Spatial Analysis Dissemination of Census Data United Nations

Spatial Analysis & Dissemination of Census Data United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial Analysis and Dissemination of Census Data q Outline n The Power of Maps o Introduction and Example n Dynamic Census Atlases o Overview & Example n Spatial Analysis Techniques o Overview & Examples n Digital Geographic Data for Dissemination o Overview & Cost and Benefits n Digital Data Dissemination Strategies and Users o Overview of Users United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial Analysis Techniques q the main use of spatial analysis is for census products and services q Techniques include: buffering, linear interpolation, point pattern analysis, and cartograms, etc. q All offer functionality beyond standard thematic (choropleth) mapping, with many tools now available in both commercial and open-source software programs. United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial Analysis Techniques q Some prevalent forms of spatial analysis especially useful for use with population data include: n Queries n Distance measurements n Transformations o Buffering o point-in-polygon analysis o Polygon overlay analysis United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial Analysis: Query q select features by their attributes: n “find all districts with literacy rates < 60%” q select features by geographic relationships n “find all family planning clinics within this district” q combined attributes/geographic queries n “find all villages within 10 km of a health facility that have high child mortality” Query operations are based on the SQL (Structured Query Language) concept United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Examples: What is at…? Features that meet a set of criteria United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Modeling/Geoprocessing q modeling: identify or predict a process that has created or will create a certain spatial pattern n diffusion: how is the epidemic spreading in the province? interaction: where do people migrate to? what-if scenarios: if the dam is built, how many people will be displaced? United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial relationships q Logical connections between spatial objects represented by points, lines and polygons q e. g. , - point-in-polygon - line-line - polygon-polygon United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial Operations q “adjacent to” q “connected to” q “near to” q “intersects with” q “within” q “overlaps” q etc. United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

“is nearest to” • Point/point • Which family planning clinic is closest to the village? • Point/line • Which road is nearest to the village • Same with other combinations of spatial features United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial Analysis (cont. ) q Buffer: find all settlements that are more than 10 km from a health clinic q Point-in-polygon operations: identify for all villages into which vegetation zone they fall q Polygon overlay: combine administrative records with health district data q Network operations: find the shortest route from village to hospital United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial Analysis Techniques q Queries: n Often this is the first step in an analysis, where one seeks to create a subset of units such as populated places with certain characteristics, allowing the user to check how typical an observation is against other observations n They use a GIS program to answer simple questions posed by the user, with no changes in the database and no new data produced. n An example of a query using geo-coded census data is, select all towns with a population greater than 1, 000 persons. These towns can then have their attributes summarized, for instance, to measure their total fertility rates against smaller towns and villages, then map the results n The term exploratory data analysis refers to investigations of patterns and trends in data using such techniques as querying United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Area delineation q E. g. Interactive determination of school districts with the same number of children in each school grade by aggregating census dissemination areas United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial Analysis Techniques q Distance measurements n Easily done with all GIS programs, using the centroids (or center points) of cities, towns, and villages. n An analysis can be done to select villages located more than a kilometer from a school, clinic, or water source. n These can then be further analyzed using the attribute information for the populated places themselves. United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial Analysis Techniques q Transformations n Methods of spatial analysis that use simple geometric, arithmetic or logical rules to create new datasets n Transformations can include operations that convert raster into vector data, or a stream of GPS coordinates into a route or a boundary n Of all the transformational techniques, buffering is the most well known and important United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial Analysis Techniques q Buffering (transformation) n Involves building a new data layer by identifying all areas that are within a certain specified distance of the original. n Buffering can be performed on points, lines and polygons and can be weighted by attribute values. n Buffering can be used to model travel time, for instance, by creating a “catchment area” around a particular feature such as a school or a clinic. n This provides a measure of accessibility that can be mapped across the extent of a country. United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

“is near to”: Buffer Operations • Point buffer • Affected area around a Hospital • Catchment area of a water source United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

“is near to”: Buffer Operations • Point buffer • Affected area around a polluting facility • Catchment area of a water source United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Buffer Operations • Line buffer • How many people live near the polluted river? • What is the area impacted by highway noise? United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Buffer Operations • Polygon buffer • Area around a reservoir where development should not be permitted United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial Analysis Techniques q point-in-polygon analysis n n Determines whether a point lies inside or outside a polygon. Can be used to compare geo-coded village centroids lying inside and outside hazardous areas such as tropical storm tracks or earthquake zones. q Polygon overlay analysis n n Involves comparison between the locations of two different polygonal data layers. For example, the boundaries of two administrative districts could be compared to troubleshoot errors in the field enumeration process United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

“ is within”: point in polygon • Which of the cholera cases are within the containment area United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Problem: We may have a set of point coordinates representing clusters from a demographic survey and we would like to combine the survey information with data from the census that is available by enumeration areas. Solution: “Point-in-Polygon” operation will identify for each point the EA area into which it falls and will attach the census data to the attribute record of that survey point. United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial aggregation q Example of Spatial aggregation: n fusion of many provinces constituting an economic region United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial Analysis Techniques q Spatial interpolation n A spatial analysis method designed to fill in values that lie between observations n A variety of methods including inverse-distance weighting and kriging are used to estimate the values of unsampled sites n based on Tobler’s first law that all nearby objects are more similar than distant objects n Kriging: interpolation technique for obtaining statistically unbiased estimates of spatial variation of known points such as surface elevations or yield measurements utilizing a set of control points o In kriging, the general properties of a surface are modeled to estimate the missing parts of the surface United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial data transformation: interpolation Example 1: Based on a set of station precipitation surface estimates, we can create a raster surface that shows rainfall in the entire region 13. 5 20. 1 12. 7 15. 9 26. 0 27. 2 24. 5 26. 1 United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Example of linear interpolation creating contours United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial Analysis Techniques q Thiessen polygons n Have the unique property that each polygon contains only one input point (e. g. settlements), and any location within a polygon is closer to its associated point than to the point of any other polygon. Thiessen polygons illustrated United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Areas of influence q Commuting distances: daily commuters flow United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Spatial Analysis Techniques q Cartograms n sometimes used to display census results n The areas of the original polygons are expanded or contracted based on their attribute values such as population size or voting habits United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Modelling: smoothing q Evolution of the population beetwen two censuses United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

Location-allocation q Site selection q Optimal allocation q Multicriteria Analysis United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010

THANK YOU! United Nations Regional Seminar on Census Data Dissemination and Spatial Analysis Nairobi, Kenya, 14 -17 September, 2010