Introduction to GEOGRAPHIC INFORMATION SYSTEMS Dr. Ahmet Çizmeli METU GGIT Geodetic and Geographic Information Technologies Fall 2008 CE 413

The nature of spatial data Special language for spatial phenomena Various ways of defining spatial phenomena Importance of spatial scale Types of spatial objects / data models Measurements Information content of spatial data

The nature of spatial data A spatial language for spatial phenomena Now that we have a better idea why we need spatial data, we have to : Think more clearly about space & spatial phenomena; Communicate spatial phenomena with others; One needs to use a language specific to spatial phenomena and become more efficient in communicating spatial ideas; A regular use of a common spatial language will eventually change the way we think and perceive spatial phenomena : orthophoto continuous raster digital high scale/low resolution random autocorrelated transect GIScience kriging DEM cylindrical equidistant feature extraction WMS/WFS postgreSQL Voronoi contour map SWOT

The nature of spatial data Spatial phenomena (objects) described digitally : Spatial phenomena are the real-world objects and events that we would like to describe digitally with GIS; Spatial phenomena digital spatial data models represented in GIS using

The nature of spatial data Spatial data models : Spatial data models are the c omputer-level data organization models that one uses to represent real world objects or phenomena. There are 4 main types of elementary spatial data models : Point (0 dimensional); Line (1 dimensional); Raster (2 dimensional); Surface (3 dimensional); Others... The choice of the right spatial object depends not only on the spatial phenomena to be described but also on the spatial scale of the study and the specific needs of the project.

The nature of spatial data Spatial phenomena (objects) described digitally : The digital representation of a real-world phenomena is only an approximation at best; To make the digital representation of a given spatial phenomena, one has to choose the appropriate data object at the optimal spatial resolution; There is always a given amount of information that is lost in the representation process; This loss can be significant if care is not taken during the GIS design stage; A thorough understanding and a skillful mastering of the data models is mandatory for a successful GIS study.

The nature of spatial data Types of spatial phenomena : There are various majors types of spatial phenomena. One major way of categorizing them is with respect to their spatial continuity : Discrete phenomena; Continuous phenomena. Discrete phenomena are easier to represent. Usually points are used. Continuous phenomena are harder to model. One needs to makes some assumptions/simplifications. Although some alternatives exist, the raster data model is generally suitable for the representation of continuous phenomena;

The nature of spatial data Importance of the spatial scale : The scale has a primary effect on all spatial considerations; Depending on the scale, a user may decide whether to express a building as a 0-dimensional point or a couple of raster pixels; Political boundaries are abstract (virtual) concepts that do not exist in the physical world; Most of the time, they are correctly expressed as lines (no width).

The nature of spatial data Importance of the spatial scale : In reality, a road is a 3-dimensional surface. It is however possible to display it as a 2- dimensional area (patch) or as two 1- dimensional lines or simply as a 1- dimensional line. The choice will certainly depend on the scale; A area object can be expressed using a point object or an area object or else. There is no magical formula. Its all about the art of spatial modeling!

The nature of spatial data Information content of spatial data : There are various levels of coexisting information stored in a given spatial dataset : Description of the way objects occupy space (simple location of a dam, spatial extent of a garden); Identification of the objects (what type of tree? what brand of car?); The abundance of objects (what concentration of a substance in water/air?); Other attributes (all sorts of data... age, skin color, language spoken... )

The nature of spatial data Spatial patterns : Spatial patterns describe the way spatial objects occupy space. Among the usual patterns we encounter in every day life, one can count patterns which are : Regular (systematic); Random (with no underlying spatial design); Clustered; Oriented; High-density; Dispersed;... One can also note other patterns on the temporal dimension such as static and dynamic (steady, exponential) patterns;

The nature of spatial data Geographic data can be obtained by : Taking measurements in the field; Purchasing; Finding from existing sources in digital form; Capturing from analog maps by GEOCODING : Scanning hardcopy maps; Digitizing with a digitizer; Real Time Coordinate Input Ground Surveying Global Positioning System (GPS)‏ Existing Digital Data Topo Maps Road Networks Census Data Property Maps

The nature of spatial data Data measurements : Data used in GIS needs to be collected with the objective of serving a spatial purpose. A thorough understanding of the spatial objects and their spatial attributes (described best with the geospatial language) is critical even at the beginning of data collection stage; Geospatial data sampling should be conducted in the correct measurement/spatial/temporal scale. Otherwise the quality and the usability of the data is jeopardized;

The nature of spatial data Data measurement strategies : The nature of the spatial phenomena to be studied should be taken into account from at the early stages of data collection design; One can define the following data schemes : Data collected on points, over quadrats, along transects over a regular grid, etc.; Probabilistic sampling; Random sampling; Systematic sampling; Stratified sampling; Homogeneous sampling;

The nature of spatial data Measurements can be in : Nominal scale, in ordinal scale or as interval/ratio.

The nature of spatial data Associations between patterns : The main role of the GIS is to illustrate, describe and quantify spatial associations; Spatial autocorrelation is one of the most significant spatial association that one studies using GIS. It helps quantify the spatial heterogeneity; Because of spatial autocorrelation, points (phenomena) located closer have a tendency to be more similar than points apart; Many other associations also do exist : spatial causality (crime in one place causes more crime in the surrounding areas), spatial interaction (persons committing crime interact with their surrounding), spatial co-variance (cities are nearly always located near water sources) etc....

The nature of spatial data Spatial autocorrelation :

The nature of spatial data Associations between patterns : A GIS user regularly faces the challenge of being obliged to estimate non-existing measurements using existing values : Interpolation is the process of estimating non-existing measurements within bounding values ; Extrapolation is the process of estimating information outside bounding values; The estimation can be or 4 dimensional (or sometimes more). When it is two-dimensional, it is called surface fitting; There are two steps in the process : Fitting all existing values to an equation; Solving the equation for each desired missing values. Nearer values have more weight on missing values than points further away : spatial autocorrelation;

The nature of spatial data Extrapolation is always associated with more uncertainty than interpolation : spatial autocorrelation; Both procedures are challenging and non- exact. One needs to make assumptions on the nature of the spatial data (and choose the mathematical model accordingly (linear, exponential, weighted etc...); Associations between patterns :