ENV508 APPLIED GIS

PROJECT S1 2015

GIS analysis of data for public health analysis in West Timor

Background

Eastern Indonesia faces the challenges of providing adequate and equitable health services to a largely remote, rural population. Health in the eastern Indonesian province of Nusa Tenggara Timur (NTT) is generally poor, with high incidence of malaria (WHO high infant mortality rate (54/1000, compared with 44/1000 nationally), and child malnutrition averaging 39% and reaching 50% in some areas. GIS provides a powerful tool for visualising and analysing public health data. You can find more information about GIS and health applications at these websites:

Project aims

The aims of this project are to create a GIS for the district of Timor Tenggah Selatan in West Timor and to explore the datasets contained within it to investigate health service delivery and maternal health indicators. In addition you will explore one case study area and quantify the influence of environment on selected health indicators.Using your analysis and results produce a scientific research paper addressing the problems and issues and discuss your results.

Your research paper should include:

  • Abstract
  • Introduction: objectives of the work, background
  • Methods: study area, data and available resources, any preparation of the data, your geo-processing and analysis techniques
  • Results: using relevant maps, tables, charts, and results of queries to illustrate your findings
  • Discussion: Discussion of the results and approach including any limitations of the study
  • Conclusions
  • References

Figures, Tables, and Appendices should be included as appropriate.

For assistance and a guide for the suggested format of the research paper, see those specified by the International Journal of Health Geographics:

DATASETS

ArcGIS 10.2 in the GIS lab or student licence of ArcGIS 10.2

Datasets that you are provided with include:

Vector Data

  • ne_10m_admin_0_countries.shp(counties of the world – WGS84)

Downloaded from (

  • Province_indo.shp (Provinces in Indonesia - WGS84).
    From Bakosurtanal Indonesian mapping agency.
  • NTT_Districts.shp/ NTT_Districts.lyr (Districts in Nusa Tenngara Timor – UTM)
    Attributed with data from NTT in Figures (NTT Dalam angka) (
  • TTS_District.shp/ TTS_District.lyr (Subdistrict boundaries in Timor Tenggah Selatan District – UTM)
    Provided by local TTS planning department and attributed with data provided by the local health department
  • Village_office.shp (Village office locations - UTM)
    Provided by local TTS planning department
  • TTS_Village.shp (Village boundaries in Timor Tengga Selatan District – UTM)
    Provided by local TTS planning department
  • Linamnutu_HH.shp (House Hold Location Shape file for Linamnutu Village – UTM. (This data has been fabricated for this tutorial)
  • Linamnutu_Health_Interview.xls (Excel file of household interview results.
    (This data has been fabricated for this tutorial)
  • Well Location.csv (Location of village drinking water wells.
    (This data has been fabricated for this tutorial)

Raster Data

  • DEM.TIF (Digital Elevation model for the area around Linamnutu village)

This data is from the Shuttle Radar Topographic Mission

  • ALOS.TIF (High Resolution ALOS satellite imagery for Linamnutu Village)

Obtained from the Japanese space agency JAXA.

  • LANDSAT.tif (Landsat 5 (2006/9/3) imagery for Linamnutu Village)
    This data was obtained from the NASA/USGS Glovis website (

You should describe these data more fully in your project report. Include both what you know and don’t know about the data. Try to construct some metadata. This is real data (apart from the fabricated data clearly indicated) so sometimes you find it like this – without much metadata.

STEPS IN ANALYSIS

Phase 1: Create Background maps

In ArcMap, open the countires of the world data shapefile and the Province_indo.shp. Make a map showing the location of the province of Nusa Tenggara Timor in relation to North Australia. How far is Darwin from Timor?

Open NTT provincial shape file (attributed with population and health staffing data) and create thematic maps of:

  • Population distribution
  • Population density
  • Using map calculator
  • A poverty map
  • Distribution of Health staff
  • Distribution of number of doctors and midwives per/person
  • You will need to create a new field in the attribute table and used the Field Calculator.

