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Bangladesh University of Engineering and Technology (BUET)

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1 Bangladesh University of Engineering and Technology (BUET)
Introduction to GIS Dr. A.K.M. Saiful Islam Institute of Water and Flood Management (IWFM) Bangladesh University of Engineering and Technology (BUET)

2 Presentation outline Introduction to GIS Components of GIS
Sources of geospatial data Geospatial databases ESRI Data models

3 Introduction to GIS What is GIS ?
An Information System that is used to input, store , retrieve, manipulate, analyze and output geographically referenced data or geospatial data, in order to support decision making for planning and management of land use, natural resources, environment, transportation, urban facilities, and other administrative records

4 Why GIS is essential ? Common problems of handing geospatial information: Geospatial data are poorly maintained. Maps and statistics are out of date. Data and information are inaccurate. There is no data retrieval service. There is no data sharing.

5 GIS Versus Manual Works
Maps GIS Manual works Storage Standardized and integrated Different scales on different standard Retrieval Digital Database Paper Maps, Census, Tables Updating Search by Computer Manual Check Overlay Very Fast Expensive & Time consuming Spatial Analysis Easy Complicated Display Cheap & Fast Expensive

6 Benefits once GIS is implemented
Geospatial data are better maintained in a standard format. Revision and updating are easier. Geospatial data and information are easier to search, analysis and represent. More value added product. Geospatial data can be shared and exchanged freely. Productivity of the staff improved and more efficient. Time and money are saved. Better decision can be made.

7 Basic Functions of GIS Functions Sub-functions Data Acquisition
Remote Sensing and GIS in Water Management © Dr. Saiful Islam, IWFM, BUET Basic Functions of GIS Functions Sub-functions Data Acquisition and prepossessing Digitizing, Editing , Topology Building, Projection Transformation, Format Conversion etc. Database Management and Retrieval Data Archival, Hierarchical Modeling , Network Modeling, Relational Modeling, Attribute Query, Object-oriented Database etc. Spatial Measurement and Analysis Measurement operations, Buffering, Overlay operations, connectivity Operations etc. Graphic output and Visualization Scale Transformation, Generalization, Topological Map, Statistical Map etc.

8 GIS as Multidisciplinary Science

9 Area of GIS Applications
Facilities Management Locating underground pipes & cables, planning facility maintenance, telecommunication network services Environmental and Natural Resources Management Environmental impact analysis, disaster management and mitigation Street Network Locating houses and streets, car navigation, transportation planning Planning and Engineering Urban planning, regional planning, development of public facilities Land Information Taxation, zoning of land use, land acquisition

10 GIS for decision support

11 Components of GIS Key components of GIS are:
Computer system, geospatial data, and users Sources of geospatial data are: Digitized maps, aerial photographs, satellite images, statistical tables, and other related documents Computer System Geospatial Data Users Figure: Key components of GIS

12 Classification of Geospatial Data
Graphical data (called geometric data) Attributes (called thematic data)

13 Data Model A set of guidelines to convert the real world (called entity) to the digitally and logically represented spatial objects consisting of the attributes and geometry. Types of geometric data model Vector Model Model uses discrete points, lines and/or areas corresponding to discrete objects with name or code number of attributes Raster Model - Model uses regularly spaced grid cells in specific sequence. An element of grid cell is called a pixel (picture cell)

14 Example of vector based model
Vector model

15 Example of raster representation
more colors 256 color

16 Components of Raster model
Raster model, otherwise known as a raster dataset (image), in its simplest form is a matrix (grid) of cells. Cell value - Each cell has a value. Cell size- Each cell has a width and height and is a portion of the entire area represented by the raster Cell location - The location of each cell is defined by its row or column location within the raster matrix.

17 Geometry and Topology of Vector Data
Spatial objects are classified into point object such as meteorological station, line object such as highway and area object such as agricultural land, which are represented geometrically by point, line and area respectively Topology refers to the relationships or connectivity between spatial objects

18

19 Topological of Spatial Objects

20 Attributes Attributes are often termed "thematic data" or "non-spatial data", that are linked with spatial data or geometric data. An attribute has a defined characteristic of entity in the real world. Attribute can be categorized as normal, ordinal, numerical, conditional and other characteristics. Attribute values are often listed in attribute tables which will establish relationships between the attributes and spatial data such as point, line and area objects, and also among the attributes

21 Map Layers Spatial objects in digital representation can be grouped into layers. For example, a map can be divided into a set of map layers consisting of contours, boundaries, roads, rivers, houses, forests etc.

