Spatial Analysis

anal00.gif - 4068,0 K A GIS is essentially based on query operations and geographic data manipulation. That is why Geoprocessing is different from other technologies such as Automated Cartography and Computer Aided Projects: spatial (geographic) analysis functions use spatial and non-spatial attributes of geographic entities stored on spatial databases and simulate real world phenomena, aspects and parameters.

The main aspect of data treated in a GIS is their dual nature: geographic location (expressed as coordinates in a map) and descriptive attributes (that might be represented in a conventional database). Another essential aspect is that geographic data do not exist by themselves on a space: as important as locating them is to find out and represent the relation between them.

See below some examples of typical GIS spatial analysis processes :

SPATIAL ANALYSIS EXAMPLES
Analysis General Question Example
Condition What is ... What is the population of this city?
Localization Where is...? Where are the areas with slope greater than 20%?
Tendency What has changed...? Was this land productive 5 years ago?
Route What path.. ? What is the best path to the subway?
Pattern What is the pattern....? What is the distribution of dengue fever in the city of Fortaleza?
Models What happens if...? What is the impact on the weather if the Brazilian Amazonia is deforested?

 

A pioneer example, where the space category was intuitively incorporated to the analyses performed took place in the 19th century carried out by John Snow. In 1854, one the many cholera epidemics was taking place in London, brought from the Indies. At that time, nobody knew much about the causes of the disease. Two scientific schools tried to explain it: one relating it to miasmas concentrated in the lower and swampy regions of the city and another to the ingestion of contaminated water. The map below presents the location of deaths due to cholera and the water pumps that supplied the city, allowing the clear identification of one of the locations, in Broad Street, as the epicenter of the epidemics. Later studies confirmed this hypothesis, corroborated by other information like the localization of the water pump down river from the city, in a place where there was a maximum concentration of waste, including excrements from choleric patients. This was one of the first examples of spatial analysis where the spatial relationship of the data significantly contributed to the advancement in the comprehension of a phenomenon.

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Figure - London Map showing deaths from cholera identified by dots and water pumps represented by crosses (adapted from E. Tufte, 1983).

The most used taxonomy to characterize the problems of spatial analysis consider three types of data:

  • Events or point patterns – phenomena expressed through occurrences identified as points in space, denominated point processes. Some examples are: crime spots, disease occurrences, and the localization of vegetal species
  • Continuous surfaces – estimated from a set of field samples that can be regularly or irregularly distributed. Usually, this type of data results from natural resources survey, which includes geological, topographical, ecological, phitogeographic, and pedological maps.
  • Areas with Counts and Aggregated Rates – means data associated to population surveys, like census and health statistics, and that are originally referred to individuals situated in specific points in space. For confidentiality reasons these data are aggregated in analysis units, usually delimited by closed polygons (census tracts, postal addressing zones, municipalities).

Generally, the spatial analysis operations can be subdivided in three groups:

  • Manipulation of "geo-fields" (geographic fields): also called maps algebra, they operate on thematic maps, images and digital terrain models. For example, we have boolean operations over thematic maps.
  • Query on "geo-objects" (geographic objects): these operations show geo-objects satisfying restrictions (spatial or conventional). For example, "show all cities in São Paulo with more than 50000 inhabitants".
  • Conversion between geo-fields and geo-objects: they transform geo-fields and geo-objects. For example, the generation of a distance map from one or more geo-objects to produce a terrain model with values for the distances to selected points.

To better understand all these concepts, you should be familiar with SPRING data model, which serves as basis for the a geographic data manipulation language LEGAL. The objective of LEGAL is to provide an environment for geographic analysis, including operations on geo-fields and geo-objects (see the tools for query on geo-objects).


Tools for spatial analysis in SPRING: