GEO-SPATIAL INFORMATION SYSTEMS Miami, May 2002

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

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.)

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

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

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

Source: Global Positioning System: Jay Hyoun Kwon Department of Civil & Environmental Engineering & Geodetic Science The Ohio State University

Settlements Admin. Units Reference Grid Rivers Longitude Latitude 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 Geographic Information Systems

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

GIS Data

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

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

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

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

Random samples Every location is equally likely to be chosen

Systematic samples Sample points are spaced at regular intervals

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

Stratified samples

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

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

Secondary data Meta-data often absent This leads to –Misinterpretation –Misuse –False perception of accuracy

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

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

Topographic Map

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

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

Socioeconomic data Referenced by - Administrative units - Settlements / villages - Individual houses or facilities and/or other geographic objects (lat/long)

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

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

HEALTH FACILITIES FOR DISASTER RESPONSE IN COLOMBIA

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. Background

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.

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.

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) Database organization

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

Example of the Database Organization…

Examples of the Data Layouts

General Layout of Health Care Facilities Distribution in Colombia

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

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

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.