Data Models for GIS Part 1, Slides of Geodesy and Cartography

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Geographical Data Modeling

UML and Data Modeling Elements Examples from the Marine Data Model and ArcHydro (Thanks to Dawn Wright)

Longley et al., ch. 8

Models for GIS

representation of reality --> model

GIS itself is based on a model of

complexity and used to model

complexity

Not a full representation of reality: even

at 1:

data model = limited representation of

reality

 a discretization or partitioning of space  (^) finite, discrete nature of computing devices

3 Data Models

geo-relational coverage (object view;

from classic ArcInfo polys)

geo-relational shapefile (object view;

from ArcView)

geodatabase (object-oriented; new in

ArcGIS 8,9 etc.)

Object Oriented GIS (p. 191)

Object: a self-contained package of

information describing an entity

Collection of objects – class

Objects can have behavior – encapsulation

Inheritance – reusable objects

Polymorphism-objects can have their own

implementation for application-e.g. create,

draw, delete

ArcInfo Coverage as a Data Structure Image courtesy of Louisville/Jefferson County Information Consortium, Kentucky

Data Structure

Not tied to process at all

Concerned simply with what can be

computed and what can’t

Way in which the data model is

represented in the GIS



DEM for grid or layer model (e.g. array)



contour for isoline model



TIN for Delaunay triangulation model



coverages, shapefiles for geo-relational



geodatabase for OO geodatabase

Geodatabase

Features and attributes as objects Relationships among features encoded Validation or editing rules, behaviors “Container” for  (^) Vector, raster, tabular data  (^) Relationships  (^) Topology MS-Office

Relationships for a

feature

ArcMarine

dusk.geo.orst.edu/djl/arcgis

Data Modeling for Spatial

Analysis

What is spatial analysis?

 (^) "a set of methods whose results change when the locations of the objects being analyzed change"

Methods for working with spatial data

 (^) to detect patterns, anomalies  (^) to find answers to questions  (^) to test or confirm theories  (^) deductive reasoning-general to specific  to generate new theories and generalizations  (^) inductive reasoning-specific to general

A Georelational to a Geodatabase Model Coverage and shapefile data structures  (^) homogenous collections of points, lines, and polygons with generic, 1- and 2-dimensional "behavior" as operations Can’t distinguish behaviors  Point for a marker buoy, same as point for observation “smart features” in a geodatabase  (^) lighthouse must be on land, marine mammal siting must be in ocean  (^) Objects can “self-police”

Basic template for implementing GIS projects 

input, formatting, geoprocessing, creating

maps, performing analyses

Basic framework for writing program code and maintaining applications 

development of tools for the community

Promote networking and data sharing through established standards 

common model=interoperability

Purpose of ArcHydro, ArcMarine etc.

Geodatabase Concepts

ESRI's data object-oriented data model

 objects, features, behaviors

Geodatabase

 (^) collection of feature data sets, rasters, TINs  all data in relational tables  (^) behavior is coupled with features through rules (object- orientation)  (^) Supports model-builder for processes

Feature data set

 (^) contains feature classes  (^) defines topological role of features  (^) has a coordinate system

Geodatabase Concepts ( cont. )

Feature class



stored in a relational table



special field for geometric shape



geometric data incorporated into the

database

 Point, multipoint, segment, path, ring, polyline, polygon