e.g. please always turn off your cell phone or put it on vibrate mode for class

E.G. please always turn off your cell phone or put it on vibrate mode for class

A kind reminder on distractions such as phone use, noise (see syllabus). Please allow fellow students to focus.

GIS for environmental management Dr. William James Smith, Jr.

Assistant Professor of Environmental Studies, UNLVAdjunct Assistant Professor in Geography, The University of Iowa

Development and population animations

http://www.nrel.colostate.edu/~davet/animations.html

MAPS AS NUMBERS: Data structures, basic statistics and queries, and topology

Maps as numbers

Geographic InformationGeographic Information

ThemeTheme TimeTime SpaceSpace

NominalNominal

OrdinalOrdinal

IntervalInterval

RatioRatio

PointsPoints LinesLines AreasAreas VolumesVolumes

But that is not the main topic of discussion for this type of class

We will focus on data structures again and then connect them totopology

TopologyThe property that describes adjacency and connectivity of features.A topological data structure encodes topology with geocoded features.

We require a mental model of how the physical data represent a geographic figure.

Traditionally there have been two types of data model for map data.

1. Raster format2. Vector format

Raster modelUses a grid, like coordinate systems we discussed do, to hold the data.Each grid cell in the grid is one map unit, often selected so that each cell shows on the GIS map as one screen display point or pixel, or on the ground as a whole number increment in the coordinate system.

5

When we capture a map into the raster model we have to assign a value toevery cell in the grid.

The value we assign can be an actual number such as terrain elevation in a DEM, or an index value standing for an attribute stored in a database.

The cell size determines the resolution of the data and the cell size has both a ground and map expression.

On a map we may use several pixels to display the grid cell, or on paper we may use a dot of a certain size in a given color.

Resolution

• Resolution increases as the size of the cell decreases; however, coding, database storage, and processing speed for analysis are more costly.

Composition of a Raster data set

• Raster is made up of cells• Cells have coordinates • Cells have attributes

– Integers: categorical or nominal values

– Floating point


• Each cell is assigned a specific value to identify or describe the class, category, or group the cell belongs to (categorical data).

• Examples of categorical data include soil type, soil texture, landuse class, water body type, road class, and housing type.

Data dictionary: The part of a database containing information about the files, records, and attributes rather than just the data.

Attributes have a database dictionary of sorts, this lists of attributesand their characteristics.

Sometimes its is a separate file(s) and other times it is written in to the top or header of the file before the data begin.

Digital Chart of the World hard copy data dictionary

E.G. DEM hack if time

We often speak of Landsat imagery being 30 meter. This means that each cell in the data is 30 meters by 30 meters on the ground.

YOU CAN ZOOM IN ALL YOU WANT BUT IT DOES NOT IMPROVE BEYOND THAT! E.G. people do not show up.

Foreshadow - FSM 30, 4, 1 at same spot different things show up.

Zoom in on volcano tiff from last class to look for trash


• Raster organized in rows and columns

• Upper left corner defines origin

• Spatial extent by origin and cell size

• Again, resolution is a matter of cell size

Discrete v Continuous Models

• Discrete • Continuous

Representing features in a raster dataset - areas

• The accuracy of the raster representation is dependent on the scale of the data and the size of the cell. The finer the cell resolution and the greater the number of cells that represent small areas, the more accurate the representation.

Earth at night

There must be a link between the raster or vector and the flat file.

-For a raster grid we could store index numbers in the grid and anynumber of attributes for the index numbers of attributes in the flat file.LAND USE MAP1=FOREST2=FARMLAND3=URBAN

See DCW -- if raster data O.K. there

Raster data structuresEach pixel or grid cell contains either a data value (e.g. “brightness”)for an attribute, or an index number that points to a reference in the attribute database. Again, pixel =s 2 and then 2 always = oakforest anywhere on the map, then that pixel is oak -- and patterns manifest.

To write numbers to a file start with writing attribute codes, the number of rows and columns and maximum size of value possible, then convert tobinary in all rows and columns.

Advantages- An operation such as comparing a grid cell with its neighbors can beperformed by looking at the values in the next and preceding row and column of the grid cells in question.

- TIN structure in vector fit easily in raster.

- Remote sensing or scanning.

Disadvantages- Not good at representing lines or points since each become a wholecell in the grid -- making lines disconnected or fat if they cross the gridat a shallow angle.

