GIS DATA IN.pptx

Geographic Watershed Information System/ArcHydro – 1 February 2008
1

GEOGRAPHICAL
INFORMATION SYSTEM
2

AIM
3
To apprise the house about GIS, historical
background and recent developments.
Geographical Information

SEQUENCE
4
 Introduction
 System Description
 Historical Background
 Recent Developments
 Recommendations
 Conclusion
 Q&A

INTRODUCTION
5
What is GIS?
Geographical
Information
System

INTRODUCTION
6
 Information Management System
Cont’d…

INTRODUCTION
7
 Capture
Cont’d…

INTRODUCTION
8
 Analyze
Cont’d…

INTRODUCTION
9
 Spatial Information
Cont’d…

INTRODUCTION
10
 Spatial Information
What is Where When!
Cont’d…

INTRODUCTION
11
 Better decisions using geography
Cont’d…

INTRODUCTION
12
 Utilities Organizations
 Police Forces
Cont’d…

SYSTEM DESCRIPITION
13
 Geographic Data : Fuel for GIS

SYSTEM DESCRIPITION
14
 Geographic Data
 Analog
Cont’d…

SYSTEM DESCRIPITION
15
 Geographic Data
 Digital
Cont’d…

DATA CAPTURING TECHNIQUES
16
 Map Scanning
Cont’d…

DATA CAPTURING TECHNIQUES
17
 Aerial Photography
Cont’d…

TYPES OF DATA MODEL
18Geographical Information
GIS Data Model
Raster Model Vector Model
Cont’d…

HISTORY OF GIS

HISTORY OF GIS
20
 1854
 Dr John Snow
 London
Cholera
Outbreak
Cont’d…

HISTORY OF GIS
21
 Early 20th Century
 Photozincography
 Layered Maps
Cont’d…

HISTORY OF GIS
22
 1960
 First GIS
 Dr Roger Tomlinson
 Canada Geographic
Information System
(CGIS)
Cont’d…

HISTORY OF GIS
23
 1964
 Howard Fisher
Cont’d…

HISTORY OF GIS
24
 1964
 Laboratory for Computer
Graphics & Spatial Analysis
Cont’d…

HISTORY OF GIS
25
 1970s
 SYMAP
 GRID
 ODYSSEY
Cont’d…

HISTORY OF GIS
26
 End of 20th Century
Cont’d…

HISTORY OF GIS
27
 Early 20th Century
 Open Source GIS
 Service based GIS
Cont’d…

RECENT DEVELOPMENTS
28
 Reduced GIS production time

RECENT DEVELOPMENTS
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 Field Data Collection Devices
 Range
 Azimuth
 Inclination
 Height Difference
Cont’d…

RECENT DEVELOPMENTS
30
 Field Data Collection Devices
 Binoculars
 Laser Rangefinder
 Digital Compass
 Inclinometer
Cont’d…

RECENT DEVELOPMENTS
31
 3D Scanning
Cont’d…

RECOMMENDATIONS

CONCLUSION
33
 Change in Emphasis
Past Present / Future
Analysis
Attribute
Tagging
Data
Conversion
Analysis
Attribute
Tagging
Data Conversion

CONCLUSION
Cont’d…

CONCLUSION
35
Man must rise above the earth to the top of the
universe and beyond, for only then will he fully
understand the world in which he lives…
Socrates
Cont’d…

GIS Fundamentals/
Geographic Database Design

GIS Concepts
 Information cycle:
 Data/Information/System/Information System
 Geographic Information System
 Main Components/Characteristics
 Geographic Database
 Data Modeling
 Data Representation
 Spatial Analysis
 Implementing a GIS

Information Cycle
Territory
Information Decision
DSS
GIS
Data

Data / Information
 Information is the result of interpretation of relations
existing between a certain number of single elements
(called data).
 Example:
The Museum located at 5th Avenue, NY,
was built in 1898.
 Data: Museum, address, year of construction.

System
 A system is a set organized globally and comprising
elements which coordinate for working towards doing a
result.
 Example: Water supply system
Elements: pipes, valves, hydrants, water meters,
pumps, reservoirs, etc.

Information System (IS)
 An Information System is a set organized globally and
comprising elements (data, equipment, procedures,
users) that coordinate for working towards doing a
result (information).

GIS: “G” & “IS”
Definition:
A GIS is a collection of computer hardware and software,
geographic data, methods, and personnel assembled to
capture, store, analyze and display geographically
referenced information in order to resolve complex
problems of management and planning.

