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GIS Fundamentals/ Geographic Database Design

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. Territory.

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GIS Fundamentals/ Geographic Database Design

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  1. GIS Fundamentals/Geographic Database Design

  2. 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. Territory Data Information Cycle GIS DSS Information Decision

  4. 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.

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

  6. 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. 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 displaygeographically referenced information in order to resolve complex problems of management and planning.

  8. Components of a GIS

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

  10. Integration of Multiple data: - Sources - Scales - Formats Geographic Database Spatial Analysis GIS: Main Characteristics

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

  12. 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

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

  14. 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

  15. UTM: Universal Transverse Mercator • Cylindrical projection with a central meridian that is specific to a standard UTM zone • 60 zones around the world • 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)

  16. Space as an indexing system

  17. The concept of scale • 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 • 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

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

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

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

  21. Descriptive Data vs Geographic Data • Descriptive Data: • Descriptive attributes • Geographic Data: • Descriptive attributes • Spatial attributes • Location • Form

  22. 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

  23. 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)

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

  25. 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

  26. 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.

  27. 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.

  28. GeographicDatabase • “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

  29. 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

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

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

  32. 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.

  33. 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

  34. 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.

  35. 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 Several types of data organization

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

  37. An example of land parcels

  38. A B 2-N 0-1 1-2 3-N The E/R diagram for land parcels 1-N 2-2 A: Streets have edges (segments) B: parcels have boundaries (segments) C: line have two endpoints D: parcels have owners, and people own land. C D 2-N 1-N

  39. Data Tables

  40. 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.

  41. 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

  42. Sample components of a digital EA map

  43. EA database entities

  44. 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. Example of Relations R 1-1 1-N

  45. Type (attributes) Entity: Enumeration areas Identifier

  46. Boundary database Components of a digital EA database

  47. 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

  48. Data Representation Raster Vector Real World

  49. 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 Two Fundamental Types of Data

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

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