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地理信息系统 工程 GIS Engineering. Wuhan University School of Resource and environment Guo Qingsheng. 武汉大学郭庆胜. What is GIS? a cross disciplinary field to map out information , to think graphically and to build analytical solutions; to mode and to carry out prediction

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gis engineering

地理信息系统工程GIS Engineering

Wuhan University

School of Resource and environment

Guo Qingsheng


What is GIS?
    • a cross disciplinary fieldto map out information , to think graphically and to build analytical solutions; to mode and to carry out prediction
    • set of tools for storing and retrieving at will,
    • transforming and displaying spatial data from the real world for a particular set of purposes.(Peter Burrough, 1986)
  • A special IS where the database consists of observations on spatially distributed features, activities or events which are definable in space as points, line or areas. (Dueker, 1979)A GIS manipulates these data.
what is a gis
What is a GIS

A system of hardware, software, and procedures designed to support the capture, management, manipulation, analysis, modeling and display of spatially-referenced data for solving complex planning and management problems. (NCGIA lecture by David Cowen, 1989)

GISystem– GIScience--GIServer

four ways to view gis goodchild 90
Four ways to view GIS [Goodchild 90]

[Automated Mapping] facilitating the production of standard maps,

[Map Analysis] providing measurement and overlay tools that are cheaper than traditional methods,

[Inventory] giving geographic access capabilities to existing governmental and corporate databases,

[Spatial Analysis & Spatial Decision Support] enabling new uses for old data by giving users query and analysis tools.

and one more iles89
And one more [Iles89]

A GIS is basically a tool that mines data and displays it. It doesn't clean it up, or maintain it, and seldom even looks to see if it's reasonable.





Spatial Data

What is it!



  • Where is it?
  • How do we locate it in space?
  • Well, on earth, anyway
basic definition and explanation of spatial data

Basic Definition and explanation of Spatial Data



Spatial Data

Spatial Analysis


GIS = Geographic Information Sciences

gisystems giscience and gistudies
GISystems, GIScience and GIStudies




gisystems giscience and gistudies1
GISystems, GIScience and GIStudies
  • GISystems (GIS)
    • Emphasis on technology and tools
    • “GIServices”
    • Implements storehouse of GISci knowledge
  • GIScience (GISci)
    • Fundamental issues raised by the use of GIS and related technologies (e.g.)
      • Spatial analysis
      • Map projections
      • Accuracy
      • Scientific visualization
    • Systematic study of the use of geographic information
  • GIStudies (GISt?)
    • how systems and science are embedded in a societal context, applications
Geographic Information Science
  • The organized activity by which people measure aspects of geographic phenomena and processes.
  • Represent the measurements and operate upon these representations to discover new relationships.
Geographic Information Science
  • Organized activities and tools by which people make:Measurements, Representation, Operations and Transformations within

Institutional, Social and Cultural Context (Chrisman, 2002)

Our Geographic Information Science
  • We will emphasize integration of data, technical and human resources within the framework of the information technology.
  • We will explore issues related to GIS future development (capability, interoperability, accessibility).




Spatial Data







Maps and Plans

Digital data

Paper files








Field survey

Remote Sensing

GIS Data Sources




Land use


Soil Type



Land Parcels

Geography Objects

spatial geography data objects
  • spatial component
    • relative position between objects
    • coordinate system
  • attribute component
    • explains spatial objects characteristics
  • spatial relationship
    • relationship between objects
  • time component
    • temporal element















  • X-Y Coordinate System
  • Shape
  • Area/Size
  • Perimeter
  • Distance
  • Neighborhood


  • Explains about spatial data
  • Relevant non-spatial data
  • Words or Numbers
  • Qualitative methods
  • Quantitative methods












kinds of data
Kinds of data
  • Geographic Where it is
  • Attribute What it is
  • Metadata Data about the data
    • documentation How it was made
    • data dictionary What do terms mean
      • What is a road, highway, expressway, thruway, …….?
      • What is a lake, pond, impoundment?
      • What are names, addresses ……?
earth s coordinate system
Earth’s Coordinate System

X is Longitude and is measured E and W from Greenwich, England. West is negative, East is positive

Y is latitude and is measured N and S from the equator. North is positive and S is negative.

These are called Geographic Coordinates

Spatial Reference Systems
  • The relationship between places creates the geometry and gets analytical geometry. Spatial measurements require geometric assumptions. Local measurements must be connected to a larger framework, otherwise they become obsolete.
Earth is NOT a sphere!
  • It is more pear shaped
  • To accommodate this geographers and surveyors have created models of the earth’s surface
  • These are called Datums
When using geographic data you must be sure that all the data is based on the same Datum
  • There are hundreds of datums worldwide
  • In the US the common ones are

NAD 27

NAD 83

WGS 72

WGS 84


In order to place objects at a specific location, we need to define a coordinate system to represent the surface of the Earth.