GIS Components
Input Output
•Maps
•Census
•Field Data
•RS Data
•Others
• Reports
• Maps
• Photo.
Products
• Statistics
• Input Data
for models
Data
Capture
Storage
Manipulatio
n
Analysis
Display
GIS
Models
Other GIS
User
Interface
Geographic Data Geographic Information

GIS: Main Characteristics
 Integration of Multiple data:
- Sources
- Scales
- Formats
 Spatial Analysis

GPS/ air photos/
satellite images
Census
/
Tabular
data
Picture &
Multimedi
a
Maps
Data from multiple sources-at multiple scales-in multiple
formats

• To integrate geographic data from many different
sources, we need to use a consistent spatial referencing
system for all data sets
Referencing map features: Coordinate systems & map
projections

The Latitude/Longitude reference system
• latitude φ : angle from
the equator to the
parallel
• longitude λ : angle from
Greenwich meridian

Map Projections
 Curved surface of the earth needs to be “flattened” to be
presented on a map: Map Projection
 Projections are classified according to which properties
they preserve: area, shape, angles, distance
 Some distortion is inevitable:
 Less distortion if maps show only small areas, but large if
the entire earth is shown

UTM: Universal Transverse Mercator
 Minimal distortions of area, angles, distance and shape
at large and medium scales
 Very popular for large and medium scale mapping (e.g.,
topographic maps)
• Cylindrical projection with
a central meridian that is
specific to a standard UTM
zone
• 60 zones around the world

The concept of scale
 Scale is the ratio between distances on a map and the
corresponding distances on the earth’s surface
 e.g., a scale of 1:100,000 means that 1 cm on the map
corresponds to 100,000 cm or 1 km in the real world
• Small scale: small fraction such as 1:10,000,000 shows
only large features
• Large scale: large fraction such as 1:25,000 shows great
detail for a small area
• “small scale” vs “large scale” often confused

Multi-scale
 The same feature represented in different scales.
 Example: lake
Large scale
(1:25.000)
Small scale
1:500.000

Multi-formats
• Raster
• Vector
• Raster-Vector-Raster
• DXF-DGN-etc.
• Shapefile
• KML
• Etc.

Geographic Database
 Geographic Data
 Characteristics/Examples
 Definitions:
 Entity/Attribute/Dataset/Database
 Data Modeling
 Spatial representation
 Vector/Raster
 Topology

Descriptive Data vs Geographic Data
 Descriptive Data:
 Descriptive attributes
 Geographic Data:
 Descriptive attributes
 Spatial attributes
 Location
 Form

Geographic Data Characteristics :
Position:
explicit geographic reference
 Cartesian coordinates :X,Y,Z
 Geographic coordinates (lat, log)
implicit geographic reference
 Address
 Place-name
 Etc.
Geometric Form:
 ex: a polygon representing a parcel of land

Example1: Parcel of land
• Attribute (descriptive) Data
• Landowner
• Area
• Etc.
• Spatial data
• Position
• Located at 100 Nelson Mandela Ave
• X= a; Y=b within system (X,Y)
• Form
• dimensions (sides and arcs, constituting a polygon)

Example 2: District
 Attribute (Descriptive) data:
 District-Code
 District-Name
 Population 1990
 Population 2000
 Population 2010
 Spatial data:
 Geographical Position
 Polygon

Spatial entity
 We use the term entity to refer to a phenomenon that can not
be subdivided into like units.
Example: a house is not divisible into houses, but
can be split into rooms.
Others: a lake, a statistical unit, a school, etc.
 In database management systems, the collection of objects
that share the same attributes.
 An entity is referenced by a single identifier, perhaps a
place-name, or just a code number

Attribute
 Each spatial entity has one or more attributes that identify
what the entity is, and describe it.
Example: you can categorize roads by whether they are
local roads, highways, etc; by their length; their width;
their pavement; etc.
 The type of analysis you plan to do depends on the type of
attributes you are working with.

Dataset
“A dataset is a single collection of values or objects
without any particular requirement as to form of
organization.”
Example: Streets, rivers, cities, etc.

Geographic Database
 “A geographic database is a collection of spatial data
and related descriptive data organized for efficient
storage, manipulation and analysis by many users.”
 It supports all the different types of data that can be used
by a GIS such as:
 Attribute tables
 Geographic features
 Satellite and aerial imagery
 Surface modeling data
 Survey measurements

Data Modeling
 Data Modeling is the process of defining (geographic
features) to be included in the database, their attributes
and relationships, and their internal representation in the
Database. It involves the development of conceptual,
logical and physical models of the geographic Database.
 The outcomes include a Data Dictionary

Modeling Process
Reality
Geographic
Database
Modeling
(data & treat.)
Abstracting the Real World

Conceptual Model
Logical Model
“Real
World
”
Physical Model
External Model 1
Different users have different
views of the world
ANSI/SPARC: Study
Group on Data Base
Management Systems
(1975)
External Model 2 External Model 3

Conceptual Model
 A synthesis of all external models (user’s views).
 Schematic representations of phenomena and how they
are related.
 Information content of the database (not the physical
storage) so that the same conceptual model may be
appropriate for diverse physical implementations.
 Therefore, the conceptual model is independent from
technology.

