GIS Fundamentals/ Geographic Database Design

GIS Fundamentals/ Geographic Database Design Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

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 Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

Information Cycle

Territory

Data

GIS

Information

DSS

Decision

Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

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 5 th was built in 1898.

Avenue, NY, • 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.



Geographic Data

Input

•Maps •Census •Field Data •RS Data •Others Data Capture

GIS

Manipulation Analysis Storage Display Geographic Information Output • Reports • Maps • Photo. Products • Statistics • Input Data for models

GIS Components

Other GIS User Interface Models


GIS: Main Characteristics

• Integration of Multiple data: • • • - Sources - Scales - Formats •


•

Spatial Analysis Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

Data from multiple sources-at multiple scales-in multiple formats

Census/ Tabular data Maps Picture & Multimedia GPS/ air photos/ satellite images


Referencing map features: Coordinate systems & map projections

• To integrate geographic data from many different sources, we need to use a consistent spatial referencing system for all data sets


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 • Projection is the method by which the curved surface is converted into a flat representation


Map Projections (Cont.)

• We can think of a projection as a light source located inside the globe which projects the features on the earth’s surface onto a flat map • Point p on the globe becomes point p on the map


Distortion in Map Projections

• Some distortion is inevitable • Less distortion if maps show only small areas, but large if the entire earth is shown • Projections are classified according to which properties they preserve: area, shape, angles, distance


Compromise projections

• Do not preserve any property, but represent a good compromise between the different objectives • e.g., Robinson’s projection for the World


Compromise projections Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

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)


UTM

• Cylindrical projection with a central meridian that is specific to a standard UTM zone • 60 zones around the world


Space as an indexing system Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

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 1cm on the map corresponds to 100,000 cm or 1 km in the real world


The concept of scale

• scale is essentially a ratio or representative fraction • 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” versus “large scale” often confused


Multi-scales

• • 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 Data

• Characteristics • Examples

Geographic Dataset

• •

Geographic Database Concepts

Spatial entity Data Modeling


Descriptive Data vs Geographic Data

• • General 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   Etc.

Geometric Form:  Place-name 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


• •


• • • • Definition Components: Spatial Entity/Attribute/Dataset Data Modeling/Data Dictionary Spatial Representation • Vector/Raster • Topology Standard Spatial Operations


•

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 .”



• • “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 Approach Modeling Process Entity/Relationship Approach Example


Modeling Process Reality

Modeling (data & treat.)

Abstracting the Real World Geographic Database Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

ANSI/SPARC: Study Group on Data Base Management Systems (1975)

“Real World”

External Model 1 External Model 2 Conceptual Model Logical Model Physical Model

Different users have different views of the world

External Model 3 Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

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 •

Relational model

- Based on relations (tables) •

Object-Oriented model

- Focus on Objects


Entity-relationship Formalism

Entity Entity name Attributes ENTITY_NAME -attribute 1 -attribute 2 … 0-N Identifier (key-attribute) Maximum cardinality 0-1 ENTITY_NAME -attribute 1 -attribute 2 … Association (relationship) Minimum cardinality


An example of land parcels Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

The E/R diagram for land parcels

STREET -name 2-N A 0-1 SEGMENT -number 1-2 B 3-N PARCEL -number 2-2 1-N A: Streets have edges (segments) B: parcels have boundaries (segments) C: line have two endpoints D: parcels have owners, and people own land.

C 2-N POINT -number -x,y D 1-N LANDOWNER -name -date-of-birth


Data Tables Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

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: Census GIS database

• • - Basic elements Entity: administrative or census units • enumeration areas • Entity type / Relations • • • • Components of a digital spatial census database: Boundary database Geographic attribute tables Census data tables


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. EA EA-code Area Pop.

1-1 R 1-N Crew leader area CL-code Name RO responsible


Entity: Enumeration areas

Type (attributes) 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


Components of a digital spatial census database Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

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

Vector Raster River 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”) Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

Vector to Raster Conversion: Polygons

b a c


Vector to Raster Conversion: Lines Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

Raster to Vector Conversion: Polygons Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

Raster to Vector Conversion: Polygons Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

Vector data + image (raster) Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

Vector: Points, lines, polygons

• Set of geometric primitives:

polygons

y

points lines

node vertex x


Vector Structure

• • Spaghetti Topology • Network (graph) I II


Spaghetti File

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


Example of “Spaghetti” data structure

6 5 4 3 2 1

A B C

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) 1 2 3 4 5 6


•

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

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


6 5 4 3 1 2 2

Example of Topological data structure

I 4

B

1

A

II 6 IV 5

C

III 1 2 3 4 5 6 3 O = “outside” polygon 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 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


Encoding Topology (not): CAD Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

Encoding Topology: GIS Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

Comparison

Advantages:

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


Comparison (cont.)

Disavantages

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


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 Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

TIGER Data: Line Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

TIGER Data: Point

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 Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

Examples:

What is at…?

Features that meet a set of criteria Id Name Population Popdens Num_H H 0012376027 Limop 31838 37.5

8719 8 Population density greater than 100 persons/sqkm?


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 Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

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 Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

“is near to”: Buffer Operations

• • •

Point buffer Affected area around a polluting facility Catchment area of a water source Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

Buffer Operations

• • •

Line buffer How many people live near the polluted river?

What is the area impacted by highway noise Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

Buffet Operations

• •

Polygon buffer Area around a reservoir where development should not be permitted Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

“ is within”: point in polygon

•

Which of the cholera cases are within the containment area Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

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 Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

Polygon Overlay Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

Data Layers

A d m u n i t s in is t r a t iv e E le v a t i o n B u i ld in g s H y d r o lo g y R o a d s V e g e t a t io n


Spatial aggregation

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


Spatial data transformation: interpolation

Example 1: Based on a set of station precipitation surface estimates, we can create a raster surface that shows rainfall in the entire region 13.5

20.1

12.7

15.9

26.0

24.5

27.2

26.1


GIS capabilities: Visualization Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007

• • • • • • • • • •

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


Contact Information: Demographic Statistics Section UN Statistics Division New York globalcensus2010@un.org


GIS Fundamentals/ Geographic Database Design

Territory

Data

Information

Decision

Raster Vector Real World

Advantages:

Spaghetti Topology

Disavantages

Spaghetti Topology

Centroids

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GIS Fundamentals/ Geographic Database Design

Territory

Data

Information

Decision

Raster Vector Real World

Advantages:

Spaghetti Topology

Disavantages

Spaghetti Topology

Centroids

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