Spatial Analysis
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.

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:
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