22 Sources for GIS data Analog maps Ground survey with GPS
Remote Sensing and GIS in Water Management © Dr. Saiful Islam, IWFM, BUET Sources for GIS data Analog maps Aerial photographs Satellite image Ground survey with GPS Reports and publications

23 Data Acquisition Methods

24 Concept of Spatial Database
A spatial database is defined as a collection of inter-related geospatial data, that can handle and maintain a large amount of data which is shareable between different GIS applications. Required functions of a spatial database are as follows. - consistency with little or no redundancy. - maintenance of data quality including updating - self descriptive with metadata. - high performance by database management system with database language. - security including access control.

25 Design of Spatial Database
The following parameters should be well designed. storage media Volume, access speed and on line service should be considered. partition of data Choice of administrative boundaries, map sheets, watersheds etc. will be made in consideration of GIS applications standards Format, accuracy and quality should be standardized. change and updating Add, delete, edit and update should be well controlled by the database manager. scheduling Data availability, priorities, data acquisition etc. should be well scheduled. security Copyright, back up system and responsibilities should be well managed. partition of data

26 Spatial Data Models Hierarchical Relational Object oriented

27 1. Hierarchical Model Stores data as hierarchically related to each other. Record shape are tree structure. BUET Faculty of Civil Engineering Faculty of Architectural WRE CE Archit.. URP

28 (Contd..) Advantages High speed access to large databases
Easy to update- (to add or delete new nodes) Disadvantages Links are only possible in Vertical Direction (from top to bottom) but not for horizontal or diagonal unless they have same parents. For example, it is hard to find what is the relation between URP and DCE from this data model.

29 2. Relational Model Based on two important concepts:
Employee Table Based on two important concepts: Key of relation - one to one, one to many, many to many Primary attribute – which can’t be duplicate EmployeeID Name Course ID 1 Rahim 001 2 Karim 002 3 Sharmin 003 Course table CourseID Title Fees 001 RS & GIS in WM 6,000 002 Risk Management 003 River Management 4,000 * * Employee Table Course Table Many to many relationship

30 Find the relationship between this two tables in the BUET Library
Book Table ISBN Title Author 050 Applied Hydrology David Maidmen 060 Irrigation Cheng One to one Many to Many One to Many Students Table ID Name ISBN 1 Rahim 050 2 Karim 060 3 Sharmin 070 ?

31 Structural Query Language (SQL)
SQL is used to perform query in relations databases. For example, find the name of the employee who have spend more than 10,000 this year to attend different courses. SELECT Employee.Name, Course.Fees FROM Employee WHERE Employee.CourseID = Course.CourseID AND Fees >= 10,000 The answer is : Rahim

32 3. Object Oriented Model BUET Part of Part of Departments Institutes
Is a Is a Is a CE URP DCE IWFM AIT WRE Is a = Inheritance Part of = association Attributes: Faculty, Staff, Students

33 Object Oriented Database
An Object Oriented model uses functions to model spatial and non-spatial relationships of geographic objects and the attributes. An object is an encapsulated unit which is characterized by attributes, a set of orientations and rules. An object oriented model has the following characteristics. generic properties : there should be an inheritance relationship. abstraction : objects, classes and super classes are to be generated by classification, generalization, association and aggregation. adhoc queries : users can order spatial operations to obtain spatial relationships of geographic objects using a special language.

34 ESRI Data models Advancements in GIS
Vector Data models Shape file Coverage Composite Data model TIN Regions Route Database Geodatabase

35 ESRI’s models Shapefiles – as non-topological data format. Shape file treats points are pair of x, y coordinates, a line as a series of points and a polygon as a series of lines. Can be displayed more rapidly on monitors. Interoperable among other software. Coverage – as topological based vector data format. A coverage can be point coverage, line coverage or polygon coverage. Connectivity: Arcs connect to each other at nodes. Area definition: An area is defined by a series of connected arcs. Contiguity: Arcs have directions and left and right polygons

36 Data models for composite features
TIN – Triangulated irregular network data model approximates the terrain with a set of non-overlapping triangles. Regions – is defined here as a geographic area with similar characteristics. A coverage feature class that can represent a single area feature as more than one polygon. Routes - is a line feature such as highway, a bike path, or a stream but unlike other linear features, a route has a measurement system that allows linear measures to be used on a projected coordinate system.

