Transcript Map Information - UW Courses Web Server

Geographic Data and
Relationships
Outline
• Types of Geographic Data
– spatial data
– tabular data
– image data
• Acquiring Data
• Storing Geographical data
• Spatial Data Models and Structures
– Vector data model
• spaghetti
• topological data structures (concepts of topology)
– Raster data model
– Database Structures
• Referencing Spatial Data and Map Projections
Types of Geographic Data
• Geographic data: data that describes any part of
the Earth's surface or the features found on it such
as: cartographic data, scientific data, business
data, land records, photographs, customer
databases, travel guides, real estate listings, legal
documents, videos, etc.
• ArcView supports three types:
– Spatial data.
– Images.
– Tabular (Descriptive) data.
Data Types Supported by ArcGIS
Spatial Data
• Spatial data is geographic data that stores the
geometric location of particular features, along with
attribute information describing what these features
represent. Also known as a digital map.
– location data is stored in a vector or raster data structure.
– Corresponding attribute data is stored in a set of tables
related geographically to the features they describe.
– Location, shape, tables, and the rest of the attributes
together form what we call “spatial data”
Example of vector on raster data
Example of tabular data (attributes of measured highways)
Attributes of “Cities” in the “Topography” Data Frame
Spatial Data Format Supported
by ArcView
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ArcView shapefiles
ARC/INFO coverages
ARC/INFO GRID data
Image data
CAD drawings
SDE data (If Database Access is installed)
StreetMap data (If StreetMap is installed)
TINs (If 3D Analyst is installed)
VPF data
Example of themes imported from AutoCad by “Cad Reader”
in ArcView
Differences between Spatial Data
and Simple Vector Graphics or
Images
What is the difference between spatial data and a scanned
image or a CAD file??
1- In spatial data there is an explicit relationship between the
geometric and attribute information, so that both are always
available when you work with the data. For example, if you
select particular features displayed on a view. ArcView will
automatically highlight the records containing the attributes
of these features when the attribute table is displayed.
Streets selected on the map of SF are also selected in the table
2- Spatial data is georeferenced to known locations on the
Earth's surface.
Coordinates
3- Spatial data is organized thematically into different
layers, or themes. There is one theme for each set of
geographic features or phenomena for which information
will be recorded. For example, streams, land use, elevation,
and buildings will each be stored as a separate spatial data
sources, rather than trying to store them all together in one
layer.
4- Spatial data is primarily feature based. It is designed to
enable specific geographic features and phenomena to be
managed, manipulated and analyzed easily and flexibly.
1- Vector Data
• Usually constructed by digitizing a map or a photograph.
• Features are represented by pairs of Cartesian coordinates.
They can be points, lines, or polygons.
– Point features: are represented by discrete locations
defining a map object whose boundary or shape is too
small to be shown as a line or area feature. A special
symbol or label usually depicts a point location.
Examples of such features are wells and telephone poles
– Line features: are sets of ordered coordinates that, when
connected, represent the linear shapes of map objects too
narrow to be displayed by areas such as roads and
streams. Line features can also represent features that has
no width such as contours. Line features can be referred
to as arcs or links.
– Area features: an area feature is a closed figure whose boundary
encloses a homogenous area, such as a state or a water body
• Geographic boundaries often come with area and perimeter
calculated. Street data often include address ranges along
each street.
• Graphics can be used to represent attributes using symbols.
Roads can be drawn with different line widths or colors.
School location can be represented by a special symbol.
• In vector representation, each points is recorded as
a single x, y location. Lines (arcs, or links) are
recorded as a series of ordered x,y. Areas are
recorded as a series of x,y coordinates defining
arcs that enclose the area, first and last points are
the same in this case. Areas can also be defined by
the arcs around their boundaries, see figures.
• This way, features are stored in terms of pairs of
x,y coordinates instead of storing a graph.
• Multiple features are represented by assigning an ID
to each feature and list the pairs of coordinates against
feature ID.
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2- Tabular (Descriptive) Data
• Tabular data can include almost any data set, whether or
not it contains geographic data.
• Can be displayed:
– on the view directly (by hyper linking?); or
– as descriptive (attribute) data that GIS links to map
features.
• Can be linked to map features through unique ID_s of the
features
• Often comes packaged with featured data.
• May include description of locations by address or by
coordinates for example.