- Mixed pixels are a problem page 76 CLARKE, as sometimes more thanone type of attribute occupies a cell, and then if there are two classes anedge pixel is assigned, but when lots of types exist in the cell we need toassign computational rules, such as the majority attribute takes the wholecell

Composition of a Raster data set• Raster data can be organized into zones of cells with similar values.• Each group of connected cells in a zone is considered a region.

Legend

Mixed conifer

Douglas fir

Oak savannah

Grassland

Raster representation. Each color represents a different value of a nominal-

scale field denoting land cover class.

We need to decide what patterns are legal, e.g. diagonal vs. cardinal.

Each grid cell can usually only be owned by one feature, that is, the one whose attribute it holds. But that is not the real world!

Soils for example, are often listed by percent of sand, silt and clay. And the grid may be 49 percent water and 51 percent land, but the cell must be one.

DEMO Lat/long over counties to make grid of land water

See why lots of cells at high resolution is best?

Each cell must be able to hold maximum data possible, so as the rows Xcolumns space is eaten up fast.

Representing features in a raster dataset - points

• Point data• Point data have area depending on resolution or cell size

(centroid)

Representing features in a raster dataset - lines

• As with point data, linear features will become the width of the cell.

DEMO CENTROIDS FOR BASINS AND TAXPARCELS & LINE TO RASTER ELE exts in topology pjr

Last word, you can convert from vector to raster easily, but the other way makes is more complicated. Either way you need to avoid problems likelines overlapping (e.g. streams).

Digital Orthophoto Quad

Typical sourcesof local data you might use.

Digital Raster Graphic

Surfaces -- some examples

What are TINs?

TIN: A vector topological data structure designed to store the attributes, usually geographic surfaces. Uses contiguous non-overlapping triangle facets.

Not as efficient as grids, but good for detailed large scale apps and better for position of roads, etc. (as triangle edges) than grids.

But needs relatively better data to be worth using (e.g. not for DEMs at U.S. scale).

See Using AV 3D pp 24-25 figs

Digital Elevation Models grid elevation sources

DefinitionsMore TINs and other modelslater in the semester -- not on your midterm.

DEMOELE TO GRIDTO TIN

Volumes (surfaces) are structured with the TIN model, including edge or triangle topology.

TINs use an optimal Delaunay triangulation of a set of irregularly distributed points.

TINs are popular in CAD and surveying packages.

Chuuk Demo in topology vector only view with DEM

Disadvantages include high costs and processing power / time

More on using such data to drape, for flow, aspect, slope, visibility/view-sheds, watersheds, etc. later

And more on querying and exploring their relationships and making calculations to produce maps later

Continuous Surface Models• Concentration • Diffusion

Satellite Imagery

Online rastser only GISgrid outputs with cells user defined

• http://www.ngdc.noaa.gov/mgg/gdas/gd_designagrid.html

http://www.nrel.colostate.edu/~davet/animations.html

Positive aspects-They are easy to understand.-Capable of rapid retrieval and analysis -Easy to draw on a screen and devices that display pixels.

Chuuk or NV topo

Ways around the raster data hog problem

Run-length encodingIt looks at CHANGES between attributes and thenumbers of them stored.

If a whole row is a class,then it is stored as such, and not lots of cells individually.

Not much help if the rasterimage is quite varied!

Quad tree can help tooDivides the grid into 4 quadrants, saving a reference to a given quadrant only if it hosts the data desired. And that quadrant is also divided, and so on till one quadrant has all of the same pixels desired.

Less resource intensive technique utilizing vector topology, ratherthan raster topology, to get at a very similar environmental issue

Interpolation

• Interpolation predicts values for cells in a raster from a limited number of sample data points. It can be used to predict unknown values for any geographic point data: elevation, rainfall, chemical concentrations, noise levels, and so on.

On white board - show points, leading to interpolation, leading to points to lines to raster

or

Demo: vector only topology view add points & add lat/long for wq testing

orDEMO interpolation later when discussing renewable energy

Some positive aspects of the vector model include:

-Very good at representing features shown on maps as lines e.g. roads

-Unlike raster, we do not have to store grid cells of no interest. Size!

-We only place the amount of data really necessary to make the shapee.g. more for bends in a river, less where straight.

Some disadvantages

-Not good at representing continuous field variables e.g. topography,though Triangulated Irregular Networks get around this.

