CREATION AND UTILITIES OF GEOGRAPHIC DATABASE A CASE STUDY OF BINPUR-I CD BLOCK, PASCHIM MEDINIPUR, WEST BENGAL Indrita Saha1 and Prof Ashis Sarkar2 1 Research Scholar and 2 Professor of Geography, Chandernagore College, West Bengal ABSTRACT Geographical variables are quantitative or descriptive measures of different geographical features. They may belong to different domains, ranging from biology (distribution of species and biodiversity measures), soil science (soil properties and types), vegetation science (plant species and communities, land cover types), climatology (climatic variables at surface and beneath/above), to hydrology (water quantities and conditions) and associated terrain attributes (geomorphological aspects). These are commonly collected through field sampling (supported by remote sensing); field samples are then used to produce maps showing their distribution in an area. Such accurate and up-to-date maps of environmental features provide a crucial input to spatial planning, decision making, land evaluation and/or land degradation assessment. Today, our main challenge is to build high quality geographical information / database relating to contemporary socio-economic issues, e.g., food security, landuse, landcover, urbanization, deforestation, hazards / disaster, climate change, environmental degradation, water scarcity, biodiversity, etc. This poster is simply an attempt to show how modern tools can be used to mitigate development issues through a case study of Binpur-I CD Block, Paschim Medinipur, West Bengal. OBJECTIVES To extract physical data from different sources To build relevant socio-economic GDMs. To manipulate the databases and create data products To interpret the data products from the perspective of geography. METHODOLOGY Sources of Data: The relevant landuse / landcover map prepared by Land and Land Reforms Department, Govt. of West Bengal in the scale of 1:50000 have served as one of the basic data source. Besides digital data of Landsat with xyz resolution has been used. The District Census Handbook of Paschim Medinipur district have also been consulted. Techniques employed: The present work is involved of following three steps which can be summarized in the form of a Schematic model — • Preparation of various maps using GIS software Mapinfo Professional 7.0 • Extraction of Digital Elevation Data and its modeling through Terrain Analysis Software (TAS) • The image processing by using Remote Sensing Software ERDAS IMAGINE-9.2. • Land use/land cover classification by applying supervised classification method. • Computation of different indices and charts using Microsoft Excel 2010 Techniques of Analysis: The methodological approach adopted in the research for this particular dissertation is strictly quantitative. Initially the Geographer’s Data Matrix (GDM) of 553 villages was extracted from the Census data. They were compiled to get the data for the separate Gram Panchayats (GP). Similar data were mined from the DEM files. Based on these data, different indices were calculated, appropriate for the study. Almost all the indices have been mapped using GIS. There are three types of maps, isoline maps of physical aspects, choropleth maps of both village and GP level and proportional pie and square maps. Source: L&LR, GoWB Geographic Data can be described as different observations, which are collected and stored. Information is that data, which is useful in answering queries or solving a problem. Digitizing a large number of maps provides a large amount of data, but the data can only render useful information if it is used in analysis. Types of Geographic Data: Geographic data are organised in a geographic database. This database is a collection of spatially referenced data that acts as a model of reality. There are two important components of this geographic database: its geographic position and its attributes or properties. In other words, spatial data (where is it?) and attribute data (what is it?) Attribute Data: The attributes refer to the properties of spatial entities. They are often referred to as non-spatial data since they do not represent location information. Spatial data: Geographic position refers to the fact that each feature has a location that must be specified. To specify the position in an absolute way a coordinate system is used. For small areas, the simplest coordinate system is the regular square grid. For larger areas, certain approved cartographic projections are commonly used. Geographic object can be shown by points, lines, areas, and continuous surfaces. Point Data: Points are the simplest type of spatial data. They are-zero dimensional objects with only a position in space but no length. Line Data: Lines are one-dimensional spatial objects. Besides having a position in space, they also have a length. Area Data: Areas are two-dimensional spatial objects with not only a position in space and a length but also a width (in other words they have an area). Continuous Surface: Continuous surfaces are three-dimensional spatial objects with not only a position in space, a length and a width, but also a depth or height. Linkages and Matching: A GIS typically links different sets to give infinite number of combinations and permutations of data which if interpreted with expertise might give perfect insight of the characteristics of the region. Exact Matching: Exact matching occurs when information a file (e.g., towns) and additional information in another file about the same set of features is brought together. Thus, the record in each file with the same town name is extracted, and the two are joined and stored in another file. Hierarchical Matching: Some types of information, however, are collected in more detail and less frequently than other types of information. For example, population data smaller areas are collected less frequently than in larger areas. If the smaller areas nest within the larger ones, then by hierarchical matching, the data for the small areas are added until the grouped areas match the bigger ones and then match them exactly. Fuzzy Matching: On many occasions, the boundaries of the smaller areas do not match those of the larger ones. For example, crop boundaries, usually defined by field edges, rarely match the boundaries between the soil types. To determine the most productive soil for a particular crop, you need to overlay the two sets and compute crop productivity for each and every soil type. INFERENCE A GIS can carry out all these operations because it uses geography, as a common key between the data sets. Information is linked only if it relates to the same geographical area. Considering a situation with two data sets for a given area, e.g. Population and Area, each data might be analysed and/or mapped individually or they may be combined (Density of Population). By bringing them together, value is added to the database. Extracted from SRTM DEM Source: L&LR, GoWB Extracted from SRTM DEM Source: L&LR, GoWB and DCHB, Paschim Medinipur, 2001 Extracted from SRTM DEM Source: L&LR, GoWB and DCHB, Paschim Medinipur, 2001 For any queries kindly contact: indrita.saha@gmail.com profdrashis@gmail.com Source: DCHB, Paschim Medinipur, 2001 Source: DCHB, Paschim Medinipur, 2001 Extracted from Landsat TM Image