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

2. 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

3. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Information Cycle Territory Information Decision DSS GIS Data

4. 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 Avenue, NY, was built in 1898. Data: Museum, address, year of construction.

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

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

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

8. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Components of a GIS

9. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 GIS Components Input Output Maps Census Field Data RS Data Others Reports Maps Photo. Products Statistics Input Data for models Data Capture Storage Manipulation Analysis Display GIS Models Other GIS User Interface Geographic DataGeographic Information

10. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 GIS: Main Characteristics Integration of Multiple data: - Sources - Scales - Formats Geographic Database Spatial Analysis

11. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 GPS/ air photos/ satellite images Census/ Tabular data Picture & Multimedia Maps Data from multiple sources-at multiple scales-in multiple formats

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

13. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 The Latitude/Longitude reference system latitude φ : angle from the equator to the parallel longitude λ : angle from Greenwich meridian

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

15. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 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 Map Projections (Cont.)

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

17. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Compromise projections Do not preserve any property, but represent a good compromise between the different objectives e.g., Robinson’s projection for the World

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

19. 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)

20. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 UTM Cylindrical projection with a central meridian that is specific to a standard UTM zone 60 zones around the world

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

22. 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

23. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 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 The concept of scale

24. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Multi-scales The same feature represented in different scales. Example: lake Large scale (1:25.000) Small scale 1:500.000

25. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Multi-formats Raster Vector Raster-Vector- Raster DXF-DGN-etc. Shapefile KML Etc.

26. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Geographic Database Geographic Data Characteristics Examples Geographic Dataset Geographic Database Concepts Spatial entity Data Modeling

27. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Descriptive Data vs Geographic Data General Data: Descriptive attributes Geographic Data: Descriptive attributes Spatial attributes Location Form

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

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

30. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Example 2: District Attribute (Descriptive) data: District-Code District-Name Population 1990 Population 2000 Population 2010 Spatial data: Geographical Position Polygon

31. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Geographic Database Definition Components: Spatial Entity/Attribute/Dataset Data Modeling/Data Dictionary Spatial Representation Vector/Raster Topology Standard Spatial Operations

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

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

34. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Dataset “ A dataset is a single collection of values or objects without any particular requirement as to form of organization.”

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

36. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Data Modeling Data Approach Modeling Process Entity/Relationship Approach Example

37. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Modeling Process Reality Geographic Database Modeling (data & treat.) Abstracting the Real World

38. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 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 2External Model 3

39. 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.

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

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

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

43. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Entity-relationship Formalism ENTITY_NAME -attribute 1 -attribute 2 … ENTITY_NAME -attribute 1 -attribute 2 … 0-N 0-1 Minimum cardinality Maximum cardinality Attributes Association (relationship) Entity Entity name Identifier (key-attribute)

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

45. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 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 0-1 1-2 2-2 AB 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.

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

47. 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.

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

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

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

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

52. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Data Representation Raster Vector Real World

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

54. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Vector vs Raster or Discrete vs Continuous River Vector Raster x1,y1 xn,yn

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

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

57. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Raster-Vector conversion (“vectorization”)

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

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

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

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

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

63. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Vector: Points, lines, polygons Set of geometric primitives: pointslinespolygons node vertex x y

64. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Vector Structure Spaghetti Topology Network (graph) I II

65. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Spaghetti File No Topology = raw file or ‘spagehetti file’ Lines not connected; have no ‘intelligence’

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

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

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

69. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Example of Topological data structure 1 2 3 4 5 6 A BC 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 IIIIII IV 1 2 3 45 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

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

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

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

73. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Comparison (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 Disavantages

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

75. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 TIGER Data: Polygon Counties MCD’sCensus Tracts Block Groups Voting Districts Zip CodesCities

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

77. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 TIGER Data: Point Key LocationsLandmarksPlace NamesZip+4 Centroids

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

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

80. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Examples: What is at…? Features that meet a set of criteria

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

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

83. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Spatial relationships Logical connections between spatial objects represented by points, lines and polygons e.g., - point-in-polygon - line-line - polygon-polygon

84. 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.

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

86. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 “is nearest to”: Thiessen Polygons

87. 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

88. 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

89. 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

90. 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

91. 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.

92. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 “overlaps”: Polygon overlay

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

94. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Data Layers

95. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Spatial aggregation Example of Spatial aggregation: fusion of many provinces constituting an economic region

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

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

98. 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

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

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

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

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

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

104. Workshop on Census Cartography and Management, Bangkok, Thailand, 15–19 October 2007 Contact Information: Demographic Statistics Section UN Statistics Division New York globalcensus2010@un.org