Description of locations in tables can be displayed
graphically. You can use a symbol to display the locations
of bird nests or airport locations for example
Often stored in abbreviated way. A data dictionary describes
the data not just full names. Very important to obtain a data
dictionary when acquiring descriptive data
• An ArcView map references the tabular data source it
represents, but doesn't contain the tabular data itself. This
means that tables are dynamic, because they reflect the
current status of the source data they are based on. If the
source data changes, a table based on this data will
automatically reflect the change the next time you open the
project containing this table. Data frames are also updated.
• Formats supported by ArcView:
– Data from database servers such as Oracle, Ingres,
Sybase, Informix, etc.
– dBASE III files
– dBASE IV files
– INFO tables
– Text files with fields separated by tabs or commas
3- Image Data
• Image data includes satellite images, aerial photographs,
and other remotely sensed or scanned data
• Image data is a form of raster data where each grid-cell, or
pixel, has a certain value depending on how the image was
captured and what it represents. For example, if the image
is a remotely sensed satellite image, each pixel represents
light energy reflected from a portion of the Earth's surface.
If, however, the image is a scanned document, each pixel
represents a brightness value associated with a particular
point on the document. ArcGIS refers to rasters as
“surfaces”
• Can be used as maps for analysis, background of a view or
a map display, or as attributes linked to features.
Aerial photograph used as a map in the background Notice
that locations of samples are displayed
Features can be drawn and displayed on top of images
• ArcView -without extensions- supports images
for display and attribute purposes only. They
cannot be used for analysis since they are not
feature based. In order to be able to create and
analyze image data, “Spatial Analyst” must be
added to ArcView.
• ArcMap can handle raster data deeper. Images
can be georefrenced and classified.
• Scanned images used as attributes can also
represent scanned text document such as permits.
A View or a
data frame
may
Contain a
single
photograph
Images can be “hot linked” or “hyper-linked” to features.
ArcView supports the following image formats as themes:
– ARC Digitized Raster Graphics (ADRG) (if ArcView's
ADRG Image Support extension is loaded)
– BMP
– BSQ, BIL and BIP
– Compressed ARC Digitized Raster Graphics (CADRG)
(if ArcView's CADRG Image Support extension is
loaded)
– Controlled Image Base (CIB) (if ArcView's CIB Image
Support extension is loaded)
– ERDAS
– GRID
– IMAGINE (if ArcView’s IMAGINE image extension is
loaded)
– IMPELL Bitmaps (Run-length compressed files)
– Image catalogs
– JPEG (if ArcView’s JPEG image extension is loaded)
– MrSID (if ArcView’s MrSID image extension is loaded)
– National Image Transfer Format (NITF) (if ArcView's
NITF Image Support extension is loaded)
– Sun rasterfiles
– TIFF
– TIFF/LZW compressed
ArcView supports hot linking to the following image formats:
(Notice that JPG is not supported in Version 3.1 but supported
in ArcGIS)
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–
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GIF (Graphics Interchange Format)
MacPaint
Microsoft DIB (Device-Independent Bitmap)
Sun raster files
TIFF (Tag Image File Format)
TIFF/LZW compressed
X-Bitmap (generated by ‘bitmap' utility on X Windows)
XWD (X Windows Dump Format)
Acquiring Data
• Certain considerations before acquiring data: (refer to
attached sheet)
– Area: consider an area that is not much larger and is not
smaller than the area under study
– Scale: the same feature is displayed differently at
different scales. Roads can be lines or areas. Schools can
be points or areas. Acquire data at a scale that fits your
needs.
– Time: some data change with time. If this is the case,
make sure you obtain data at the time you want to
consider.
– Accuracy: location of roads within 40 ft is OK for
traveling information but not for planning.
– Description: must obtain a data dictionary with the data, see
attached data dictionary
– Compatibility: the format of the data need to be supported by the
software you are using. If not, it can be used only if you have a
way of transforming the data into a format you can use.
• Sources of data:
1- Governmental agencies provide them for a minimum
charge.
2- UW libraries have a huge collection of all sorts of
data. Some of them are available on CD’s and some
are available online. Check the sites:
www.lib.washington.edu/maps/digdata.html
wagda.lib.washington.edu {data for King County, Seattle,
and WA state} wa-node.gis.washington.edu
3- Vendors:
all types of data at different scales are
available for purchasing.
4- The World Wide Web, became a very important
source for free data check (www.esri.com), see
attached sheet
5- Users:
users can create their own data by:
1- digitizing from maps or images
digitizing is the process of manually converting
hard copy maps into digital format for use by a
computer.
•Digitizers have their
own internal coordinate
systems, up to 0.025mm,
which may be related to
terrain coordinates by
cross-registering
At least three points with known terrain coordinates. After
registering the points, a cross hair or cursor is placed over
the position to be recorded and a key is pressed.