Consider the geographic grid system.


Vertical meridians

Horizontal parallels

Prime meridian at British Royal Observatory at Greenwich



longitude and latitude
Longitude and Latitude

Longitude measures the angular distance from the prime meridian.


Latitude measures the angular distance from the equator.



Degrees minutes seconds 12d 30m 15s

Decimal degrees 12.5042 degrees



Can't flatten a


Without distortion

map projection
Map Projection

Geographic coordinates are based on a spherical model. However to represent maps on a 2D surface like on our computer screen or on a paper map, we need to rely upon a map projection, a mathematical formula that transforms latitude and longitude locations to x, y coordinates.

For example: Moscow

Geographic UTM

latitude: 37d 36m 30s x: 412,648.41 meters

longitude: 55d 45m 01s y: 6,179,073.07 meters

many different projections
Many Different Projections

Different projections suit different applications. For example, some projections preserve distance, while other preserve area, or shape, or direction.

It is important to note that different datasets transformed to different projections will not register with one another. That’s where GIS can help a lot!

x y longitude latitude

Stretch the top

Stretch the bottom

X, Y = Longitude, Latitude














Lines of constant Longitude

Lines of constant Latitude

x y longitude latitude1

-76.15° 43.04°

W76.15° N43.04°

X, Y = Longitude, Latitude

90E, 30N

+90, +30








-90 -30

90W, 30S







Lines of constant Longitude

Lines of constant Latitude

the world in geographic coordinates
The world in Geographic Coordinates



Really that big?

the projection problem
  • There are many mathematical ways of projecting the spherical surface onto a flat surface.
  • For the earth these have names like

Albers equal area





Lambert equal area Azimuthal

other projections
Other Projections

Which is right?

Answer: All of them!

They are just different!

All data must be in the same projection
  • (Unless you are working with a very small area)
question 2

Table of Contents shows 3 layers

2 Layers displayed –

They match!

All three match in space

Question #2


  • In GISs data is almost always stored in thematic layers – for example
    • Boundaries
    • Rivers
    • Roads
  • In ArcView, one kind of GIS, it looks like this


map data in a gis
Map data in a GIS
  • Paper maps have many Themes orLayers
    • lakes, roads, streams, names, etc.
  • In a GIS we can separate themes
  • In fact, generally we MUST separate themes
  • So how is the theme data stored
themes or layers
Themes or Layers

Geographic location (X,Y)


Power lines



gis map data
GIS map data
  • Most GISs store data in thematic layers. They can be called
    • Layersor
    • Coverages or
    • Shape files
  • Features are called features or objects
  • A mapcompositionis a combination of layers
gis representation of the world
GIS representation of the world

In a GIS, different object types are represented by different layers or themes. A layer can either be points, lines, polygons, and possibly a raster dataset. Each layer is also associated with an attribute table.

examples of layer types
Examples of layer types

points lines polygons

Retail Stores Highways Countries

Cities,towns City Streets Postal Zones

Manhole Covers Power Lines Tax Parcels

Telephone Poles Rivers Census Blocks or Tracts

Airports Water, Sewer Lines Airports

Businesses Railroads Building Outlines

Warehouses Shorelines Military Installations

Customers Bus Routes Lakes

Prospects Pipelines Area Code Boundaries

Disease cases Runways Counties

examples of layer types1
Examples of layer types

points lines polygons

Retail Stores Highways Countries

Cities,towns City Streets Postal Zones

Manhole Covers Power Lines Tax Parcels

Telephone Poles Rivers Census Blocks or Tracts

Airports Water, Sewer Lines Airports

Businesses Railroads Building Outlines

Warehouses Shorelines Military Installations

Customers Bus Routes Lakes

Prospects Pipelines Area Code Boundaries

Disease cases Runways Counties

map compositions
Map compositions

Roads, streams, & powerlines

Landuse, roads, & powerlines

Spatial data is unique because linked to geographic map feature.
  • Features in a map database are used to manage the data/measurements.
  • What are features? They are objects placed on a map, such as point features, line features, area (poly) features and annotations.
representing geographic features
Representing Geographic Features

How do we describe geographical features?

  • by recognizing two types of data:
    • Spatial data which describes location (where)
    • Attribute data which specifies characteristics at that location (what and how much)

How do we represent these digitally in a GIS?

  • by grouping into themes based on similar characteristics (e.g hydrography, elevation, water lines, sewer lines, grocery sales) and using either:
    • vector data model (coverage in ARC/INFO, shapefile in ArcView)
    • raster data model (GRID in ARC/INFO)
  • by selecting appropriate scale, projection, accuracy level, and resolution

How do we incorporate into a computer application system?

