Outline 1. GIS definition 2. Components 3. Spatial data collection 4. Remote sensing 5. Projections 6. List of free GIS datasets

4 Background GIS applications Malaria elimination phase Surveillance- response: Locate and map the distribution of cases Rapidly classify the transmission foci Explore interactions disease-environment Design, delivery and monitoring of interventions Evidence-based practices Spatially targeted responses

5 Geographic information systems (GIS) Computer-based information system that enable us to capture, store, analyse and display georeferenced data that can be exported to statistical platforms for further analysis.

6 But is GIS about computers only? A GIS is an organised collection of : GIS users GIS data Include 2 types of information -Geographical information: (coordinates or area unit) -Attributes: (qualitative or quantitative) Hardware Software Methods Visualise data Stored in different formats vector raster table Relational geodatabase Query functions (SQL): Overlays, joins, map calculations New products image

7 Geo-referenced data Survey at the household level CoordinateX CoordinateY Serology Age Occupations Negative 17 student Positive 24 cadre Negative 15 student Positive 12 student Negative 13 student Geographical information Attributes

8 Geo-referenced data Number of cases aggregated by township by month Township Month Cases Population Elevation TemperaturePrecipitation Geographical information Attributes

9 But is GIS about computers only? A GIS is an organised collection of : GIS users GIS data Include 2 types of information -Geographical information: (coordinates or area unit) -Attributes: (qualitative or quantitative) Hardware Software Methods Visualise data Stored in different formats vector raster table Relational geodatabase Query functions (SQL): Overlays, joins, map calculations Final products image

10 Examples of GIS Software QGIS is an open source, desktop. Lots of functionality and plugins which allow complex spatial analysis. Large user community so plenty of forum support. ILWIS is an open source, remote sensing and GIS software which integrates image, vector and thematic data. GRASS is an open source, desktop remote sensing package. Very powerful but difficult to use. A GRASS plugin is available in QGIS makes it much simpler. SAGA GIS is a free and open source GIS used for editing spatial data. ArcGIS is licensed system to create, visualize, manage, and analyse spatial data. Supports desktop, (ArcView, ArcEditor and ArcInfo), web, and mobile applications. MapInfo is a licensed desktop GIS software enhanced with many plugins for route analysis, 3D and statistical analysis and geocoding. IDRISI is a commercial GIS and remote sensing software developed for the analysis and display of digital geospatial information. Many others...

11 Spatial data collection for the analysis of malaria risk Types of data Malaria data Vector data Interventions Environment Climate Socio-demographic variables Climate Precipitation, temperature, humidity, sea surface temperature, sea surface height Human dimension Infrastructure, population (poverty, demography density), house, typology, source of drinking water Malaria C Environment Land use, forest cover, water bodies, vegetation, dust, soil, moisture, biodiversity, ph, salinity Vector- parasite ecology Density, diversity, population dynamics Interventions Geographical Reconnaissance (GR), Long lasting insecticidal net (LLIN), indoor residual spraying (IRS), etc.

12 Sources: spatial data collection Disease, vector and intervention data: Sub-national and national surveillance systems (Clinical burden malaria) Prospective surveys (detection of different aspects of malaria transmission): Parasitaemia (microscopy, rapid diagnostic tests, PCR methods)=high-transmission areas Sero-surveys (malaria antibodies)=monitoring in post-elimination period Intervention programmes Environmental and Climate data: Remote sensors Weather stations Administrative division maps and socio-demographic data: Paper maps Aerial photos (map digitisation) Census Surveys

13 Geographically referenced data Vector layer : Administrative division At the Province level - China Raster layer : Land cover data - China

14 Geographically referenced data Vector data Vectors portray real world data as a point, line or polygon. Good at storing discrete data (for example: political boundaries, rivers, lakes, land parcels, and streets). Each point, line, or polygon is called a feature and has a unique ID#. Attribute information can be added to each feature in geographical space. Graphic output is usually more aesthetically pleasing (traditional cartographic representation) Disadvantages: It sometimes needs great amounts of processing power to run a complex analysis. The location of each vertex needs to be stored explicitly. Issues with representation of continuous data.

15 Geographically referenced data Raster Data Rasters portray the real world data as a matrix or a grid of cells. Cell size determines resolution. The geographic location of each cell is implied by its position in the cell matrix Good at storing discrete and continuous data (for example: precipitation, temperature, and elevation). Each cell stores information numerically, usually between and has a geographical location. Disadvantages The cell size determines the resolution at which the data is represented. Cells are squares and most real world information doesn t fit into a perfect square. Linear features difficult to represent. Most output maps from grid-cell systems do not conform to high-quality cartographic needs.