37 Triangulated Irregular Network (TIN)
Triangulated irregular network. A vector data structure used to store and display surface models. A TIN partitions geographic space using a set of irregularly spaced data points, each of which has an x-, y-, and z-value. These points are connected by edges into a set of contiguous, non-overlapping triangles, creating a continuous surface that represents the terrain. TIN TIN & Contour

38 Components of TIN Nodes Edges Triangles Hull
The nodes originate from the points and line vertices contained in the input data sources. Every node is incorporated in the TIN triangulation. Every node in the TIN surface model must have a z value. Every node is joined with its nearest neighbors by edges to form triangles which satisfy the Delaunay criterion. Each edge has two nodes, but a node may have two or more edges. Because edges have a node with a z value at each end, it is possible to calculate a slope along the edge from one node to the other. Each triangular facet describes the behavior of a portion of the TIN's surface. The x,y,z coordinate values of a triangle’s three nodes can be used to derive information about the facet, such as slope, aspect, surface area, and surface length. The hull of a TIN is formed by one or more polygons containing the entire set of data points used to construct the TIN. The hull polygons define the zone of interpolation of the TIN. Inside or on the edge of the hull polygons, it is possible to interpolate surface z values, perform analysis, and generate surface displays. Outside the hull polygons, it is not possible to derive information about the surface. The hull of a TIN can be formed by one or more polygons which can be non-convex. Nodes Edges Triangles Hull

39 Delaunay Triangulation for TIN
A method of fitting triangles to a set of points. The triangles are defined by the condition that the circumscribing circle of any triangle does not contain any other points of the data except the three defining it. It is a method which produces triangles with a low variance in edge length. The resulting triangles may be used as an irregular tessellation for interpolation of other points on a surface.

40 Region and polygon -1 Polygons do not overlap and completely cover the area being represented (do not contain any void areas). In a region, the polygons representing geographic features can be freestanding, they can overlap, and they need not exhaust the total area.

41 Region and polygon -2 Another premise of polygons is that each geographic feature is represented by one polygon. This is extended for regions, so that a single geographic feature can be represented by several polygons.

42 Region and polygon -3 As with points, lines, and polygons, each region is given a unique identifier. As with polygons, area and perimeter are maintained for each region. Constructing regions with polygons is similar to constructing polygons from arcs. Whereas a polygon is a list of arcs, a region is simply a list of polygons. One important distinction exists: the order of the polygons is not significant.

43 Route In ArcGIS, the term route refers to any linear feature, such as a city street, highway, river, or pipe, that has a unique identifier and a common measurement system along each linear feature. A collection of routes with a common measurement system is a route feature class. Each route in the feature class will also have a unique identifier. Line features with the same unique identifier are considered to be part of the same route: Route feature classes are created and managed as line feature classes in the geodatabase. You can also use route feature classes from ArcInfo coverages and polyline shapefiles that include route identifiers and measured features.

44 Point events along Route
Point events occur at a precise point location along a route. Accident locations along highways, signals along rail lines, bus stops along bus routes, Wells or gauging stations along river reaches, pumping stations along pipe lines, Manholes along city streets and valves along pipes are all examples of point events. Point events use a single measure value to describe their location.

45 Line events along Route
Line events describe portions of routes. Pavement quality, salmon spawning grounds, bus fares, pipe widths, and traffic volumes are all examples of line events. Line events use two measure values to describe their location.

46 Polygon events along Route
Locating polygon features along routes computes the route and measure information at the geometric intersection of polygon data and route data. Once polygon data has been located along routes, the resulting event table can be used, for example, to calculate the length of route that traveled through each polygon. Examples: Soils, spillways, areas of inundation, or hazard zones along river reaches Wetlands, hazard zones, or town boundaries along highways

47 Example of Route Hydrologists and ecologists use linear referencing on stream networks to locate various types of events The route feature class for streams provides measures along the streams using river reach mile. Point and line event tables record the route ID and location along each river reach. These event tables can be used to locate point and line events.

48 Route system A collection of routes with a common system of measurement is called a route system. Route systems usually define linear features with similar attributes. For example, a set of all bus routes in a county would be a route system. Many route systems can exist within a single coverage. For example, school bus, truck, and ambulance route systems could exist in a coverage of a city. Route systems are built using arcs, routes, and sections, and can accurately model linear features without having to modify the underlying arc-node topology. The route below is defined using four arcs. Notice how the route's endpoints fall along the arcs. Routes need not begin and end at nodes. Sections, as shown below, are the arcs or portions of arcs used to define each route. They form the infrastructure of the route system. The diagram below shows an example of attributes containing distance measurements, such as milepost numbers or addresses, which can be used to locate events, such as accidents or pavement quality.

49 Dynamic segmentation Dynamic segmentation (DynSeg) is the process of computing the map location (shape) of events stored in an event table. Dynamic segmentation is what allows multiple sets of attributes to be associated with any portion of a linear feature.