Conceptual Model (cont.)
• Easy to read
• Conceived for the analyst or designer
• Objective representation of the reality, therefore
independently from the selected GDB System
• One conceptual model for the Database

Data Logical Model & Physical Model
 We transform the conceptual model into a new modeling
level which is more computing oriented: the logical
model (Example: the Relational Database approach)
 We transform the logical model into an internal model
(physical model) which is concerned with the byte-level
data structure of the database.
 Whereas the logical model is concerned with tables and
data records, the physical model deals with storage
devices, file structure, access methods, and locations of
data.

Several types of data organization
• Hierarchical model
- Hierarchical relationships between data(parent-
child)
• Network Model
- Focus on connections (e.g. airline booking
system)
• Relational model
- Based on relations (tables)- True Relat. DBMS use
SQL
• Object-Oriented model
- Focus on Objects

Entity-relationship Formalism
ENTITY_NAME1
-attribute 1
-attribute 2
…
ENTITY_NAME2
-attribute 1
-attribute 2
…
0-N 0-1
Minimum cardinality
Maximum cardinality
(indeterminable/any number)
Attributes
Association
(relationship)
Entity Entity name
Identifier
(key-attribute)
(0,N) refers to the cardinality of the
relationship

The E/R diagram for land parcels
STREET
-name
PARCEL
-number
POINT
-number
-x,y
2-N 3-N
2-N
SEGMENT
-number
LANDOWNER
-name
-date-of-birth
1-N
1-N
0-1 1-2
2-2
A B
C D
A: Streets have edges
(segments)
B: parcels have boundaries
(segments)
C: line have two endpoints
D: parcels have owners, and
people own land.

Data Dictionary
 Definition:
A data catalog that describes the contents of a database.
Information is listed about each field in the attribute table and
about the format, definitions and structures of the attribute
tables.
A data dictionary is an essential component of metadata
information.

Example
 Definition of entities
 RAIL: way of communication and transportation
 Definition of attributes
 RAIL-ID: reference numbers for rail segments
 RAIL_CLASS: single track, double track, electrified, etc.
 RAIL_NAME: name for particular railway
 Explanations for measurements of attributes (type of
attribute values) or coding practices
 RAIL-ID: INTEGER
 RAIL-NAME: CHARACTER, LONG=30

Sample components of a digital EA map
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358
Enumeration Area Map Symbols
National Statistical Office -
July 1998
Census
2000
EA
Locality
District EA-Code
Hospital
Church
School
Building
number
Province:
District:
Locality:
EA-Code:
Cartania
Chartes
Maptown
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032
0221
00361
Approximate scale
N
45
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Street Network Buildings
Boundaries Annotation and symbols
Building numbers Neatlines and legend
361
378
374
349
350
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377
362
358
Lambe
rt
Avenue
M
e
rc
at
or
A
ve
nu
e
Cassini Drive
Cassini Drive
Imhof Drive
Eckert Drive
Miller
D
r
iv
e
Bonne Street
Mollweide Street
Grinten
Street
Good
e
St
re
et
Bessel Street
Street
Robinson Street
Tissot Street
Gall Street
Pto
lem
y
Str
eet
Ort
eliu
s
Str
eet
C
la
rk
e
St
re
et
Tobler Street
Sny
der
Stre
et
Kra
sso
wsk
ij
Stre
et
361
378
374
349
350
358
377
362
Enumeration Area Map Symbols
National Statistical Office - July 1998
Census 2000
EA
Locality
District EA-Code
Hospital
Church
School
Building
number
Province:
District:
Locality:
EA-Code:
Cartania
Chartes
Maptown
14
032
0221
00361
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0 100
Approximate scale
200m
N
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EA database entities
EA
EA-code
Area
Pop.
Street
Number
Name
---
Buildings
Number
HHs
Etc.
Crew leader area
CL-code
Name
RO responsible
Admin. Unit
AU
AU_Pop.
---
Landmark
--
---
---

Example of Relations
EA entity can be linked to the entity crew leader area. The table for this
entity could have attributes such as the name of the crew leader, the regional
office responsible, contact information, and the crew leader code (CL code)
as primary code, which is also present in the EA entity.
Crew leader area
CL-code
Name
RO responsible
1-N
EA
EA-code
Area
Pop.
1-1
R

Entity: Enumeration areas
EA-code Area Pop. CL-code
50101 28.5 988 78
50102 20.2 708 78
50103 18.1 590 78
50104 22.4 812 78
50201 19.3 677 79
50202 17.6 907 79
50203 25.7 879 79
50204 26.8 591 79
… … …
Identifier
Type
(attributes)

Components of a digital EA database
Boundary database

A Simpler Alternative
 In many countries, EA map design may be simpler
than in this example
 Instead of a fully integrated digital base map in
vector format, rasterized images of topographic
maps may be used as a backdrop for EA boundaries
 In some instances, map features may be more
generalized, for instance by using only the
centerlines for the streets and polygons for entire city
blocks rather than for individual houses