16 Geographically referenced data Formats Vector x,y Raster x y ID Name Value Owner Area 1 water 4 State beach 3 State 50 3 grass 2 State forest 1 Warner 500 Values Name Count 1 forest 10 2 grass 9 3 beach 2 4 water 4

17 Remote Sensing (RS) RS is the collection of information without being in physical contact with the object of study (e.g., aircraft and satellite sensors) Data stored as digital data which can provide indirect estimates of environmental variables Wide rage of spatiotemporal resolutions Spatial resolution 2.5-8km Temporal resolution 15 min - 26 days Satellites: Landsat, NOAA, Terra, Aqua, SPOT

18 Remote Sensing (RS)

19 Coordinate Reference System (CRS) CRS is used to represent the locations of geographic features, imagery, and observations such as GPS locations within a common geographic framework. There are two common types of coordinate systems used in GIS: Geographic coordinate systems Projected coordinate systems Enables geographic datasets to use common locations for integration

20 Free GIS datasets Land cover and vegetation indices USGS Land Cover Institute: Great set of links to almost all land cover datasets. Links here include most of the datasets below, and many more esoteric data such as river observations, aquifers data and ocean colour information. Although the page starts with US data, it continues with data for other continents lower down the page. GLOBCOVER: Global land cover dataset at 300m resolution from the MERIS sensor on the ENVISAT satellite. MODIS Global Land Cover: 1km and 4km resolution global land cover maps derived from MODIS images. MODIS Global vegetation indices : 250m, 500m, 1km, and 5.6km resolution maps derived from MODIS images. UMD GLC: 1km resolution global land cover maps from the University of Maryland created using a classification tree approach from MODIS data. GLC2000: Global land cover map for the year 2000 created from the VEGETATION instrument on the SPOT-4 satellite. Individual continents were classified by local experts, increasing the quality of the classification overall. Global Land Cover by National Mapping Organisations: 1km data of land cover for the globe, with a classification scheme based on the UN FAO LCCS, facilitating easy comparison with other land cover products.

21 Free GIS datasets Population Gridded Population of the World: Includes raw population, population density, both historic, current and predicted. Global Rural-Urban Mapping Project: Based on the above, but includes information on rural and urban population balances. WorldPop: High-resolution, contemporary data on population across Africa, Asia and Central/Southern America. Combines the AfriPop, AmeriPop and AsiaPop projects. Large Urban Areas : Historic, current and future estimates of populations in large urban areas of the world. Global Urban Extent: Maps showing urban extent across the world, at 500m resolution, derived from MODIS images. Requires to author to download.

22 Free GIS datasets Climate WorldClim: Climate data for past, present and predicted future conditions. Includes temperature (min, max, mean) and precipitation. 1km x 1km resolution. MODIS Global Land Surface temperature: 1km and 5.6km resolution land surface temperature maps derived from MODIS images. Food and Agriculture Organization of the United Nations: A wide range of over 300 datasets from various climate models and datasets. NCAR GIS Climate Change Scenarios: Lots of data from the National Centre for Atmospheric Research models, including data used by the IPCC in their reports. Registration required.

23 Free GIS datasets Elevation GDEM: 30m resolution global elevation data derived from ASTER satellite images. SRTM: Approx 90m (3 arc-second) resolution elevation data from the Shuttle Radar Topography Mission for the whole world. WorldClim: Climate data for past, present and predicted future conditions. Includes temperature (min, max, mean) and precipitation. 1km x 1km resolution.

24 Administrative Boundary maps and infrastructure DIVA-GIS Country Data: A collection of data collected from a number of the sources below - includes administrative areas, inland water, roads and railways, elevation, land cover, population and climate. Probably the easiest place to get a simple set of data for a specific country. Natural Earth: Includes countries, disputed areas, first-order admin (departments, states etc), populated places, urban polygons, parks and protected areas and water boundaries. Available at multiple levels of detail. GADM: Global administrative boundaries, with extensive attribute sets. Covers countries and up to four levels of internal administrative boundary (states, departments, counties etc). Food and Agriculture Organization of the United Nations: A wide range of over 300 datasets from various climate models and datasets.

25 Thank you!