• Digitizing includes the entry of thematic
codes for object types and ID codes which
link the object type to attribute data. For
example, digitizing a building includes
entry of the thematic codes for buildings
and ID number for a building. A new ID
number is entered for the next building, and
so on.
2- drawing over maps or images
3- from tables: features can be mapped
based on their locations in tables, usually
using symbols. Address geocoding helps
translates addresses in tables into
coordinates for display on maps.
4- typing attribute tables.
Table of attributes
Storing Geographic Data
• A digital map database consists of two types of
information: spatial and descriptive data.
• The computer stores a series of files that contain
either type
• The power of GIS lies in its ability to link the two
types of data and maintain the spatial relationship
between the map features (what is next to what?)
• Tabular data can be accessed from the map and can
be used to create maps. For example, you can
change the classification (colors) according to
different attributes.
Tracts classified according to price
Tracts classified according to roof type
Tracts classified according to area
Representing Maps in the Computer
• Features are represented by (points, lines or areas) or cells.
• Features are referenced to ground locations through a two
dimensional flat Cartesian system.
• Spatial Data Models and Structures
– Vector
– Raster (surface in ArcGIS)
– Database (tables)
I- Vector Data Structures
Vector data usually come in one of two data structures:
A- Spaghetti
• digital map data with crossing lines, loose ends, open
shapes, double boundaries, etc. The data lie in a pile, just
like spaghetti, see attached figure.
• Takes large space to store and very hard to search
through.
• Not suitable for most GIS applications, no overlaying
possible for example.
B- Topological data structure:
Topology
• Topology is a mathematical procedure for explicitly
defining spatial relationships.
• Example of spatial relationships: the route from the airport
to a hotel.
• Using topological relationships, data can be stored more
efficiently and can be processed faster.
• Three major topological relationships:
– connectivity
– area definition
– contiguity
B.1. Connectivity
• Arcs connects to each others at nodes
• Points along the arc are called vertices, points at the end of
arcs are called nodes
• Each arc has two nodes: a from-node and a to-node.
• Arcs join only at nodes. That enables a GIS software to
identify which arcs meet (connect) at a certain point (node).
Consequently, the software can recognize that certain lines
are connected, see figure
• If two lines are connected, share the same node, then you
can travel from one to the other?
B.2. Area Definition
• Arcs that connect to surround an area
define a polygon
• Areas can be defined by sets of x,y coordinates.
• A more efficient way is to store the ID’s of the
arcs defining the area. That allows for storing the
arcs only once and insures that boundaries of
adjacent polygons do not overlap, see figure.
• In this case, we store a polygon-arc list and an arc
-coordinates list.
B.3. Contiguity
• Contiguity: arcs have directions, right and left
arc; and each arc has a direction from the fromnode to the to-node.
• A GIS software may store a left-right list which
defines the polygons on the left and right sides of
each arc, see figure.
• Polygons sharing a common arc are adjacent. The
left-right list describes the spatial arrangement of
the areas, which one is to the left?
• Other topological data structures, see figure.
• In summary, topological data structures
define how points, lines, and polygons are
related to each other on a map.
• This relationship is obvious to the human
eye, but needs to be explicitly defined to a
computer.
• Topological data structures may vary from a
software to another, but they usually carry
the same basic information.
Remarks about topological data structures
• The connections and relationships between the objects
are described. Their topology remains fixed as the
geometry is stretched and bent.
• Require that all lines be connected and all polygons be
closed. No double boundaries.
• Permit several spatial analysis such as overlaying,
network analysis, contiguity analysis, and connectivity
analysis
• Less storage space, faster display and search
• Take more time to construct and to update.
• The prime choice in most GIS
II- Raster (Grid) Data Model
• Reality is represented in terms of uniform, regular cells
(pixels: picture elements).
• Cells are usually rectangular or squares.
• Geometric resolution of the model depends on the
size of the cells.
• The location of the cell is described in terms of its
row and column. The numbering start at the top
left cell being 00.
• Cell locations in terms of rows and columns can
easily be transformed into a Cartesian system by a
two dimensional affine coordinate transformation
(Row,Column)
(X, Y)
Devils Tower, Wyoming
1:24,000 raster
visualization of DTM
DTM of ground under canopy
LIDAR images of the WTC by NOAA. Elevations are color coded
http://www.noaanews.noaa.gov/stories/s781.htm
Dark Green
-30 to 0
Green
0 to 98
Yellow
98 to 328
Magenta
328 to 492
Red
492 to 765
The 3-D models have helped to locate original support structures, stairwells, elevator
shafts, basements, etc.