Practical environmental application

Threats Posed by Perchlorate Contamination:

The Hills Site, Johnson County, Iowa

Warren Pierce

University of Iowa Student

Dr. Smith’s

Socio-Political Aspects of Water Resources Management GEOG 605

12/15/03

Geographic Information Systems• Plume size

• Plume concentration

• Aerial photographs

• Field samples

• Ground/surface water

• Determining areas at risk

• Locating point source

The Plume Has A Volume of Approximately 2,750,000 Cubic

Meters • A home on Main Street in Hills

• Very nearly in the center of the plume

• Is that a for sale sign?

Aerial Photo of Hills

• Triangle• ~1000m

base• ~1100m

length• ~5m

height

Source: ArcScene 3D Interpolation Snapshot

Tetra Tech EM Inc.

Hills Perchlorate SiteHills, IowaFigure 1

15 ft. to 18 ft. Below Ground Surface

11

/05

/20

03

o:\

hill

s_ia

\pro

ject

s\fig

ure

s\ro

un

d2

\r2

_15

_to_1

8ft

.pp

t

Date: 11/05/2003 Drawn By: Kurt Cholak Project No: G9011.L.03.0131.00

Legend0.01 to 4.00 ppb4.01 to 11.00 ppb11.01 to 18.00 ppb

Greater than 18.00 ppb

This could be done in 3-D with depth of plume


Tetra Tech EM Inc.



11

/05

/20

03

o:\

hill

s_ia

\pro

ject

s\fig

ure

s\ro

un

d2

\r2

_18

_to_2

1ft

.pp

t





Tetra Tech EM Inc.



11

/05

/20

03

o:\

hill

s_ia

\pro

ject

s\fig

ure

s\ro

un

d2

\r2

_21

_to_2

4ft

.pp

t




A pound of perchlorate is distributed throughout the plume

• Photo taken at the Atrium Village

• The plume engulfs this area

• Hopefully there is close monitoring


Tetra Tech EM Inc.



11

/05

/20

03

o:\

hill

s_ia

\pro

ject

s\fig

ure

s\ro

un

d2

\r2

_24

_to_2

7ft

.pp

t




Downstream Communities

• The plume has reached the Iowa River

• The first town downstream is River Junction

• A second river then runs past Riverside


Tetra Tech EM Inc.



11

/05

/20

03

o:\

hill

s_ia

\pro

ject

s\fig

ure

s\ro

un

d2

\r2

_27

_to_3

1ft

.pp

t




Bioaccumulation

• Plant biota readily absorbs perchlorate

• Concentrations increase

• Biomagnification occurs moving up trophic levels

• MCL levels for drinking water does not account for ingestion through food

Lucky FarmsSan Bernardino, California

• Lockheed Martin (aerospace and defense contractor)

• Contaminated much of Southern California

• Irrigation contains perchlorates

• Concentrations in vegetables 65 times greater than irrigation water

Detrimental to Farming

• Products not safe to sell• Decreased income from

ruined produce• Money lost from product

input• Labor costs• FDA

Bioaccumulation in Aquatic Biota•Plants

•Fish

•Amphibians

“Microbial (algal and bacterial) and fish tissues accumulated perchlorate to high levels and the magnitude of accumulation corresponded well to exposure levels.

Therefore, it is likely that even exposures to relatively low concentrations of perchlorate(<20 ppm) will result in the bioaccumulation of perchlorate throughout the food chain.”

---U.S. Air Force Space and Missile Center report

Stakeholders Hills Residents

Stutsman, Inc. Employees

Government

Downstream Communities

Environment

Developers Farmers

Stutsman, Inc Owners

Development

• Six homes and two lots for sale

• Property values drop• New development

stymied• Lose tax revenue• Environmental justice

Conclusion

• The EPA must set MCL levels for perchlorate• Regulation standards for wells• Chance findings of contaminations not good

enough• Water treatment facility needed• General safety and welfare top priority• Those responsible held accountable• GIS excellent tool• Bioaccumulation must be of concern

Do ch 13 and 14c demo, if time, come back start topology.

VECTOR TOPOLOGY

TopologyThe property that describes adjacency and connectivity of features.A topological data structure encodes topology with geocoded features.

Vector topology diagram

e.g. river or not

There are lots of formats for GIS data such as TIGER, DLG, DFX, etc.But we do not have time to get into this now.

You will have to learn them if you get into advanced use of them.

Data exchange by translation (export and import) can lead to significant errors in attributes and in geometry.