(TIP: Use the Layer file to see field alias description. You will need to re-link the layer file to the .shp file)

Phase 2: Examine access to Maternal health services in on district.

Open the Timor Tenngah Selatan district shape file attributed with maternal health data.

  • Map the proportion of births attended by trained health workers compared to the number of maternal and neonatal deaths. (Use the layer file to see field alias’s)

Phase 3: The effect of geography and environment of public health.

Open the TTS village shape file

  • Use a query to find the village named Linamnutu. Zoom to the village area.

Open the Household location (LinamnutuHH.shp) file.

Open the House hold interview data excel file.

  • Join the household interview data with the new household location point data.

Open the Landsat satellite image, the ALOS high resolution imagery and the Digital elevation model for this region from the ‘Raster’ data folder. Use these image data-sets to identify different geographic/landuse regions

  • Map the occurrence of malaria and diarrhoea. How is it spatially distributed?
  • Create a new ‘geographic region’ polygon layer. In its attribute table create a new (text) field ‘GEO_TYPE’.
  • Manually digitize polygons around the lowland irrigation area and the uplands using the satellite imagery and digital elevation raster layers as a guide.
  • Attribute each polygon with its geographic type (ie ‘upland’, ‘irrigation’).
  • Intersect the geographic attribute with the household data.
  • Export the resulting attribute data to Excel.
  • Create graphs to show where there may be correlations between where people live and health.

Open the well location shapefile.

  • Buffer the well location points by 250 meters.
  • Intersect with the household location data.
  • Report on any correlations between well locations and public health indicators.

What correlations between location and health indicators can you see? What might be causing them?

Phase 4:Further examination of access to maternal health services

Open the village office shape file attributed with the presence and absence of midwives

  • Display the presence or absence of midwives and compare it to proportional attendance of trained health workers.
  • Run a query and select those villages with midwives and create a new shape file.
  • Buffer this shape file by 3km to see regions remote from health care. Remember many people have no access to transport so often women in labour are carried to health centres or to main roads where public transport may be available.

Load the Roads shape file

  • Buffer this road data by 1km.
  • Display the road buffer with the location of midwives buffer to identify areas with poor access to maternal health services.

Results

Present the finding of your analysis using maps, charts, tables and reports as appropriate

Discussion

Evaluate the results. Include a section in the discussion on the issues and potential problems with the data and approaches used.

ASSESSMENT

This project carries 55% of the final assessment for this unit. See details of the marking sheet for allocation of marks within this piece of assessment.

The project report should be submitted in DIGITAL format (submitted via Learnline (preferred option), e-mailed, or put on CD and submitted to lecturer/tutor) by Friday 12 June, 2015.

Additional references.

Fisher, Rohan P., and Bronwyn A. Myers. "Free and simple GIS as appropriate for health mapping in a low resource setting: a case study in eastern Indonesia."Int J Health Geogr10 (2011): 15.

Belton, Suzanne, Bronwyn Myers, and Frederika Rambu Ngana. "Maternal deaths in eastern Indonesia: 20 years and still walking: an ethnographic study."BMC pregnancy and childbirth14.1 (2014): 39.

Myers, Bronwyn, Sam Pickering, and Vidyahwati Tenrisanna. "Food security of households with access to subsidized rice in west Timor where maize is the traditional staple."Food Security: 1-11.

Rambu Ngana, Frederika, Bronwyn A. Myers, and Suzanne Belton. "Health reporting system in two subdistricts in eastern Indonesia: Highlighting the role of village midwives."Midwifery28.6 (2012): 809-815.

CWS-CARE-HKI, Church World Service – CARE International Indonesia – Helen Keller International. (2008). Nutrition and Health Survey of Underfive Children and Women in West Timor 2007: Need for Immediate Action in West Timor. Jakarta, Indonesia: CWS/CARE/HKI

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