Data Representation
Raster
Vector
Real World

Two Fundamental Types of Data
• GIS work with two fundamentally different types
of geographic information
• Vector
• Raster (or Grid)
• Both types have unique advantages and
disadvantages
• A GIS should be able to handle both types

Vector vs Raster or Discrete vs Continuous
River
Vector Raster
x1,y1
xn,yn

Raster Data
• A raster image is a collection of grid cells - like a
scanned map or picture
• Raster data is extremely useful for continuous data
representation
• elevation
• slope
• modeling surfaces
• Satellite imagery and aerial photos are commonly
used raster data sets

Vector Data
• Vector data are stored as a series of
x,y coordinates
• Good for discrete data
representation
• points: wells, town centroids
• lines: roads, rivers, contours
• polygons: enumeration areas,
districts, town boundaries, building
footprints

Raster-Vector conversion (“vectorization”)

Vector: Points, lines, polygons
• Set of geometric primitives:
points lines polygons
node
vertex
x
y

Vector Structure
• Spaghetti
• Topology
• Network
(graph)
I I
I

Spaghetti File
No Topology = raw file or ‘spagehetti file’
Lines not connected; have no ‘intelligence’

Example of “Spaghetti” data structure
1 2 3 4 5 6
1
2
3
4
5
6
Poly coordinates
A (1,4), (1,6), (6,6), (6,4), (4,4), (1,4)
B (1,4), (4,4), (4,1), (1,1), (1,4)
C (4,4), (6,4), (6,1), (4,1), (4,4)
A
B C

Topology
• Data structure in which each point, line and piece or
whole of a polygon :
• “knows” where it is
• “knows” what is around it
• “understands” its environment
• “knows” how to get around
Helps answer the question what is where?

Topology: Spatial Relationships
Adjacency
Connectivity
Containment
Left Polygon = A
Right Polygon =
B
Node 1 = Chains
A,B,C
Chain A is
connected to
chains B & C
Polygon B
Contained within
polygon A

Example of Topological data structure
1
2
3
4
5
6
A
B C
Node X Y Lines
I 1 4 1,2,4
II 4 4 4,5,6
III 6 4 1,3,5
IV 4 1 2,3,6
1 2 3 4 5 6
I II II
I
IV
1
2 3
4 5
6
Poly Lines
A 1,4,5
B 2,4,6
C 3,5,6
From To Left Right
Line Node Node Poly Poly
1 I III O A
2 I IV B O
3 III IV O C
4 I II A B
5 II III A C
6 II IV C B
O = “outside” polygon

Comparison
Spaghetti Topology
-Set of independent
objects
- Representation of
heterogonous objects
within the same model
-Appropriate to CAD
-Pre-calculation of
topological relations
-Maintenance of topological
constraints
- correspondence with
exchange formats
Advantages:

…cont.
Spaghetti Topology
-Spatial Relationships
calculated
- Risk of incoherence
(duplication of common
boundaries)
-High cost of up-to-date
-Many levels of indirections
for complex objects
-Maintenance
Disadvantages:

Some well known Topological models
TIGER: Topologically Integrated Geographic Encoding and Referencing
(Census Bureau of the USA)
Line is the principal element to which are related points and area
features
ARC/INFO model: ESRI
Point, Line, Polygon

TIGER Data: Polygon
Counties
MCD’s
Census
Tracts
Block
Groups
Voting
Districts
Zip Codes
Cities

TIGER Data: Line
Streams
Streets
Railroads

TIGER Data: Point
Key
Locations
Landmarks
Place Names
Zip+4
Centroids

Recapitulation on spatial models
 Transformations between models:
 “vectorization” of raster images (costly)
 topology toward spaghetti (easy)
 spaghetti toward topology (possible but costly)
 The vector model most used, essentially topology; it’s
useful to integrate raster and vector

Spatial Analysis: Query
 select features by their attributes:
 “find all districts with literacy rates < 60%”
 select features by geographic relationships
 “find all family planning clinics within this district”
 combined attributes/geographic queries
 “find all villages within 10km of a health facility that have high
child mortality”
Query operations are based on the SQL (Structured
Query Language) concept

Examples: Id 0012376027
Name Limop
Population 31838
Popdens 37.5
Num_H
H
8719
Clinics 8
Population density
greater than 100
persons/sqkm?
What is
at…?
Features that
meet a set of
criteria

Spatial Analysis (cont.)
 Buffer: find all settlements that are more than 10km from
a health clinic
 Point-in-polygon operations: identify for all villages into
which vegetation zone they fall
 Polygon overlay: combine administrative records with
health district data
 Network operations: find the shortest route from village
to hospital

Modeling/Geoprocessing
 modeling: identify or predict a process that has
created or will create a certain spatial pattern
 diffusion: how is the epidemic spreading in the
province?
 interaction: where do people migrate to?
 what-if scenarios: if the dam is built, how many people
will be displaced?