50 Geodatabase A spatial and attribute data container
Relational database management system (RDBMS) Maintains data integrity Apply Rules and Behavior Native data format for ArcGIS Relational Database - A method of structuring data as collections of tables that are logically associated to each other by shared attributes. Any data element can be found in a relation by knowing the name of the table, the attribute (column) name, and the value of the primary key.

51 First Generation Storage/Linking
AS400 Database Access Tabular Data Spatial Data Tabular/Spatial data is linked outside the database Links occur using unique IDs….Parcel Numbers Storage is still in separate locations

52 Second Generation Storage/Linking Geodatabases
Tabular Data Spatial Data Tabular/Spatial data is stored/linked in a single location!!

53 Benefits of a GeoDatabase
Spatial & attribute data integrity Intelligent Behavior Centralized Data Storage Increased Performance Advanced Analysis Capabilities Multi-user editing (SDE format)

54 Benefits of Migrating to a Geodatabase Data Integrity
Maintain tabular data more efficiently Reduce typological data errors Maintain spatial data more efficiently Reduce spatial errors Pro-West & Associates

55 2 Types of Geodatabase Personal Geodatabase Enterprise Geodatabase
Stand alone PC, MS Access database Supports individual and small groups on moderate size datasets Enterprise Geodatabase Exists on underlying RDBMS through Spatial Database Engine (SDE) e.g. SQL Server Usually runs on a dedicated server Supports many users and massive datasets Supports raster datasets

56 Two types of GeoDatabases
Personal Access Multi-user SDE There are two types of GeoDatabases: Personal and Multi-user. A Personal Geodatabase uses Microsoft Access as the DBMS (Database Management System) which has certain limitations imposed by Access and limitations imposed by ArcGIS. It is a great learning tool, but may fall short of expectations due to some limitations listed later. All Personal GeoDatabases created with earlier (than 8.3) software should be upgraded to version 9.1. This is easily done within the Properties for the GeoDatabase. A Multi-user Geodatabase uses one of the supported DBMS’s listed below. It requires an SDE license and has the full suite of capabilities outlined in this chapter and more. As information is presented in this chapter, differences between the two will be pointed out. Supported DBMS’s (hardware platforms may differ): IBM DB2 Universal Database (UDB) INFORMIX Dynamic Server Microsoft SQL Server Oracle GIS SQL SDE Data Storage Interpreter View/Analyze

57 The Personal Geodatabase It’s not Scary!
Stores spatial and tabular data in an Access database format Sets the stage for future SDE geodatbase migration Edit in ArcView, ArcEditor or ArcInfo

58 Geodatabase Features Feature Dataset Contains tables, feature classes, feature datasets, topology rules, etc. Topology Feature Classes Tables

59 Geodatabase Elements Geodatabase Feature data set Geometric network
Feature class Relationship class Table Annotation class

60 GeoDatabase (GDB) structure
Stores Feature datasets Feature classes Tables Raster More A unique structure within the GDB One might make the statement that we can store about any type of data for our GIS needs within a Geodatabase. For the most part that is true, as long as we differentiate between the “Personal Geodatabase” and the “Multi-User Geodatabase”. As mentioned earlier, the Personal Geodatabase makes use of MS Access to store the structured tables and their relationships needed to represent our GIS data. MS Access has limitations and prohibits the storage of certain data formats (for example, Rasters cannot be stored internally with the Personal Geodatabase due to size limitations). Because of these differences between personal and multi-user Geodatabases, one should consider the needs of the organization to determine the use of the system in setting up and choosing the RDBMS and how it will be part of the integrated system. The common structures you will work with in the beginning are: Feature Datasets, Feature Classes, Tables and Rasters. Certainly there are other objects that are available in a Geodatabase which you should learn as you advance. Each of the basic objects will be discussed on the following pages.

61 Feature Dataset Contains Feature Classes Required for Topology
Must have same coordinate system Required for Topology Behavior relationships between feature classes.

62 GDB Objects: Feature Dataset
A collection of feature classes Environment for spatial reference Environment for topology Environment for coincident geometry and linked annotation Feature classes inherit spatial reference Data loaded are projected on the fly, if necessary The feature dataset provides the user with the “framework” to organize logical collections of Feature classes (even different feature types – point, line, poly) together to represent the behavior and topologic relationships that the individual Feature classes share. The extent of the flexibility delivered in using Feature datasets is much more exhaustive and efficient than in previous models, such as coverages. Feature datasets are “user grouped” Feature classes (which are discussed on the next page). This allows the user to more accurately and efficiently model real world entities as “feature groupings” and establish topological relationships between those Feature classes that participate in the topology. All of this is accomplished as a structured collection of tables in the RDBMS, thus exploiting the performance benefits of the database. The Feature dataset name can be interpreted as the “container” for the groupings of the Feature classes listed “below and indented” under the Feature dataset. Benefit can be reaped from having a spatial reference defined for the Feature dataset so when new Feature classes are loaded into an existing Feature dataset, the data will “project on the fly” (if necessary) into the spatial reference of the Feature dataset.

