Census Mapping

Census Mapping


A. Sai Venkata Lakshmi, P. S. Bhavani Kumar, J. Sai Baba
ADRIN, Secunderabad – 500009, India
Email: [email protected]

The evolving standards for software components such as ActiveX, COM and Javabeans, as well as industry specific Open GIS Consortium standards are making it possible to embed GIS capabilities in census mapping.

Data produced by Census is a primary source of information needed for effective development, planning and monitoring of population, socio-economic and environmental trends. Though stored in databases, this data has a spatial component inherently associated with it. The census database contains a lot of attribute information which can be linked to spatial units by spatial referencing. Relating the spatial component along with the non-spatial attributes of the existing corporate data enhances user’s understanding and gives new insights into the patterns and relationships in the data that would otherwise not be found.

Embedded GIS
As the software industry is becoming component-based, the GIS industry is providing components that can be embedded in other applications. The evolving standards for software components such as ActiveX, COM and Javabeans, as well as industry specific Open GIS Consortium standards are making it possible to embed GIS capabilities in mainstream IT applications. ESRI’s MapObjects, MapInfo’s MapX and InterGraph’s GeoMedia objects are examples of products that provide GIS functionality in individual, accessible software components.

Using Census Mapper to visualise Census data
The objective is to enable viewing of existing Census data stored in an Relational Data Base Management System (RDBMS) which has an inherent spatial component. “We present an efficient, cost-effective method that utilises the best features of databases and GIS packages: efficient data storage and retrieval as well as viewing the data spatially so the underlying pattern can be observed.” The solution is to store the non-spatial data in a database and relate it to the corresponding spatial layer which is stored in a GIS supported format like Shapefile. The user first defines a relation between a field in a table in the database and a field in the corresponding spatial layer. Any number of relates can be made. For example, if there is a table that holds state-level data which has a filed ‘STATENAME”, then a relate is made on this field to the field ‘Name’ in the spatial layer ‘states’. The table containing the district-level data can correspondingly be related to the spatial layer holding districts. A similar relate can be made at the city-level.

If the data is not enumerated or a many-to-one relationship exists between the database and spatial layer, a new table can be created by appropriately summarising the data and this table can be related to this spatial layer. Otherwise code can be included to convert the many-to-one relationship to a one-to-one relationship appropriately. The database is queried and the selected records are passed to the application, which selects the corresponding spatial objects. This enables dynamic mapping, i.e. what you see on the map changes based on other related factors.

Fig.1: State Layer

Various features like panning, zooming, searching for features satisfying a criteria and proximity analysis are available. This provides a framework for viewing, analysing and supporting decision making on different scales. The information is first viewed at the state level, further zooming displays the district-level data. Zooming onto a district then gives town level data, wherever available. Thus one can view the variation between states, the variation between different districts within a state along with the variation among towns in a district. For example in Fig. 1, six fields-Total Male Population, Total Female Population, Total Male Literates, Total Female Literates, Total Male Workers and Total Female Workers are represented as bar charts. One can also flash features that are selected based on some criteria. Visualising enumerated data aids planners and decision makers. Symbols positioned to represent sample locations can be visually configured in order to display recorded attributes to enable geographic patterns to be interpreted at different scales, and the detail for local variations to be detected and data values perceived. The spatial data can be visualised in a number of ways that enhance the user’s understanding and interpretation of the data, some of which are mentioned below. One can choose which fields to use for the charts. One can compare multiple attributes of a feature by depicting the attributes as elements of pie charts or bar charts. One can also scale the bar and pie charts based on a field like population. To simplify visualisation, attributes or density values can be classified into a few categories that span the data range. Each category can be assigned a shade in a graded sequence of a user-defined color ramp. This is illustrated in Fig 2, where the states of India are coloured based on total population. Features can also be symbolised with unique attribute values. Areal units can be filled with a dot density proportional to attribute values. Distance between features can be measured. Features can be labeled based on some attribute and text can be splined along the feature. Text (such as title) and other graphics (that are not in the spatial layer) can be added to stress on important events or landmarks with appropriate symbols and thus enhance the readability or take a print. Features can be searched based on attribute values or spatial proximity. Moreover this search can be extended over more than one layer, e.g, one can find all towns within 10 km of national highways. Other spatial layers can be added to the display to enhance understanding (say water bodies or roads layer) and visibility of layers can be toggled. Symbology of each of these layers can be modified to suit the user’s needs. The map contents can be exported to a Windows Bitmap or emf format. It can also be printed.

Fig. 2: Classification of districts based on Total Population 
Data not available for Jammu Kashmir

The data may reside anywhere on a network. It may be stored in any database that can be accessed through Visual Basic. This is because the current implementation uses MapObjects through Visual Basic. A similar application could be developed with VC++, Delphi and PowerBuilder also.

This application is developed in Visual Basic. Currently, it can be added as a plug-in to any existing windows application that can call a Dynamic Link Library (DLL). It may be any windows application which is querying a database. The spatial viewing capability is provided through MapObjects. Developed on Windows NT and tested on Windows 95, Windows 98 and Windows 2000.

The stated objective can be met in a number of ways but Visual Basic and MapObjects were chosen for a number of reasons – Visual Basic has a user-friendly Graphic User Interface (GUI). MapObjects is easy to use, seamlessly integrated with Visual Basic application and cost effective. This is meant to be used at many places by many people so licensing issues have to be looked into,hence ArcView was avoided. This is an ideal solution for users who have database applications and want to add a spatial component with a few GIS operations. If the database is very large and better performance is expected in terms of speed at an extra cost, then SDE can be used. In that case, since both spatial and non-spatial data is stored in the same RDBMS, all the RDBMS facilities would be taken advantage of and the application would give enhanced performance.