Spatial relationships
 Logical connections between spatial objects
represented by points, lines and polygons
 e.g.,
- point-in-polygon
- line-line
- polygon-polygon

Spatial Operations
• “adjacent to”
• “connected to”
• “near to”
• “intersects with”
• “within”
• “overlaps”
• etc.

“is nearest to”
• Point/point
• Which family planning clinic
is closest to the village?
• Point/line
•Which road is nearest to the
village
• Same with other combinations of
spatial features

“is nearest to”: Thiessen Polygons

“is near to”: Buffer Operations
• Point buffer
• Affected area around a polluting facility
• Catchment area of a water source

Buffer Operations
• Line buffer
• How many people live near the polluted river?
• What is the area impacted by highway noise

Buffet Operations
• Polygon buffer
• Area around a reservoir where development should not
be permitted

“ is within”: point in polygon
• Which of the cholera cases are within the containment area

Problem:
We may have a set of point coordinates representing
clusters from a demographic survey and we would like
to combine the survey information with data from the
census that is available by enumeration areas.
Solution:
“Point-in-Polygon” operation will identify for each
point the EA area into which it falls and will attach
the census data to the attribute record of that survey
point.

“overlaps”: Polygon overlay

Data Layers
B u i l d i n g s
E l e v a t i o n
A d m i n i s t r a t i v e
u n i t s
H y d r o l o g y
R o a d s
V e g e t a t i o n

Spatial aggregation
• Example of Spatial aggregation:
• fusion of many provinces constituting an
economic region

Spatial data transformation: interpolation
13.5
12.7
15.9
20.1
24.5
26.0
27.2
26.1
Example 1: Based on a set of station precipitation surface
estimates, we can create a raster surface that shows rainfall
in the entire region

GIS capabilities:
Visualization

Implementing a GIS
• Consider the strategic purpose
• Plan for the planning
• Determine technology requirements
• Determine the end products
• Define the system scope
• Create a data design
• Choose a data model
• Determine system requirements
• Analyze benefits and costs
• Make an implementation plan
Source: Thinking About GIS, Third Edition
Geographic Information System Planning for Managers

GIS:
Enables us to handle very large amounts of data
• Example: census data
– thousands of EAs
– hundreds of variables
– many complementary data layers
(roads, rivers, public facilities)
• Example: remote sensing
– satellites send huge amounts of data
that need to be processed, interpreted
and stored

GIS:
Helps to make data re-usable and useful to many more
users
• Census geography
– EA maps do not have to be redrawn
every time, only updated
– census information can be used for
many more applications
– data sharing among agencies

In Conclusion
• GIS for inventory/visualization
• GIS creates maps from data pulled from databases anytime
to any scale for anyone
• GIS for database management
• GIS for spatial analysis/modeling
• GIS a tool to query, analyze, and map data in support of the
decision making process.

What is Not GIS
 GPS – Global Positioning System
 …not just software!
 …not just for making maps!
 Maps are an input data to and a “product” of a GIS
 A way to visualize the analysis

Literature related to Census Mapping & GIS
• US National Research Council:
• Tools and Methods for Estimating
Populations At Risk
• David Martin (1996)
• Geographic Information Systems:
Socioeconomic Applications
• Longley and al, Wiley (2005)
• Geographic Information Systems and
Science, second edition
• ESRI Press:
• Unlocking the Census with GIS
• Mapping the Census 2000

Vector to Raster Conversion: Polygons
c
b
a

Vector to Raster Conversion: Lines

Raster to Vector Conversion: Polygons

Spatial Analysis & Dissemination of
Census Data

Outline
 Spatial Analysis Techniques
 Examples

Geographic Database
 Geographical features (Conceptual Model)
 Components selection
 Attributes
 Structure
 Spatial Relationships (explicit -Topolgy)

Spatial
relationships
 Logical connections between spatial objects
represented by points, lines and polygons
 e.g.,
- point-in-polygon
- line-line
- polygon-polygon

Spatial
Operations
 “adjacent to”
 “connected to”
 “near to”
 “intersects with”
 “within”
 “overlaps”
 etc.

Spatial Analysis Techniques
 the main use of spatial analysis is for census products and
services
 Techniques include: queries, distance measurements,
buffering, linear interpolation, point pattern analysis, and
cartograms, etc.
 All offer functionality beyond standard thematic (choropleth)
mapping, with many tools now available in both commercial
and open-source software programs.

Spatial Analysis:
Query
 select features by their attributes:
 “find all districts with literacy rates < 60%”
 select features by geographic relationships
 “find all family planning clinics within this district”
 combined attributes/geographic queries
 “find all villages within 10km of a health facility that have high
child mortality”
Query operations are based on the SQL (Structured
Query Language) concept

“is nearest
to”
• Point/point
• Which family planning clinic
is closest to the village?
• Point/line
•Which road is nearest to the
village
• Same with other combinations of
spatial features

United Nations Regional Seminar on Census
Data Dissemination and Spatial Analysis
Nairobi, Kenya, 14-17 September, 2010
“is near to”: Buffer
Operations
• Point buffer
• Affected area
around a Hospital
• Catchment area of a
water source

“is near to”: Buffer
Operations
• Point buffer
• Affected area around a polluting facility
• Catchment area of a water source

Buffer Operations
• Line buffer
• How many people live near the polluted river?
• What is the area impacted by highway noise?

