Rayat Shikshan Sanstha’s Chhatrapati Shivaji College Satara Department of Geography Digital Mapping By : Shri. Sandip Kolekar Assistant Professor Department of Geography Chhatrapati Shivaji College, Satara.

PRINCIPAL OF DIGITAL MAPPING Representation of Geospatial Data in Digital Environment So….. What is Geospatial Data ?

E.g.: Road: To describe a road, we refer to its location (i.e. where it is) The Location also called geometry or shape, represents Spatial Data. AND

II) Its Characteristics Characteristics are Attribute Data Thus a road, like any geospatial data, has the two components…… Length - 100 Km Name – NH-4 Speed Limit – 80 km/Hr Direction – Both Way

Geospatial Data I) Spatial Data II) Attribute Data Spatial Data: Data that describe the geometry of spatial features Attribute/ Non-spatial Data: Data that describe the characteristics of spatial features

Spatial Feature There are three Basic Spatial features Shape Examples 1. Point Location of village Well Post Office etc. 2. Line Road Stream Telephone line etc. 3. Polygon (Area) Forest Water Body Garden etc.

Spatial Data Spatial data describe the location of spatial features, which may be discrete or continuous. Discrete features are individually distinguishable features that do not exist between observations. Discrete features include points (e.g., wells), lines (e.g., roads), and areas (e.g., land use types).

Spatial Data Continuous Features are features that exist spatially between observation. Examples of continuous features are elevation and precipitation. A Digital Mapping represents these spatial features on the Earth’s surface as map features on a plane surface. This transformation involves two main issues: The spatial reference system and The data model.

The spatial reference system The location of spatial features on the Earth’s surface are based on a geographic coordinate system with longitude & latitude values. Whereas the locations of map features are based on a plane coordinate system with x-, y-coordinates. Projection is process that can transform the Earth’s spherical surface to a plane surface and bridge the two spatial reference systems.

The spatial reference system But because the transformation always involves some distortion, hundreds of plane coordinate systems that have been developed to preserve certain spatial properties are in use. To align with one another spatially for GIS/ Mapping operations, map layers must be based on the same coordinate system. A Basic understanding of projection and coordinate system is therefore crucial to users of spatial data.

The Data Model The data model defines how spatial features are represented in GIS / Digital Mapping (Col. 2, row 2) (Col. 5, row 5) (Col. 1, row 7)

The Data Model Vector Raster The vector data model uses point & their x-, y-coordinates to construct spatial features of points, lines, and areas. Vector data are ideal for representing discrete features(e.g. wells, roads, land use type etc.). Data Structure: The vector data model may be georelational or object-based, may or may not involve topology, and may include simple or composite features Raster The raster data model uses a grid and grid cells to represent the spatial variation of a feature. Raster data are better suited for continuous features(e.g.: Elevation, Precipitation etc.). Data Structure: The raster data model uses a simple data structure with rows and columns and fixed cell locations.

Vector Data Model Georelational Data Model Object-based Data Model OR May or may not involve Topology May include simple or composite features

Vector Data Model The Vector data model may be georelational or object based, may or may not involve topology, and may include simple or composite features. The georelational data model uses a split system to store spatial data and attribute data. The Object-based data model, on the other hand, stores spatial data and attribute data in a single system. Recent trends suggest that GIS vendors have adopted the object based data model in their software development. E.g.: The geodatabase data model, introduced by ESRI.

Topology Topology expresses explicitly the spatial re­lationships between features, such as two lines meeting perfectly at a point and a directed line having an explicit left and right side. Topological or topology-based data are useful for detecting and correcting digitizing errors in geographic data sets and are necessary for some GIS analyses. But non- topological data can display faster. To distinguish between them, the Ordnance Survey in Great Britain offers topological and nontopological data separately to accommodate end users with different needs. Likewise, users of ESRI software recognize coverages as topological data, shapefiles as nontopological data, and geodatabases as with or without topology

Vector Data Model Geo-Relational Object-based Topological Coverage Geodatabase Non­ topological Shapefile

Composite Features Composite features are built on simple features of points, lines, and polygons. The triangulated irregular network (TIN), which approximates the terrain with a set of non-overlapping triangles, is made of nodes (points) and edges (lines) Composite features are useful in GIS because they can handle more complex spatial relationships. For example, dynamic segmentation allows highway rest areas, which are typically recorded in linear measures, to be plotted on a highway map, which is based on a coordinate system.

Composite Features An example of the TIN model

Attribute Data Attribute data describe the characteristics of spatial features. For raster data, each cell has a value that corresponds to the attribute of the spatial feature at that location. A cell is tightly bound to its cell value. For vector data, the amount of attribute data to be associated with a spatial feature can vary significantly. A road segment may only have the attributes of length and speed limit, whereas a soil polygon may have dozens of properties, interpretations, and performance data. How to join spatial and attribute data is therefore important in the case of vector data

Joining Spatial and Attribute Data The georelational data model stores attribute data separately from spatial data in a split system. The two data components are linked through the feature IDs. The object-based data model stores spatial data as an attribute along with other attributes in a single system. Thus the object-based data model eliminates the complexity of coordinating and synchronizing two sets of data files as required in a split system. It also brings GIS closer to other nonspatial information systems because spatial data files are no longer needed. Whether spatial and attribute data are stored in a split or single system, the relational database model is the norm for data management in GIS. A relational database is a collection of tables (relations). The connection between tables is made through a key, a common field whose values can uniquely identify a record in a table. For example, the feature ID serves as the key in the georelational data model to link spatial data and attribute data.

A relational database is efficient and flexible for data search, data retrieval, data editing, and creating tabular reports. Each table in the database can be prepared, maintained, and edited separately from other tables. And the tables can remain separate until a query or an analysis requires that attribute data from different tables be linked or joined together. With GIS increasingly becoming part of an organization’s much larger information system, attribute data needed for a GIS project are likely to come from an enterprise wide database. If spatial data distinguish GIS as a special type of information system, attribute data tend to connect GIS with other information management systems. This unique combination enhances the role of GIS in an organization.