  • by using one or other of two types of computing environments
    • relational data base model (RDBMS) ( such as Arc/Info)
    • object oriented model (ArcView, Smallworld--although ArcView presents data to user as RDBMS)
spatial and attribute data
Spatial and Attribute Data
  • Spatial data (where)
    • specifies location
    • stored in a shape file in Arcview
  • Attribute (descriptive) data (what and how much)
    • specifies characteristics at that location, natural or human-created
    • stored in a data base table

GIS systems traditionally maintain spatial and attribute data separately, then “join” them for display or analysis

    • for example, in ArcView, the Attributes of … table is used to link a shape file (spatial structure) with a data base table containing attribute information in order to display the attribute data
spatial data types
Spatial Data Types
  • continuous: elevation, rainfall, ocean salinity
  • areas:
    • unbounded: landuse, market areas, soils, rock type
    • bounded: city/county/state boundaries, ownership parcels, zoning
    • moving: air masses, animal herds, schools of fish
  • networks: roads, transmission lines, streams
  • points:
    • fixed: wells, street lamps, addresses
    • moving: cars, fish, deer
attribute data types
Categorical (name):(often coded to numbers eg SSN but can’t do arithmetic)


no inherent ordering

land use types, county names


inherent order

road class; stream class

Numerical(may be expressed as integer [whole number] or floating point [decimal fraction])


known distance between values

can’t say ‘twice as much’

temperature (Celsius or Fahrenheit)


natural zero

ratios make sense (e.g. twice as much)

income, age, rainfall

Attribute data types

Attribute data tables can contain locational information, such as addresses

or a list of X,Y coordinates. ArcView refers to these as event tables. However,

these must be converted to true spatial data (shape file), for example by

geocoding, before they can be displayed as a map.

attributes reference systems

Attributes Reference Systems

Each specific attribute scale requires its own reference system.

Fundamental Physical properties form the basis for the international standards that include the metric system or SI.

Stevens formulated the broader framework called “levels of measurements of Attributes reference systems” in 1946

Attributes Reference Systems: levels of measurements

Concept: invariance under transformations – to what degree a scale can retain its essential information content even if it is not identical to some other scale?

A level groups the scales that share a set of possible transformations. (ex. Temperature)

Stevens’ four levels of measurements I

Nominal Scale: objects are classified into groups. Any symbol can be used.

Ordinal Scale: objects are sorted and follow same direction. Order matters, but importance of each step may vary.

Stevens’ four levels of measurements II

Interval Scale: arbitrary zero point and interval (unit of measurement).

Ratio scale: difference between two interval measures.

Additional levels: Absolute scale; Cyclical measures, Counts and Graded membership in categories (from fuzzy set theory).

scales of measurement
Scales of measurement

Heywood et. al. 1998 – Table 2.1

spatial data models
Spatial data models
  • Raster
  • Vector
  • Object-oriented

Spatial data formats:

gis data models raster v vector raster is faster but vector is corrector joseph berry
Raster data model

location is referenced by a grid cell in a rectangular array (matrix)

attribute is represented as a single value for that cell

much data comes in this form

images from remote sensing (LANDSAT, SPOT)

scanned maps

elevation data from USGS

best for continuous features:



soil type

land use

Vector data model

location referenced by x,y coordinates, which can be linked to form lines and polygons

attributes referenced through unique ID number to tables

much data comes in this form

DIME and TIGER files from US Census

DLG from USGS for streams, roads, etc

census data (tabular)

best for features with discrete boundaries

property lines

political boundaries








GIS Data Models: Raster v. Vector“raster is faster but vector is corrector” Joseph Berry

Concept of

Vector and Raster

Real World

Raster Representation

Vector Representation




stages of development
Stages of development:
  • Conceptual model: select the features of reality to be modeled and decide what entities will represent them
  • Spatial data model: select a format that will represent the model entities
  • Spatial data structure: decide how to code the entities in the model’s data files
raster format
Raster format
  • Features represented by cell contents
  • Spatial precision limited by cell size
  • Surfaces modeled as continuous values
vector format
Vector format
  • Spatial precision limited by number format
  • Discrete features explicitly represented
  • Surfaces shown by contours rather than continuous values
thematic data a k a attribute data
Thematic data (a.k.a. “attribute data”)
  • Quantitative or descriptive
  • May represent 1 or many themes
  • Tied to a spatial reference
  • Represented differently in raster vs. vector formats
vector data
Vector Data

Geographers typically model the world with objects located at different places on the surface of the earth. Through this model, real -world entities are represented simply by:




These “shapes” represent the spatial character of the object:

shape, size, spatial arrangement


How do we give meaning to these “shapes”?