63 Feature Class Stores a single feature type
Point, Line, Polygon Can be standalone or member of a Feature dataset Feature Dataset Feature Class Stand Alone

64 GDB Objects: Feature Class (FC)
A collection of features Each feature class has one geometry type (point, multi-point, line, polygon) Can be stored in a feature dataset or ‘stand-alone’ Attributes are stored with coordinate data in one table A Feature class is a collection of features. Features are vector data (which you learned in an earlier chapter as discrete x,y coordinates) and represent points, lines, or polygons. Feature classes can be “stand alone” or be grouped into Feature datasets to model more complex topologic relationships.

65 Spatial Reference In setting up the Spatial Reference, ArcGIS provides a dialog allowing the user to select from a standard list of coordinate systems, import from other datasets already having a Spatial Reference defined, create a new coordinate system with all the particular parameters (which is tedious and difficult), or modify an existing coordinate system. The Spatial Reference also defines the maximum spatial extent for the data. Other parameters of the Spatial Reference for the Geodatabase are: X/Y Domain – The “bounding rectangle” or extent of the feature class or feature dataset. It is specified with the minimum X value, minimum Y value, maximum X value, and maximum Y value. Z Domain – The maximum range of “Z” values for the data. For example: 0 to (Sea Level to a peak of ft maximum elevation) M Domain – The maximum range of Measure units (this can be time or distance ranges). This tolerance is also the only exception to the rule regarding consistency; feature classes within the same feature dataset can have different Measure domains. A

66 Domain A property of a feature dataset or feature class (cannot change once set) The domain of the feature coordinates is, essentially, the minimum and maximum ranges available for feature values, for feature classes, and for feature datasets. The exception is the Measure Domain for feature classes in a feature dataset. Feature classes in a feature dataset may have different Measure Domains. The Feature Coordinate domains are set up upon creation of the data in ArcCatalog. All data that will be loaded in the future will have to adhere to the valid domains. The precision of a domain will affect the extent of that domain. The spatial domain for a feature class or feature dataset cannot be changed once it is established. Here is a very short list of example precisions and the corresponding x/y domain: Note: Precision does not have to be base 10 numbers, although this is easiest to understand. Precision Min. Value Max. Value 1 2,147,483,645 1000 2,147,483,645,000 10 214,748,364.5 100 21,474,836.45

67 Coordinate domain Extent of available coordinates
Min and max X,Y coordinates Precision = storage units per map unit Example, 1000 mm per meter Make sure it covers study area Allow for growth ArcCatalog default Import: data plus room for growth Set your own Import from existing data Type in extent for study area Coordinate domain: Spatial domain is best described as the allowable coordinate range for x,y coordinates. Precision describes the number of divisions per one unit of measure. A spatial reference with a precision of 1 will store integer values, while a precision of 1,000 will store one thousandth of a unit. The spatial domain for a feature class or feature dataset cannot be changed. If the required x-, y-, m-, or z-value ranges for your database increases, the data has to be reloaded into feature classes with a spatial reference that accommodates the new domain. You must store your coordinates at a specific precision. For best results, ensure that the chosen precision will support the accuracy of your data. The ArcCatalog loading wizard will look at your data and determine defaults for your precision and database growth. Naturally, it is impossible for ArcCatalog to understand your intentions, so the default settings often are not the best choice. It is important that you understand domains. The geodatabase stores coordinates as a 4-byte integer that has a maximum value of 2,147,483,648. You decide what the units represent. For instance, if you need to store meter accuracy, then you have 2.1 billion meters to work with. If you decide to store centimeters, you would have 2.1 billion centimeters to work with. These are called storage units. You should set your storage units based on your accuracy.. Map units are the measurement units of your data, and have nothing to do with accuracy. They are the units that represent measurements. For example, the meter is the standard unit for UTM data, while the foot is the standard unit for a state plane coordinate system. 2.14 billion storage units

68 The Spatial Domain The Geodatabase stores all geometry coordinates as positive integers Faster Display, Processing, and Analysis Better Compression (DBMS only) Efficient for managing topologic relationships Limited to 2,147,423,647 storage units. 2.14x109 meters, or miles, or inches, or ...

69 Thank you !


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