GEO-SPATIAL INFORMATION SYSTEMS Miami, May 2002

1. GEO-SPATIAL INFORMATION SYSTEMS Miami, May 2002

2. GEO-SPATIAL INFORMATION SYSTEMS • Computer-based systems to integrate spatial data in order to produce maps, graphics and/or reports • The integration of Remote Sensing –based information, GPS, and GIS is commonly referred to as Geo-Spatial Information Systems

3. Remote Sensing “is the measurement or acquisition of information of some property of an object or phenomena by a recording device that is not in physical or intimate contact with the object or phenomena under study” • includes aircraft, spacecraft and satellite based systems • products can be analog (e.g., photos) or digital images • remotely sensed images need to be interpreted to yield thematic information (roads, crop lands, etc.)

4. Principles • sensors measure the amount of energy reflected from the earth’s surface • based on energy sources and radiation principles • different sensors measure different parts of the electromagnetic spectrum

5. Remote sensing systems • satellite images • aerial photography • aerial video • visible light • using off-the-shelf video cameras and post-processing systems • cheap, rapid data collection for monitoring and data capture

6. VISIBLE LIGHT • optical systems use photographic film or electro-optical scanners • aerial photography based on cameras on aircraft • satellite-based systems AERIAL PHOTOGRAPHY • important for updating large scale topographic maps (e.g., new roads, urban areas) • stereo-effect: pairs of images that are displaced produce 3-D effect • allows for measuring elevation

7. Source: Global Positioning System:http://scour.myongji.ac.kr/~kwon/tsld001.htm Jay Hyoun Kwon Department of Civil & Environmental Engineering & Geodetic Science The Ohio State University

8. Rivers + + Settlements + + + Admin. Units Reference Grid Latitude Longitude Geographic Information Systems • GIS is central in the process of integration of spatial data • GIS is the tool by which spatial data can be transformed and be integrated in order to be represented as maps, graphics and/or other data representation formats

9. Remote sensing and GIS • remotely sensed data is an important data source (currency, frequency) • large scale: e.g., “cities revealed” • medium scale: framework data, urban/non-urban, crop conditions, etc. • small scale: global landcover data sets • requires considerable processing to achieve high accuracy products • image interpretation guided by GIS data

10. GIS Data

11. GIS data • Obtaining data is an important part of any GIS project • You need to know • What types of data you can use with GIS • How to evaluate it • Where to find it • And how to create it yourself

12. Data Sources • Two types of data sources • Primary data • Data measured directly by surveys, field data collection, remote sensing • Secondary data • Data obtained from existing maps, tables or other data sources

13. Primary data • We cannot usually observe the spatial distribution of a variable throughout the study area • Therefore we need to sample: • Take measurements of a subset of the features in the area that best captures the actual spatial variation

14. Sampling • The sampling density determines the resolution of the data • Samples taken at 1 km intervals will miss variation smaller than 1 km • Standard approaches to sampling: • Random • Systematic • Stratified

15. Random samples • Every location is equally likely to be chosen

16. Systematic samples • Sample points are spaced at regular intervals

17. Stratified samples • Requires knowledge about distinct, spatially defined sub-populations (spatial subsets such as ecological zones) • More sample points are chosen in areas where higher variability is expected

18. Stratified samples

19. Secondary data • More and more ready-made digital GIS data sets become available • Government agencies: census geography • Topographic surveys • Private companies

20. Secondary data • Meta-data: “data about the data” • Procedures used to collect or compile the data • Data lineage • Accuracy and measurement standards • Coding schemes • Required for both spatial and attribute data

21. Secondary data • Meta-data often absent • This leads to • Misinterpretation • Misuse • False perception of accuracy

22. Data sources for Disaster Prevention and Response • Framework data (base data) • Socioeconomic data • Environmental data (geo-physical data) • Infrastructure (transportation and utilities) • Healthcare facilities • Shelters

23. Framework data • Reference data to provide context for other data • Roads, rivers, elevation contours • Topographic survey, ordnance survey

24. Topographic Map

25. Framework data • Digital Chart of the World (DCW) • Largest scale consistent digital data set for the whole world (1:1 million) • Designed for air navigation -> not necessarily appropriate for other uses

26. Socioeconomic data • Data about humans, human activities, and the space and/or structures to conduct human activities • Demographic data • Migration • Housing • Transportation • Economic activity

27. 302 305 304 303 306 154 156 155 157 158 159 160 Socioeconomic data • Referenced by- Administrative units- Settlements / villages- Individual houses or facilities and/or other geographic objects (lat/long)

28. Data input • Data input involves digital encoding of both geographic and attribute data • For attribute data: • Spreadsheets • Database management systems • For geographic data: • Coordinate entry • Digitizing • Scanning

29. Data input • Conversion of hardcopy to digital maps is the most time-consuming task in GIS • Up to 80% of project costs • Labor intensive, tedious and error-prone • Database development sometimes becomes an end in itself

30. HEALTH FACILITIES FOR DISASTER RESPONSE IN COLOMBIA

31. Background CDMHA has developed a GIS to assess disaster response medical capability in Colombia. The system will serve as a model for further development of similar databases in other countries within CDMHA’s geographic area of operation. A total of 1466 health-related institutions were contacted and surveyed throughout Colombia, of which 655 met the basic criteria of (a) having a minimum of ten beds; and (b) having capacity of performing basic emergency health services. Those meeting the criteria were included in the database.

32. The initial list of healthcare facilities and contact information was obtained from Ministry of Health and/or the National Institute of Health, institutions responsible for evaluating, certifying and maintaining records of authorized facilities. A telephone-based survey was implemented to collect data from 70% of the institutions. The remaining 30% responded the survey via fax.

33. Determination of Georeferences Several methods were used to determine geo-references for each healthcare facility. Based on location of facilities, geographic coordinates for each facility range from those on the exact site for which GPS devises were used to record Lat/Lon position --as is the case for the facilities in large urban centers– to those which their geo-references correspond to the geographic center of the small town where they are located, as is the case for most facilities in rural/semi-rural spaces. Cartographic material from the Instituto Geografico Agustin Codazzi and that from local municipal cartographic databases, were the source for a few cases.

34. Database organization • The database is organized within six broad categories: • Basic Information • Evacuation Capability • Medical Services • Ancillary Services • Support Services • Personnel (Human Resources) In total, the dataset is made of 84 data attributes (fields)

35. Example of the Database Organization and Data Components (Continues on next page…)

36. Example of the Database Organization…

37. Examples of the Data Layouts

38. General Layout of Health Care Facilities Distribution in Colombia

39. Query: Only Healthcare Facilities within 1 to 20 Miles Range from an Airport

40. Proximity of Healthcare Facilities to Airports in the Bogota Region (10 Mile Ranges)

41. Comments There are an important number of institutions in the country, but the available information is outdated and scarce, and this does not permit the adequate assessment of the capacity of these institutions in case of an emergency. The presented database will be an important and initial step in the assessment of the health system and its capacity to respond to emergencies.