Buffer Operations
• Polygon buffer
• Area around a reservoir where development should not
be permitted

 point-in-polygon analysis
 Determines whether a point lies inside or outside a
polygon.
 Can be used to compare geo-coded village centroids
lying inside and outside hazardous areas such as tropical
storm tracks or earthquake zones.
 Polygon overlay analysis
 Involves comparison between the locations of two
different polygonal data layers.
 For example, the boundaries of two administrative
districts could be compared to troubleshoot errors in the
field enumeration process

“ is within”: point in
polygon
• Which of the cholera cases are within the containment area

Spatial
aggregation
 Example of Spatial aggregation:
 fusion of many provinces constituting an
economic region

Spatial data transformation:
interpolation
13.5
12.7
15.9
20.1
24.
5
26.0
27.2
26.1
Example 1: Based on a set of station precipitation surface
estimates, we can create a raster surface that shows rainfall
in the entire region

Example of linear interpolation creating contours

Thiessen polygons illustrated
 Thiessen polygons
 Have the unique property that each
polygon contains only one input point
(e.g. settlements), and any location
within a polygon is closer to its
associated point than to the point of any
other polygon.
 This method assumes that the values of
the unsampled data are equivalent to
those of the sampled points.

Areas of influence
 Commuting distances:
daily commuters flow

Modeling/Geoproces
sing
 modeling: identify or predict a process that has
created or will create a certain spatial pattern
 diffusion: how is the epidemic spreading in the
province?
 interaction: where do people migrate to?
 what-if scenarios: if the dam is built, how many people
will be displaced?

Modelling: smoothing
 Evolution of the
population beetwen
two censuses

 Cartograms
 sometimes used to
display census results
 The areas of the original
polygons are expanded
or contracted based on
their attribute values
such as population size
or voting habits

Location-allocation problems
 Site selection
 Optimal allocation
 Multicriteria Analysis

Geographic Watershed Information System (GWIS)
DEM Modeling and Terrains with ArcGIS
(and other helpful items)

What is a Digital Elevation Model?

Digital Elevation Models, con’t.

What is a Digital Elevation Model/Digital SURFACE Models (DSM) ?

Digital Elevation Models/Digital SURFACE Models (DSM)

What is a Digital Elevation Model/Digital TERRAIN Model (DTM) ?
So…

Digital Terrain Model (DTM)
Digital Elevation Model (DEM)
Digital Surface Model (DSM)
Three terms (DEM,DSM,DTM) for the same thing?

Not so easy …
Digital Surface Model (DSM) is a first surface view of the
earth containing both location and elevation information.
Digital Terrain Model (DTM), aka "bare earth" as it is
often referred, is created by digitally removing all of the
cultural features inherent to a DSM by exposing the
underlying terrain.
A Digital Elevation Model (DEM) is any DIGITAL representation of ground surface
topography or terrain.
Representation is another issue: Raster or Triangular Irregular Network (TIN)
(TIN)
(Interpolated TIN
with faces)
Raster DEM (or Interpolated TIN)

Then…
With release 9.2 of ArcGIS, ESRI released a NEW data structure called:
TERRAIN Dataset
Terrains are a new dataset for ArcGIS 9.2. They live inside feature datasets in
personal, file or SDE geodatabases. The other feature classes in the feature dataset
can participate in the terrain or actually be embedded in the terrain, which means
that the source data could be moved off-line after the creation of the terrain dataset.
The graphic below illustrates how multiple types of feature classes can participate
to generate TIN pyramids.

A terrain dataset is a multiresolution, TIN-based surface built from measurements
stored as features in a geodatabase. They're typically made from LIDAR, SONAR,
and photogrammetric sources. Terrains reside in the geodatabase, inside feature
datasets with the features used to construct them.
Terrains have participating feature classes and rules, similar to topologies. Common
feature classes that act as data sources for terrains include:
•Multipoint feature classes of 3D mass points created from a data source such as
LIDAR or SONAR
•3D point and line feature classes created on photogrammetric workstations using stereo
imagery
•Study area boundaries used to define the bounds of the terrain dataset
The terrain dataset's rules control how features are used to define a surface. For
example, a feature class containing edge of pavement lines for roads could participate
with the rule that its features be used as hard breaklines. This will have the desired
effect of creating linear discontinuities in the surface.
So… What is a Terrain Dataset?