• Cell values can represent many things: a gray level, a code for feature
types, or any other attribute.
• Figures 4.17 and 4.18
• A cell can be assigned a single value. That results in
different themes as with the vector model. Figure 4.19
• A single cell may cover parts of two or more objects or
values. A classification scheme is followed to assign a value
to the cell: average, largest, the one in the middle, etc.,
figure 4-18
• Spatial arrangements, topology in vector models, may be
achieved by a search of the neighboring cells, takes more
time.
• Storing raster data
– can be stored in the form of a table: location and value.
– many compression algorithms are available. Run-length
encoding simply stores the number of consecutive similar
cells: 4x 2w 3r 1x 3x, and so on. Figure 4.22
• Vectorisation and rasterisation
– transforming raster into vector or vector into raster format
respectively.
– can be done automatically within the software
– part of the data is lost in the transformation process, why? Figures
4-24, and 4-25
Vector VS Raster Models
• Raster models are superior in handling phenomena that
are related to areas and points while vector models
handle line-related phenomena better.
• Overlaying in particular is faster with raster models
• Raster models are easier to produce from hard copies by
scanning, vector models require digitization which is
time consuming.
• Raster models require larger storage space and more
powerful computing system.
• Raster models are more suitable for many presentation
purposes, DEMs for example are lot easier to visualize in
a raster format.
Overlay in vector representation
Overlay in raster
representation
III- Database Structures
• A data base is simply a collection of multiple files.
• A GIS is, first and foremost, an information system
• There is a need for an efficient data management system to
facilitate the integration and cross referencing between
different types of data.
1- Flat Files
– All the information about a feature are stored in a single
record. All rows have the same number of columns
which may result in empty cells.
– Search is done through a key field (attribute)
– A structure that results in a slow system that requires
huge storage. Fig 2.3 of Huxhold
2- Hierarchical Files
– More than one type of record in the data base (many tables)
– One record can be a parent of one or more records in another table through
pointers.
– One way relationship which reduces repetition of information.
– Each record can have one parent only.
– Very useful in one-to-many situations, ArcGIS will append the first maching
record only in that case.
– Allows limited linking process, pointers are pre-set in the design.
3- Networks
– Records are not necessarily unique. One feature having
more than one value of a certain attribute may show up
twice. For example, owners of many parcels.
– Pointers allow many-to-many relationship, one record
may have more than one parent. Still one way
relationship.
– Pointers may become very complicated and may
occupy larger space than the data itself. Figure 8-7 Tor.
4- Relational databases
– Allows related records from different files to be associated with
each other through a common attribute without pointers between
the records (the rows)
– They contain a group of flat files that can be related in any
direction.
– Tables can be joined to form new tables by choosing any common
field (column)
– Fast and flexible structure. Does not require large storage for the
links.
– Figures 2.5 and 2.6 of Huxhold.
– The most common data base structure in modern GIS software.
Definition: Relational Data Base
A data model based on set theory. Each set has elements that can be
uniquely defined by a primary key. A table (relation) stores all records
for a set. Each record in a table has the same columns for attribute
values. Relationships between tables are constructed by storing the
key to a record in the other table
Referencing Spatial
Data
• GIS data must be in the same system, based on the
same ellipsoid, and the same projection.
• We will look into:
– Vertical Reference in the US “elevations”
– Horizontal Reference
• Global Systems: Geodetic “ geographic” and
UTM
• Local system: State Plane Coordinate System
Shape of the Earth
• Geoid and Ellipsoid, what for?
• A geoid is a surface of equal gravity from which
elevations are measured, cannot be
mathematically defined easily.
• An ellipsoid is an approximation of a geoid to
produce a mathematically defend surface, based
on which horizontal coordinates can be defined.
Vertical Reference
“Elevations”
{Based on Geoids}
• Vertical Datum: A level surface to which
elevations are referred, for example: MSL. A
geoid “surface of equal gravity” that contains
large water bodies
• Elevation: the vertical distance from a vertical
datum to a point or an object.