Associated with each point, line, and polygon are additional attribute data that serve to better define the object.

For example: Cities on a map are represented as points. An attribute table serves to define each city. In the attribute table, each record represents a city. Each city record contains a number of fields that store attribute data. This construct is known as a relational database.

  • The Relational database is not anything GIS- specific.
  • Generally relational databases are considered “relational” because they allow for data from multiple tables to be “related” to one another using what is known as a join.
  • A join is simply a matching of rows between tables based on a common identifier.
  • GIS-based relational databases allow for spatial joins, in which the common identifier is spatial location.
importance of topology in vector format
Importance of Topology in Vector Format

Thus far we have presented vector data simply as points, lines, and polygons with associated attributes. This provides for location and meaning. However, it is also important to understand the spatial relationship between spatial objects. This is called topology.

consider these relationships: adjacency



Topology allows for much more sophisticated spatial analyses.

raster data
Raster Data

The raster formatting represents geographic data as images in a regular grid of cells. Row and column numbers define the locations of each cell in the grid or map. These cells are known as pixels. Each pixel can be assigned attribute values.

Examples of typical raster data:

remote sensing images

scanned maps

In Arcview GIS, raster datasets are known as “grids”.


How would we represent different types of spatial data using a raster?




What are the advantages and disadvantages of raster data?

Advantages: inherent format for scanned maps and images

Disadvantages: Relationship between resolution and precision

The assignment of attributes to a set of pixels

such as street names

raster vs vector data structures
Raster vs Vector Data Structures
  • Raster data stored as an array of values
    • Georeferencing is implicit in the structure
    • Usually defined by one corner of the image and the cell size
    • Attributes are defined by the cell values (no character data!)
    • One attribute for each raster file
  • Vector data are stored as a series of xy coordinates
    • Points are stored as single xy coordinate
    • Lines are a string of xy coordinates
    • Polygons are composed of one or more lines and a label
    • Attributes are attached to each feature through a unique numeric code
    • Many attributes may be stored in each vector file
spatial modeling in raster format
Spatial modeling in raster format
  • Basic entity is the cell
  • Region represented by a tiling of cells
  • Cell size = resolution
  • Attribute data linked to individual cells
raster data models
Raster Data Models
  • Each location is represented as a cell.
  • Cells organized into a matrix or rows and columns called a grid.
  • Each row contains a group of cells with values representing a geographic phenomenon.
  • Cell values represent nominal data such as land-use classes or elevation
raster data models1
Raster Data Models
  • Cells are identified by their position in the grid.
  • Cells have eight 8 neighbors, four at the corners and four at the sides.
storing raster data
Storing Raster Data
  • Layout of a raster file
    • rows, columns, and cells (pixels)
  • Representation of features
    • Individual points, lines, or polygons
    • Maps and surfaces

Cells or pixels

raster georeferencing
Raster georeferencing
  • Must know coordinates of one corner, and the cell size
  • Some systems require all layers to be precisely matched
  • Usually one attribute per layer
raster data types
Raster data types
  • Data types
    • Ascii: stored as arrays of integers or floating point characters
      • Simple, readable by almost any software, but INEFFICIENT
    • Binary: stored in binary format
      • Not as transportable, but very efficient
    • Values may be stored as integers or floating point data.
    • Floating point takes about 4 times as much space!
    • Often worthwhile to convert floating point to integer.
images vs grids
Images vs grids
  • In Arcview, images are simple raster data for display only
  • Grids are a proprietary format for storing raster data
    • may be analyzed and manipulated
    • Only available for use with Spatial Analyst
  • A grid may be opened as an image, but not vice-versa!
  • Other GIS packages will have their own “grid” formats
  • Raw image format (raster)
    • basic binary data arranged in arrays, one row per line
    • each pixel usually one byte (0-255),
      • 0 is black, 255 is white
    • images may have one or more bands
      • bands usually represent brightness in different color ranges
      • true-color images have red,green,blue bands
      • others may have 7, 14, 225 bands!!!
    • header file gives important size and georeferencing info
      • rows, cols, bands, Upper Left, pixel size, projection, etc.