Rules also indicate how a feature class participates through a range of scales.
The edge of pavement features might only be needed for medium to large-scale
surface representations. Rules could be used to exclude them from use at small
scales, which would improve performance. A terrain dataset in the geodatabase
references the original feature classes. It doesn't actually store a surface as a raster
or TIN. Rather, it organizes the data for fast retrieval and derives a TIN surface on the
fly. This organization involves the creation of 'terrain pyramids' that are used to
quickly retrieve only the data necessary to construct a surface of the required level
of detail (LOD) for a given area of interest (AOI) from the database. The appropriate
pyramid level is used relative to the current display scale.
Terrains con’t

1- NGVD to NAVD Conversion – LiDAR data are referenced to NAVD88
but ERP data are referenced to NGVD29
2- Size limits for Terrains:
2 GB (20 million points) in pGDB
1 TB (several hundred million points) in fGDB
unlimited in ArcSDE
3- Limited to file-based GeoDatabases – Large Terrains will only work
in a file-based GDB: ArcINFO/ArcEditor only
4- Size limits for TIN – 15 – 20 million nodes (32 bit processing)
5- Size limits for Rasters/Grids – 4,000,000 x 4,000,000 cells (at 5’x5’ cells, that
amounts to a watershed no larger than
4000 x 4000 miles (quite large, but…)
6- ArcHydro processing limits – recommended for DEMs up to 20,000 x
20,000 (at 5’x5’ cells = 400 sq. miles)
7- Raster can be stored in a fGDB – but must be converted to a Grid (external
to the fGDB) for processing!
Some ESRI Terrain Gottcha’s

General LiDAR/Terrain Workflow
Grid Dataset
Feature Class
Dataset
Function
Legend
Optional Function
Object Class
(Table)
LAS (LiDAR data)
Terrain Break lines including:
HYDROGRAPHICFEATURES
ROADS
SOFTFEATURES
ISLANDS
WATERBODIES
COASTALSHORELINES
OBSCUREDVEG
NHD Flowlines
NHD Water Bodies and Swamps
PROJECT AREA (Polygon)
STEP 1 – CREATING FILE GEODATABASE
Create File GeoDatabase and a Feature DataSet
When defining the FDS, IMPORT the Spatial Data Reference
from the Terrain Break lines. Be certain to assign the correct
Vertical Datum and check to insure that the units are correct
STARTING TERRAIN FGDB
FROM PROVIDER
STEP 2 – IMPORTING FEATURE CLASSES
Import the Feature Classes from the Starting Data Sets into the
GeoDatabase. Use IMPORT as either single or multiple
features, making certain that all data are in the same projection
system as that defined for the fGDB.
STEP 3 – CONVERTING LAS (LIDAR) DATA
Engage the 3D Analyst Extension and from the 3D Analyst
ToolBox, choose Conversion|From File|LAS to Multipoint
Choose the LAS Files
Average Spacing = 6 (or other appropriate value)
Input Class Codes = 2,10,11
IMPORT the Spatial Reference from the fGDB checking for the
correct Vertical Datum and units
In ArcCatalog
In ArcCatalog
In ArcCatalog
Terrain (Multipoint) Feature Class
(MASSPOINTS)
STEP 4 – BUILD THE TERRAIN FEATURE CLASS
Create a new Terrain Feature Class with the following:
Terrain Name = XXTerrain (where XX is anything)
Select the Feature Classes (at minimum)
Feature Class = SFtype
MASSPOINTS = Mass Points
HYDROGRAPHICFEATURES = Hard Line
ROADS = Hard Line
ISLANDS = Hard Fill Value
SOFTFEATURES = Soft Line
WATERBODIES = Hard Replace
COASTALSHORELINE = Hard Line
[PROJECT AREA = Soft Clip] if not set in Environment
Calculate Pyramid Properties as:
0.25 6800
1.00 12000
Check on the Advanced Bounds Settings (Button) and make certain that the
max value is set for the minimum pyramid level.
Set Environment Variables as needed to insure appropriate
1- workspace and scratch space
2- Extents (as necessart)
3- Units and Coordinate system
TERRAIN FEATURE CLASS
(XXTerrain)
In ArcCatalog
STEP 5 – CHECK THE TERRAIN FC
Open ArcMap and Add the Terrain
Right-Click on Terrain|Properties
Select Symbology – Set the Classification Method for Elevation to “Natural
Breaks” and change the number of classes to 20.
Select the desired Color Ramp
Visually Check Terrain for consistency
In ArcMap
(OPTIONAL)
STEP 6 – CREATE A FLOATING POINT OR INTEGER
RASTER FOR ARCHYDRO PROCESSING
Close ArcMap
From the 3D Analyst Toolbox, choose:
Conversion|From Terrain|Terrain to Raster
Use the XXTerrain, output a raster (outside of fGDB) using
Method = Natural_Neighbors
CellSize = 5.0
Pyramid Level Resolution = 0
Set Environment Variables as needed
In ArcCatalog
FLOATING POINT GRID FOR
ARCHYDRO
LAS (LiDAR) Data Terrain Processing Workflow
for ArcHydro
NOTE: This step will require approximately 5-20 minutes
per square mile of terrain. The unit time is dependent on
the number of break lines and other factors.
NOTE: The import time is
approximately 30 seconds
per 1 sq. mile tile.
NOTE: Import time for the
Multiclass features is
about 2 – 3 minutes for a
15 sq. mile. watershed.
NOTE: This is the longest step. It can take a few minutes to several
hours depending on the Cell Size specified. The smaller the cell size,
the greater the processing time.
OTHER FEATURE
CLASSES TO
INCORPORATE
LAS Data
files
INTEGER GRID FOR ARCHYDRO
(OPTIONAL OUTPUT)
Choice made during Conversion
Step 1 – Create a file-based Geodatabase
Step 2 – Import/Create Feature Classes
Step 3 – Convert LAS to Multipoints
Step 5 – Extract a Digital Elevation Model
from the Terrain
Step 4 – Build the Terrain