This includes exchanging data between formats.

The Spatial DataTransfer Standard (SDTS) is the national standard.

SDTS became a federal standard (FIPS 173) in 1992.

SDTS contains a terminology, a set of references, a list of features, a transfer mechanism, and an accuracy standard.

Efficient data exchange is important for the future of GIS.

The history of GIS data exchange is chaotic and has been wasteful.

Topology Matters

Rarely are maps topologically clean when digitized or imported.

A GIS has to be able to build topology from unconnected arcs.

The tolerances controlling snapping, elimination, and merging must be considered carefully, because they can move features, or, e.g. set polygon size too big and lose small features.

Complete topology makes map overlay feasible.

Nodes that are close together are snapped -- averaging their x,y coordinates and closing polygons .

Slivers due to double digitizing and overlay are eliminated.

Mathematical topology assumes that geographic features occur on a two-dimensional plane.

We can ensure spatial features can be represented through:

1) Nodes (0-dimensional cells);

2) Edges, sometimes called arcs (one-dimensional cells); or

3) Polygons (two-dimensional cells).

Because features can exist only on a plane, lines that cross are broken into separate lines that terminate at nodes representing intersections rather than simple vertices.

This is more complicated than raster topology, so I will come back to my points multiple ways and times in this PPT to get the info across.

Vector data: arc/node model emerged in the 1960s as a nested hierarchy.

In the arc/node model, an area/polygon consists of lines -- and a line consists of points.

Points, lines, and areas can each be stored in their own files, with links between them. At least 2 dimensions of x left-right and y up-down.

pen

The topological vector model uses the line (arc) as a basic unit of storage. Areas (polygons) are built up from arcs as needed.

The endpoint of a line (arc) is called a node. Arc junctions are only at nodes.

Stored with the arc is the topology.

For a point or set of points, vectors just use a list of coordinate.

The file of arcs has a skeleton of just the first and last points in the arc (end nodes), and information as to its neighbors, so it can identify the # of the next connecting arc and polygon numbers to the left and right.

This means a polygon can be drawn by extracting all the arcs of its neighbor.

This avoids the sliver problem when digitizing info. FIG 3.5 P 74 Clark and P 79 FIG 3:10

vsDigitizing creates sliver

N1

N2

12

3A B N1 N2

x,y x,y

Arc 1

Data structure as the theory evolved

In GIS, topology is implemented through data structure.

E.G.ArcInfo “coverage” explicitly stores topological relationships among neighboring polygons in the Arc Attribute Table by storing the adjacent polygon IDs in the LPoly and RPoly fields.

Adjacent lines are connected through nodes, and this information is stored in the arc-node table.

Not every GISapproaches this the same way -- E.G. how you “build” topology.

Topological data structures are advantageous because they:

1) Provide an automated way to handle digitizing and editing errors and artifacts;

2) Reduce data storage for polygons because boundaries between adjacent polygons are stored only once;

3) Enable advanced spatial analyses such as adjacency, connectivity, and containment.

*Another important consequence is that a map that has topology contains space-filling, nonoverlapping polygons.

Automated method to clean up data entry errors and verify data.

The typical digitizing procedure is to:1) Digitize all lines;2) Build topology; 3) Label polygons;4) Clean up slivers, dangles, and under- and overshoots; and5) Build topology again, repeating the clean and build phases as many times as necessary

Government has made its contributions, e.g.

The U.S. Census Bureau, while preparing for the 1970 census, pioneered the application of mathematical topology to maps to reduce the errors in tabulating massive amounts of census data.

Today, topology in GIS is generally defined as the spatial relationships b/n adjacent or neighboring features.

Moreover, polygon features in shapefile format can contain one or more parts.

E.G, an individual parcel that is split by a road can be represented alternatively as two separate polygons with two shapes (rings) and two records in the attribute table or as one polygon with two parts and one record in the attribute table.

Same themecovered, but attributetable organizeddifferently

Why this form?1) Fast drawing (used to be like ArcExplorer2) Easy to copy3) No complex import/export concerns4) Easy to support in other software programs

By approaching digitizing from a feature-centric perspective and enforcing planar topology when each feature boundary is digitized and labeled, sliver polygons, dangling nodes, missing labels, and multilabeled features would be eliminated.

(Computer hardware was not always powerful enough to support a feature-centric digitizing approach that requires on-the-fly calculation of geometric intersections.)

skip

Your volcano digitizing!