Single band grayscale image

image file types
Image file types
  • Raw binary file layouts (for bands a, b, and c)
    • BIP (band interleaved by pixel)
    • BIL (band interleaved by line)
    • BSQ (band sequential)
  • Proprietary image formats
    • Tiff
    • GIF
    • JPEG
    • Sun Rasterfiles
    • Windows metafiles
  • Most programs will read raw image files



















storing raster map data
Storing Raster Map Data
  • Grid data are stored in special format for display and analysis
  • One grid layer per theme (not multiband)
    • elevation, geology, rainfall, etc each in separate file
raster data resolution
Raster Data Resolution
  • Rasterizing involves some idealization of the map or objects
  • Raster data are stored with a cell size that determines the resolution
    • Representing features precisely requires high resolution (small cell size)
  • Storage requirements increase by the square of the image dimensions
    • A 16x16 image has 256 pixels; a 32x32 image has 1024 pixels!
  • Resolution can affect estimates of area and length
  • The raster model is not suitable for applications requiring high precision, such as land information systems
raster resolution
Raster resolution

Vector map

16x16=256 bytes

32x32=1024 bytes

sources of raster data
Sources of raster data
  • Conversion from vector data (rasterization)
    • Nearly always involves a loss of resolution and precision
  • Scanning of maps on a B&W or color scanner
    • Usually requires some processing to assign categories
    • Especially problematic with color images due to slight variations in hue on map
  • Importing data already in raster format
    • DEMs = Digital Elevation Models, USGS
    • GIRAS = Landuse Maps from the USGS
    • Data from other programs, such as GRASS, ERDAS, IDRISI, ERMapper
  • Importing images from other formats
    • Includes aerial photos, satellite images, remotely sensed data
raster compression techniques
Raster Compression Techniques
  • Storage limits resolution, so strategies for compression are key
    • Full Raster Encoding (no compression)
    • Run-Length Encoding
    • Value-Point Encoding
    • Quadtrees
    • Tiling (access strategy)
  • Compression saves space, but requires time to save and extract
  • Compression success varies with type of data
    • Works best on data with low spatial variability and limited possible values
    • Works poorly with high spatial variability data or continuous surfaces
  • Thus, compression may actually increase storage space or access time with some types of data.
quadtree compression
Quadtree compression
  • Add resolution only where needed by dividing into quarters
raster surface representation
Raster Surface Representation
  • Stored in same format as any raster data
    • Raster surfaces are usually generated from regularly spaced data
    • May be created from irregular data from tins
    • Special algorithms needed to convert contour data to good raster surfaces
  • Grids or images are arrays of cells
  • Lattices are arrays of point data
  • Data may be integer or floating point
  • Raster surfaces uses
    • Comparing differences between surfaces Modelling surface water, deriving hydrologic parameters from DEMs
    • Creating contour maps





discrete vs continuous surfaces
Discrete vs continuous surfaces
  • Compression works best on discrete surfaces
representing data using raster model
area is covered by grid with (usually) equal-sized cells

location of each cell calculated from origin of grid: “two down, three over” (usually from upper left, but lower left in ARCVIEW)

cells often called pixels (picture elements); raster data often called image data

attributes are recorded by assigning each cell a single value based on the majority feature (attribute) in the cell, such as land use type.

easy to do overlays/analyses, just by ‘combining’ corresponding cell values: “yield= rainfall + fertilizer” (why raster is faster, at least for some things)

simple data structure:

directly store each layer as a single table (basically, each is analagous to a “spreadsheet”)

no computer data base management system required (although some GIS systems incorporate them)






Representing Data using Raster Model
advantages of raster format
many data sets available

different file formats readily inter-converted

fast computer lookupand display

easy to overlay multiple themes

able to represent multiple continuous surfaces

Advantages of raster format
limitations of raster format
poor representation of discrete objects

constant resolution throughout region modeled

exact boundary location difficult

network analysis nearly impossible

difficult to change projection or coordinate system

Limitations of raster format
vector data models
Vector Data Models
  • Point objects: single pairs of x and y coordinates in a vector theme.
  • Lines:x and y coordinates of the beginning point and the end point.
  • Arcs: series of x,y coordinate pairs, known as vertices, at each direction change between the beginning point and end point of the line.
vector data models1
Vector Data Models
  • Area (polygon): an arc, where the beginning and ending points of the line are equal.
  • Polygons which share a boundary are called adjacent.
  • Areas have a perimeter.
representing data using the vector model general concept
Representing Data using the Vector Model: general concept

The fundamental concept of vector GIS is that all geographic features in the real work (or on a map) can be represented either as:

  • points or dots (nodes): trees, poles, fire plugs, airports, cities
  • lines (arcs): streams, streets, sewers,
  • areas (polygons): land parcels, cities, counties, forest, rock type

Which is used in a particular instance depends on scale, among other things: airport or manhole may be a point or polygon

Because representation depends on shape, ArcView refers to files containing spatial data as shapefiles (altho. these used for both vector and raster)

representing data using the vector model formal application


















Representing Data using the Vector Model:formal application


  • point (node): 0-dimension
    • single x,y coordinate pair
    • zero area
    • tree, oil well, label location
  • line (arc): 1-dimension
    • two (or more) connected x,y coordinates
    • road, stream
  • polygon : 2-dimensions
    • four or more ordered and connected x,y coordinates
    • first and last x,y pairs are the same
    • encloses an area
    • census tracts, county, lake