North American Vertical Datum
• Started in 1850’s, first phase completed in 1929
• Thousands of Points across the US and Canada were
related to MSL and adjusted, the newly defined MSL
defined a new datum called: National Geodetic Vertical
Datum of 1929, or (NGVD 29)
• Due to the earth’s crust shifting and changes in MSL,
new adjustment was done and more points were added
(total of 1.3 million) which resulted in NAVD88
• Shifts are larger in the west: 1.5m in the Rocky
mountain area
• MUST MENTION WHICH DATUM
Horizontal Reference
{Based on Ellipsoids}
• The locations of map features are referenced to
actual locations of the objects they represent in the
real world.
• The positions of objects on the earth’s spherical
(ellipsoidal) surface are usually given in degrees of
latitude and longitude, also known as geographic or
geodetic coordinates.
• On a flat map, the locations of features are measured
in a two dimensional planner coordinate system.
Examples are state plane coordinate system or
UTM.
Map Projections
• Because the earth is round and maps are flat,
getting information from the curved to the flat
surface requires a mathematical operation called
map projection.
• We mathematically project data from the surface of
a certain ellipsoid to a flat surface we call a map.
• Map projection is a transformation process, it
transforms  and  into x and y coordinates.
• Flattening the earth result in distortions in:
distance, area. shape, and directions. All maps are
distorted in some of these spatial properties.
•Some map projections minimize distortions in one
property on the expense of another, while others balance
the overall distortions.
•All the data in a GIS database must be in the same map
projection. Better to store locations in unprojected
coordinates (decimal degrees)
Longitudes and latitudes are considered as a simple two
dimensional coordinate system
Conformal: scale is equal on all directions, parallels and
meridians drawn at right angles. Small areas and angles with
small sides are correct.
Equal-Area: areas are equal to those on ground. Maps cannot
be Equal-Area and conformal
Distances and directions along the center are correct. This
“Polar” map was projected onto a plan tangent at the
north pole
Which ellipsoid?
• Define ellipsoid parameters (equations not required):
•
•
semi-major axes (a), semi-minor axes (b)
e=  a 2  b 2 
= first eccentricity
b
•
2
N = normal length =
a
2
1  e
s in
2 
• Two main ellipsoids in North America:
Clarke ellipsoid of 1866, on which NAD27 is based
• Geodetic Reference System of 1980 (GRS80): on
which NAD83 is based.
• For lines up to 50 km, a sphere of equal volume can be
used
•
Horizontal Reference
Coordinate
Systems
{Based on Ellipsoids}
Global Systems
1- Geodetic “geographic”
2- UTM
1- Geodetic “geographic” Coordinate
System
• A global System that is defined anywhere on earth,
no distortions.
• Definitions :
– Geodetic latitude (): the angle in the meridian
plane of the point between the equator and the normal
to the ellipsoid through that point.
–
Geodetic longitude (): the angle along the equator
between the Greenwich and the point meridians
–
Height above the ellipsoid (h)
2- Universal Transverse Mercator (UTM)
• Preserves shapes.
• Based on the transverse mercator projection
• In zones that are 6 degrees wide, 3 in military
applications
• The unit of measure is meter
• Zones are numbered beginning with 1 for the zone
between 180W and 174W meridians. Zone numbers
increase to a maximum of 60.
• The latitude for the system varies from 80N to 80S.
• The origin of longitude is at the central
meridian
• False easting is 500,000m at central meridian.
• The origin of latitude is at the equator
• False northing is 0 for the northern
hemisphere and is 10,000,000 m for the
southern hemisphere.
• A global system used in USGS 1:250,000
scale quadrangle map series.
Coordinate
Systems
Local US Systems
State Plane Coordinate
Systems
“LOCAL” State Plane Coordinate
Systems
• Plane rectangular systems, why use them?
• How to construct them: Project the earth’s
surface onto a developable surface.
• Two major projections: Lambert Conformal
Conic, and Transverse Mercator.
Secants, Scales, and Distortions
• Scale is exact along the secants, smaller than
true in between.
• Distortions are larger away from the secants
How They Selected Projections?
• States extending East-west: Lambert Conical
• States extending North-South: Mercator
Cylindrical.
• A single surface will provide a single zone.
Maximum zone width is 158 miles to limit
distortions to 1:10,000. States longer than 158 mi,
use more than one zone (projection).
Standard Parallels & Central
Meridians
• Standard Parallels: the secants, no
distortion along them. At 1/6 of zone width
from zone edges
• Central Meridians: a meridian at the
middle of the zone, defines the direction of
the Y axis.
• The Y axis points to the grid north, which is
the geodetic north only at the central
meridian
Geodetic and SPCS
• Control points in SPCS are initially computed
from Geodetic coordinates.
• If NAD27 is used the result is SPCS27. If
NAD83 is used, the result is SPCS83.