Step 4 - Creating a Terrain Dataset
In ArcToolbox In ArcCatalog
Or…
Notice the GWIS schema

In ArcToolbox
Step 1 – Create the Terrain in a GDB
SWFWMD recommends = 4

In ArcToolbox
Step 2 – Add Feature Classes to the Terrain
Make sure to populate the:
height field
SF_Type field

In ArcToolbox
Step 3 – Add Pyramids to the Terrain
Level 1: 0.25 6800
Level 2: 1.00 12000

In ArcToolbox
Step 4 – Build the Terrain

In ArcCatalog
Use the Terrain Wizard by right-clicking on the GDB

In ArcCatalog
Post Spacing = 4
Select Feature
Classes Adjust Breaklines SF_Type
Set Pyramid Levels
Confirm Settings and Finish

Terrain of the Little Withlacoochee Watershed

Step 5 - Extracting a Digital Elevation Model
General Work Flow:
Step 1 – Set Environment Variables
Step 2 – Run the Terrain to Raster GP Tool
Step 3 – Wait
Step 4 – Check the DEM

In ArcToolbox (either in ArcMap or ArcCatalog)
Right-click on the blank space (anywhere) in the Toolbox

In ArcToolbox
Highlight the “Environments…” option to open the dialog box

In ArcToolbox
Use the Folder next to the “Extent” option to navigate to the LIDARTILES
Feature Class in the fGDB, and note the extent values. These need to be
multiples of 5’, so adjust to…

In ArcToolbox
Make them exact multiples of 5’ (ArcGIS likes to cheat; do not let it.)
Also, check on Current workspace and other variables as needed.

In ArcToolbox
The Terrain to Raster GP Tool will open the dialog box. Fill in all fields as
indicated above. At this time, it is also a good idea to check the
Environments… to make certain that they are set to the proper
coordinates. Then “OK” (and wait again!)
Make sure path is DOS 8.3 compliant
Make sure this is “0”

DEM of the Little Withlacoochee Watershed

Perfect alignment of adjacent DEMs
Aripeka DEM
Indian Creek
DEM

Some more ESRI Terrain/DEM Gottcha’s
1- File-based GDB can be read, but not processed in Arcview, so
an ArcINFO or ArcEditor license is required.
2- An ESRI 3D Analyst license is needed to build Terrains and extract
DEMs.
3- Based on a Pentium 4HT processor (3GHz with 4GB RAM), it takes
approximately 0.75 - 1 hr/100 pyramids to construct a Terrain.
The progress bar indicates the number of pyramids in
the Terrain, so judge accordingly.
4- Based on a Pentium 4HT processor (3GHz with 4 GB RAM), it takes
approximately 0.75 - 1 hr/300 pyramids to construct a DEM.
5- SWFWMD experience shows that the Terrain to Raster interpolator
appears VERY sensitive to breaklines with “null” elevations.
This will cause the interpolator (Linear or Natural Neighbors)
to stop and produce an incomplete DEM.
6- The GP tools in ArcToolbox can be used to delete feature classes
from the Terrain, but sometimes it may be easier to remake
the Terrain.
7- The Terrain to Raster interpolator is VERY sensitive to path and
file names. Paths can not have spaces, underscores, etc. and
file names can have no more than 11 DOS-compliant characters.

And even more ESRI Terrain/DEM Gottcha’s
8- ESRI Rasters CAN exist within a GDB, BUT during processing
they are converted to Grids. So, although you CAN it is
not a good idea to keep DEMs in the GDB; keep them in
their own, separate location.
9- It is a good idea to check the DEM after it is generated to make
certain that the cell size is correct and that adjacent DEMs
align properly as this will help eliminate slivers along the
catchment boundaries.
10- Make sure that you have sufficient FREE DISK space BEFORE
you begin processing any Terrains or DEMs. Our “rule
of thumb” is to have 2GB free for each 1GB in the GDB.
11- Unless you have a VERY fast, dual core or quad core
computer workstation, building Terrains or DEMs is a
dedicated task. Try to let these run overnight or some
other low-use time.
12- Make sure that the connection to the ESRI licenser server is
not broken!

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