Point: 7,2


Line: 7,2 8,1

Polygon: 7,2 8,1 7,1 7,2

representing data using the vector model data implementation







Representing Data using the Vector Model:data implementation
  • Features in the theme (coverage) have unique identifiers--point ID, polygon ID, arc ID, etc
  • common identifiers provide link to:
    • coordinates table (for ‘where)
    • attributes table (for what)


  • concepts are those of a relational data base, which is really a prerequisite for the vector model (or need object-oriented computing environment)
spatial or topological modeling
Spatial or Topological Modeling
  • Uses geographic data to describe, simulate or predict a real-world problem or system.
  • A spatial model could simulate the change in land values given a new highway
  • Three categories of spatial modeling functions
types of topological modeling
Types of Topological Modeling
  • geometric models: such as calculating the Euclidean distance between features, generating buffers, calculating areas and perimeters,
  • coincidence models, such as topological overlay;
  • adjacency models (pathfinding, redistricting, and allocation)

Raster, Vector,

what's the difference?

  • Vector GIS represents the earth and objects on it as points, lines, and polygons
  • Raster GIS does this by placing a grid over the surface and in each cell noting what is under it.
vector is the usual gis choice why
Vector is the usual GIS choice.Why?



  • Products look like maps
  • Accuracy very high
  • Storage needs moderate
  • Access large databases
  • Spatial query is easy
  • Products not like maps
  • Accuracy is limited
  • Storage needs high
  • Storage needs grow with sizeof database
  • Spatial query is not efficient
  • Cost is very high
  • Systems are complex
  • Analysis capability limited
  • Cost is very low
  • Systems are relatively simple
  • Analysis capability very high
selection of data representation the relevance of these issues for gis projects
Selection of Data Representation the relevance of these issues for GIS Projects
  • selecting vector or raster as basis for project
    • usually dependent on the nature of the data
    • natural resource people favor raster; urban folks favor vector
    • decision is reflected in the choice of GIS software package
  • selecting software which uses either the raster or the vector model for internal data representation,
    • most have conversion capabilities for the other data type
    • some (eg ARC/INFO GRID) also have analysis capabilities
    • however, accuracy/performance/capability ‘hit’ if data doesn’t match software model
selection of data representation the relevance of these issues for gis projects1
Selection of Data Representation the relevance of these issues for GIS Projects
  • if use vector, electing to represent features as point or polygon, or line or polygon
    • every real-world feature has some area
    • generally depends on scale
    • manhole generally a point, unless facilities management at 1”=50’ scale
  • traditional computing environment v. object oriented approach
    • if starting from scratch, have the option to select the latest technology
    • if established, can be exceedingly costly (data, training) to convert
choosing the data model
Choosing the data model
  • Raster advantages
    • Simple data model
    • Many spatial analysis functions often simpler and faster
    • Efficient for data with high spatial variability
    • Efficient for low spatial variability when compressed
    • Easy to integrate with satellite and remotely sensed data
    • Topological relationships are not explicitly encoded, some analysis is more difficult
  • Vector advantages
    • Can store data efficiently with high precision
    • Requires about 10% of space to store same data in raster format
    • Certain types of topological analysis are more efficient or only possible with vector
    • Gives much greater precision and accuracy
    • Greater flexibility in storing and manipulating attribute data
choosing a model for analysis
Choosing a model for analysis
  • Compare an arithmetic operation in raster and vector
    • Raster
      • Simply add two cells together
    • Vector
      • Must subdivide or intersect polygons first to build new polygon coverage
      • Then values in each new polygon may be added together
  • Buffering (finding all areas within a certain distance of a feature)
    • Raster
      • Change values of cells within specified range from the target feature
    • Vector
      • Create circular polygons at regular intervals along the feature arcs
      • Intersect all the circles
      • Dissolve arcs inside the circles
choosing a model for analysis1
Choosing a model for analysis
  • Operations best suited to raster analysis
    • Overlays and arithmetic, boolean, and map algegra operations
    • Buffering
    • Proximity, cost-distance
    • Viewshed analysis (what parts of a surface can be seen)
    • Any operations requiring continuous surfaces
    • Projects involving data with high spatial variability
    • Projects in which original data is raster (e.g. satellites)
  • Operations best suited to vector analysis
    • Connectivity, network modeling
    • Point-in-polygon and Line-in-polygon overlays
    • Overlays when many attributes are involved
    • Evaluating contiguity
    • Projects requiring high precision of stored data
    • Projects in which attributes are primarily character data
choosing a data model
Choosing a data model
  • Some GIS packages are primarily vector OR raster
    • Raster: GRASS, IDRISI, MOSS
    • Vector: Intergraph? MapInfo
    • Integrated: Arc/Info, Arcview/Spatial Analyst
  • Some allow you to convert between types with ease
  • Often convenient to use one model primarily, but convert to the other for certain operations
    • Rasterization generally involves a loss of precision
    • The precision loss is retained if data are re-converted to vector
  • A surface is a set of continuous data, such as elevation over an area.
  • Surface data is usually not characterized by a sudden change in value.
  • Surfaces can be represented by models built from regularly or irregularly spaced sample points on the surface.
surface models
Surface Models
  • May represent either discrete features with integer values or spatially continuous data with floating point values.
  • Discrete geographic features most often represents objects (e.g., parcels, land-use type, or jurisdictions)
  • Discrete data often have known and definable boundaries
surface models1
Surface Models
  • Spatially continuous data: a different value can be assigned at each location.
  • Continuous geographic phenomena do not have distinct boundaries
  • Examples include population density, income levels, soil types.
tin terrain models3

