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Reviving the old data: The spatial environmental database development approach
Methodology
The development of a spatial environmental database of this kind involves a number of activities which are briefly summarised below. However, it should be noted that some of the activities overlap with others. The environmental database discussed here was undertaken for one of the major oil producing company in Nigeria. Review of Literature: The project commenced with the review of about three hundred and fifty thousand bibliographic materials provided by the Company. The objective of the review was to identify those reports that contain useful data that can be used to develop an Environmental Database Project (EDP). In addition, the review was carried out to identify the data universe for the EDP. The data universe contains an extensive list of the entire available data field that would be represented in the environmental database. The data universe was arrived at based on the examination of all the data tables contained in all the reports. After the review, only about two hundred and eighty reports qualified to provide input into the database. Data Model. In the development of the spatial environmental database, it is important to decide on how data are to be extracted and stored. This is very important because this decision would influence how data are aggregated and modelled for subsequent use. The smallest or minimum unit of spatial data capture determines how data in database can be used and for what purpose. This decision is very crucial because it determines how data can be queried, analyse and to some extent linked. In this study, the unit of data aggregation is the sampling points. The choice of sampling points as the unit of data aggregation stem from the fact that environmental data are collected at specific point locations (Sample locations) in space. Once data are stored as point locations, they can later be aggregated to polygon level (Oil Mining Lease, Oil Field, Facility, state, or zone) or line level (Pipeline, river etc). The advantage is that once data are stored as points (Field sample locations) they can be aggregated to any other desired unit by the would be user of the database. It would also ensure that data are captured correctly. Other alternative units of data storage considered include facility level, state level or area of operation (West, East) level. Classification of Reports In order to make the database more intelligent, easy to understand and interpret, the reports covered under this project were classified into seven distinct groups. The major report groups identified are: Environmental Impact Assessment (EIA), Environmental Baseline Study (EBS), Post Impact Assessment (PIA), Special Studies, Compliance Monitoring, Drilling and Dredging, and Waste Management. Each of these major classes is made up of the following sub table, based on the type of data they contain; borehole physico chemical table, climate table, geology and geophysical table, sediment physico chemical table, socio economic table, soil sediment physico chemical table, vegetation table, and water physico chemical table. The table name describes the type of data the table holds.  Fig 2: Entity Relations Diagram |