Today's computers are powerful enough to support feature-centric digitizing for most GIS users.

ArcView GIS supports feature-centric digitizing through the Append Polygon, Split Polygon, and Split Line tools.

With these tools users can add a polygon (or line) adjacent to an existing polygon and have boundaries match perfectly.

ArcView GIS also supports topological editing of shared boundaries or nodes through the manipulation of vertices.

File Sizes No Longer an Issue

An oft-cited advantage of topological data structures is smaller file sizes because shared vertices of adjacent polygons are not stored twice.

Attribute tables are often a large proportion of the overall file size but are the same size regardless of how feature geometry is stored.

Moreover, although storage was often an important consideration in the past, the current low cost of storage means that for most GIS users storage space is not a constraint.

A misunderstanding about shapefiles is that because topology is not explicitly stored, adjacent features cannot be found.

However, adjacent features can easily be found by intersecting target polygons with other polygons in the same map and identifying the points of intersection of polygons that touch boundaries or overlap.

The geometric intersections of adjacent features are calculated on the fly by comparing the vertices of adjacent features rather than looking up adjacent features in a table skip

E.G. Demo using Ch. 13 parcel dataNeighboring parcels of a parcel:1) Select the parcel;2) Choose Theme;3) Choose Select by Theme from the View menu; 4) Choose "intersect" from the drop-down box; and5) Click on New Set to select all the parcels immediately adjacent to the originally selected parcel.

More complex adjacency analyses can be accomplished by combining the selection by theme with a query for specific attributes

Weakness(Some of the more complex adjacencies that involve direction (e.g., find the adjacent parcels to the east of a given road) are much more difficult to accomplish without stored topology.)

Although analytical operations that require adjacency information can be performed in AV through the interface, performance requirements many necessitate building a table to store adjacency information.

Algorithms for building lists of adjacent features, described here, could be incorporated in an “Avenue” script.

E.G. Although the representation of topological spatial relationships traditionally has been restricted to exactly adjacent neighbors, adjacency can be extended to include features that are within some distance (D) rather than exactly adjacent (D = 0).

skip

E.G. Although the representation of topological spatial relationships traditionally has been restricted to exactly adjacent neighbors, adjacency can be extended to include features that are within some distance (D) rather than exactly adjacent (D = 0).

One advantage can be defined in relation to the spatial precision of the coordinates. These algorithms can find adjacency for polylines and polygons. If D > 0, adjacent points can also be identified.

One algorithm creates an adjacency list using a "brute-force" approach. In this algorithm, for every pair of features, it determines if these features intersect and stores the adjacent index values

A planar-enforced shapefile can be created as described above or derived from a coverage.

However, if nontopological editing methods are used, a shapefile can lose its planar topology during editing.

E.G. If volcano digitizing “features” overlap each other and create gaps in your!

Planar topology, based on hand “heads-up” digitizing can be enforced on shapefiles with the assistance of some Avenue scripts that close gaps between features etc. using algorithms

(The logic and the scripts can be downloaded from ArcUser Online or http://www.nrel.colostate.edu/~davet/.)

Some purists might be offended at the notion that there are advantages to using shapefiles that violate planar assumptions (i.e., shapefiles that have overlaps and/or gaps).

Many useful analyses do not require data with precise planar topology, but these analyses are never conducted because it is assumed that base data must have topology.

E.G. city and county governments find it extremely time-consuming and difficult to build parcel coverages because parcel boundary descriptions rarely match cleanly with adjacent parcels. Resolving boundary disputes is very time-consuming, often fraught with complicated legal issues.

However, a standard query of parcel data performed with reasonably coincident boundaries (i.e., submeter accuracy) can be used to find landowners wn a certain distance of a location for notification purposes.

The first step in enforcing planar topology in a shapefile is to remove twisted or self-intersecting polygon rings and to ensure that the "inside" of the polygon is on the correct side of the polygon boundary. Next, gaps are identified by creating a rectangle that encompasses all the polygons of interest and serves as a backdrop. The polygons are subtracted from the rectangle containing all polygons. The remaining areas are gaps. A gap polygon is removed by merging it into an adjacent polygon or by making it a legitimate polygon. Overlaps are found by intersecting each polygon with all other polygons. If an intersection is found, then the polygon representing the overlap is created. Overlaps can be removed by deleting the overlapping area from one of the involved polygons. Once boundary changes have been made, the area and perimeter of each polygon should be recalculated.