Drainage network computation

Contour maps

TIN Terrain Models
nuggets in tins
Nuggets in TINs
  • Easy triangle strips
  • Triangle strip compression
  • “Crust” to reduce terracing
  • Tripod: minimalist data structure
  • TIN reconstruction in linear time
triangle strips from dfs
Triangle strips from DFS
  • Depth first search based on visibility order
  • Extract strips from DFS graph
triangle strips
Triangle Strips


v0 v1 v2 v3 v4 v5 v6 v7 v8


v2 v0 v1

v2 v1 v3

v4 v2 v3

v4 v3 v5

v6 v4 v5

v6 v5 v7

v8 v6 v7










Geographic SCALE


For a map to be as accurate as possible the sizes of features and distances between them should be in the same proportion as they are in the real world. This is done by drawing the map to scale.

For example: scale of 1:50000

means 1cm on the map represents 50000cm in the real world

1:10000 is a larger scale than 1:25000 scale

Scale affects how we choose to represent objects in space. It also influences the accuracy at which the location of objects can be shown.


Scale =

distance on map(distance unit)distance on ground (distance unit)


A Scale of 1/24,000 means

1 inch (or foot, or furlong) on the map =

24,000 inches (or feet or furlongs) on the ground.


Living Room

Dinning Rm.




Small Scale dataLarge area/sheetLeast accurate


Is a smaller number than



Is a smaller number than


Large Scale dataSmall area / sheetMost accurate


accuracy generalization
Accuracy & Generalization
  • When a paper map is made at a very small scale the cartographer is limited by the pen being used
  • Can’t draw anything finer than the width of the pen line.
  • At a scale of 1/1,000,000 a line 0.05 cm wide = 0.05x1,000,000 cm or 50,000 cm or 500 meters or 19,850” or 1,640’ wide!
  • What road 1,640’ wide!!!
  • So on the map the road is much, much too wide
accuracy generalization1
Accuracy & Generalization
  • Take the case of a winding stream
  • Shrink it to a Smaller scale
  • Now it is hard to see what is there


  • So the cartographer simplifies the stream
accuracy generalization2
The generalized stream is not as accurate a representation of the stream as the original

And if you try to mix data of different scale common lines are NOT going to match



Accuracy & Generalization
Mixing data from different scales will cause errors because cartographers have generalized the different scale data differently
  • Features in different scales
    • May be the “wrong” size
    • Won’t line up because of generalization

Same datum

  • Same projection
  • Same Scale
  • Using a GIS is more than just combining various data layers
  • You have to be careful that the basic three booby traps outlined here do not cause problems
  • And 3 possible sources of error give Murphy a field day since problems encountered go up as n2
Time Reference Systems (TRS)
  • .Time is a linear order . A TRS requires an origin and a unit of measurement.
  • A common TRS allow time measurements to be compared.
  • Time can be cyclical.
  • Time can also be made of ordered periods.
object oriented environment
Object Oriented Environment

Object Oriented Computing Environment:

  • Object: an entity that contains data (or properties) and the code to act on that data.
    • thus, code is integrated with data
    • especially suited to complex data types, which can not easily be represented in row/column format, such as multi-media and spatial data
  • objects are intended to be reusable components, which can be combined to produce other applications or desired results
  • most new GIS software implementations (including ArcView) are object based
  • object components for doing fundamental GIS operations (e.g. draw map, pan, zoom, projections) can also be purchased (e.g ESRI’s Map Objects)
  • Attribute: characteristic of a map feature. Attributes of a river might include its name, length, average depth, and so on.
  • Buffer: a polygon enclosing an area within a specified distance from a point, line or polygon; there are point, line and polygon buffers
  • Cadastral Map: Map showing boundaries of the subdivisions of land for purposes of recording ownership and taxation.
  • Cartesian coordinate system: two-dimensional, planar coordinate system in which x measures horizontal distance and y measures vertical distance. Each point on the plane is defined by an x,y coordinate.
  • Centroid: The geometric center of a polygon. A point in a polygon to which attribute information about that specific area is linked.
  • Contour Line: A set of points representing the same value of a selected attribute and forming an imaginary line.
  • Database: A collection of data organized according to a conceptual structure describing the characteristics of the data and the relationships among their corresponding entities, supporting applications areas. For example, a GIS database includes data about the position and characteristics of geographical features.
  • digital map data: locations and shapes of map features stored in a computer-readable format.
  • Euclidean distance: The straight line distance between two points, usually on a plane.
  • Geocoding: The process of assigning x,y coordinates to data that is not in a spatial data format.
  • Georeference: To establish the relationship between page co-ordinates on a planar map and known real-world co-ordinates.
  • Isoline: A line on a surface connecting points of equal value.
  • Layer: A thematic set of spatial data that are described, stored and organized by subject matter (e.g., soils, roads, and wells are each representative of a layer).
  • Line: A set of ordered co-ordinates that represent the shape of geographic features too narrow to be displayed as an area at the given scale (contours, street centerlines, or streams), or linear features with no area (county boundary lines). A line is synonymous with an arc.
  • Linear feature: A shape representing an object too narrow to be depicted as an area (roads, rivers, and elevation contours).
  • Point feature: A shape representing a geographic object too small to show as a line or area. Examples of point features include wells, hydrants, and bench marks.
  • Polygon: defined by the lines that make up its boundary and a point inside its boundary for identification. Polygons have attributes that describe the feature they represent.
  • Polygon feature: A shape representing a geographic object too large to be depicted as a point or line (counties, census tracts, and lakes).
  • Point: A zero-dimensional abstraction of an object represented by a single X,Y co-ordinate.
  • Relational database: A computer database where attributes (records) are stored in a series of two dimensional tables known as tuples.
  • Raster Data: An abstraction of the real world where spatial data is expressed as a matrix of cells or pixels, with spatial position implicit in the ordering of the pixels. spatial data is not continuous but divided into discrete units.
  • Spatial analysis: The study of the locations and shapes of geographic features and the relationships between them.
  • Spatial model: A methodology or procedures which simulates real world conditions using the spatial relationships of geographic features.
  • TIGER/Line files: a digital database of geographic features, such as roads, railroads, rivers, lakes, political boundaries, census statistical boundaries, etc. covering the entire United States.
  • Topology: The relative location of geographic phenomena independent of their exact position.
  • Topological relationships: (connectivity, adjacency and relative position) are relationships between nodes, links and polygons.
  • Vector Digital Data: Data which have been captured as points, lines ( a series of point coordinates), or areas (shapes bounded by lines)
  • Vector Data Model: where positional data is represented in the form of co-ordinates. In vector data, the basic units of spatial information are points, lines and polygons. Each of these units is composed as a series of co-ordinate points, for example, a line is a collection of related points, and a polygon is a collection of related lines.


Desktop GIS: ESRI family: ArcGIS

Best source:

What is ArcView?

What is ArcGIS?

How we will use them?

the software family

Appli- cation

Appli- cation





Appli- cation

Map Objects

ArcView 3.x

Professional software

Workstation A/I 8

GIS professional software

The software family

PC Arc/Info, WSArcInfo 7

GIS professional software

GIS Tools

GIS provides a set of tools that allows you to integrate data, such as overlap a series of thematic layers and to do spatial analysis, such as to manipulate each feature within the layer.

not as bad as it sounds
Not as bad as it sounds
  • There is a lot of commonality between all these softwares
  • If you learn one it easy to carry the ideas over to one of the others
what kinds of jobs exist in gis
What Kinds of Jobs Exist in GIS?
  • (1) System developers
    • high level of technical skills
    • programmers in C++, Java, Visual Basic
    • 1,000 people
  • (2) System maintainers
    • moderate technical skills
    • programmers in UML, Visio, CASE, Visual Basic
    • 10,000 people
what kinds of jobs exist in gis1
What Kinds of Jobs Exist in GIS?
  • (3) System users
    • modest technical skills
    • know how to use the tools
    • familiar with the technical issues
    • know the application domains
    • work for univ., corp., govts.
    • 100,000 people
  • (4) General public
    • minimal technical skills
    • Know how to use some tools
    • 1,000,000 people



Geographic Information

Systems and Science

by Longley, Goodchild,

Maguire, Rhind, 2001

Modeling Our World

by Zeiler, 2001


Optional Textbook

Spatial Reasoning for Effective GIS

by Joseph Berry, 1995