Conclusion

The standard notion of topology in GIS centers around explicit representation of adjacent spatial relations and involves planar enforcement of geographic features. Although shapefiles do not explicitly store spatial relations, they can conform to planar enforcement. If, during map production or editing, planar enforcement is violated, then statistical summations that assume space-filling polygons could be inaccurate.

Although this may be heresy to many users, there are advantages to using shapefiles that violate planar assumptions (i.e., shapefiles that have overlaps and/or gaps). Many useful analyses do not require data with precise planar topology, but these analyses are never conducted because it is assumed that base data must have topology. For instance, city and county governments find it extremely time-consuming and difficult to build parcel coverages because parcel boundary descriptions rarely match cleanly with adjacent parcels. Resolving boundary disputes is a very time-consuming process, often fraught with complicated legal issues. However, a standard query of parcel data performed with reasonably coincident boundaries (i.e., submeter accuracy) can be used to find landowners within a certain distance of a given location for notification purposes.

Though the advantages previously attributed to topological data structures have become less clear, in large part because of improvements in computer performance, the bottom line is that GIS users need to adequately understand the data structures and use them appropriately.

Skip

I will not go over how to “enforcing planar topology” in AV GIS at this stage

Conclusion

The standard notion of topology in GIS centers around explicit representation of adjacent spatial relations and involves planar enforcement of geographic features. Although shapefiles do not explicitly store spatial relations, they can conform to planar enforcement. If, during map production or editing, planar enforcement is violated statistical summations that assume space-filling polygons may be inaccurate.

Although this may be heresy to many users, there are advantages to using shapefiles that violate planar assumptions (i.e., shapefiles that have overlaps and/or gaps). Many useful analyses do not require data with precise planar topology, but these analyses are never conducted because it is assumed that base data must have topology. For instance, city and county governments find it extremely time-consuming and difficult to build parcel coverages because parcel boundary descriptions rarely match cleanly with adjacent parcels. Resolving boundary disputes is a very time-consuming process, often fraught with complicated legal issues. However, a standard query of parcel data performed with reasonably coincident boundaries (i.e., submeter accuracy) can be used to find landowners wn a certain distance of a given location for notification purposes.

Though the advantages previously attributed to topological data structures have become less clear, in large part because of improvements in computer performance, the bottom line is that GIS users need to adequately understand the data structures and use them appropriately.

skip

References with an emphasis on ArcView -- not including the last few years of ArcGIS

Further Reading

Cooke, Donald F., and William H. Maxfield. "The Development of a Geographic Base File and Its Uses for Mapping," Proceedings of the Fifth Annual Conference of the Urban and Regional Information Systems Association, pp. 207-218, 1967.

Corbett, James P. Topological Principles in Cartography, Technical Paper 48, United States Department of Commerce, Bureau of the Census: Washington, D.C., 1979.

ESRI Shapefile Technical Description White Paper, Environmental Systems Research Institute, Inc.: Redlands, CA, 1998.

Reed, Carl. "GIS Users Shouldn't Forget About Topology," GeoWorld, Vol. 12, No. 4, p. 12, April 1999.

Strand, Eric J. "Shapefiles Shape GIS Data Transfer Standards," GIS World, Vol. 11, No. 5, p. 28, May 1998.

Theobald, David M. “Understanding Topology and Shapefiles.” Colorado State University, ArcUser April-June 2001 .http://www.esri.com/news/arcuser/0401/topo.html

We will discuss advances in the new ArcGIS system in this area if we have

time at the end of the course

END


• Integer (discrete or categorical) raster datasets usually have an attribute table associated with them.

• Floating-point (continuous) raster datasets usually do not have a table associated with them because most, if not all cell values are unique, and the nature of continuous data excludes other associated attributes.

Spatial Autocorrelation

GISs have to store digital maps somehow!

The map, like the attributes, is eventually broken down into a sequence of numbers stored in the computer files. We will not ddiscuss this as computer programmers would - but we need general awareness at this stage.

In general 2 ways are used to store.

1. Each number is saved in the file encoded into binary digits or bits.

2. Treating each number like humans do -- one decimal digit at a time,this format is called text or ASCII files. These files can be hacked, printed and read without a program.

e.g. please always turn off your cell phone or put it on vibrate mode for class

Documents

map data

data structures

physical data

raster data seteach

grid cell

cell size

raster data setraster

examples of categorical