ABSTRACT
The study focused on assessment of revenue generation and volume of waste generation from the different housing units sampled in Oredo Local Government Area, using a geospatial approach. Basically, the instrument of this research study was based on questionnaire. A total of 312 questionnaires were sample across 24 streets that cut across the 12 wards in the study area. The analyses of the questionnaires were based on both statistical and spatial analyses.
Spatial analysis was carried out, using Ansenlin Moran 1, to assess the variations in the amount paid for waste generated from each housing unit as well as the variation in the volume of waste being discharged from the different housing units. The correlation coefficient (r) 0.237 clearly shows a weak correlation between the volume of waste generated and the number of persons that dwell in a housing unit. The spatial analysis output result from cluster and outliers using Ansenlin Moran1 shows remarkable variation in the volume of waste and income generation. However, the null hypothesis, which states that ‘there is no spatial variation in the cost of disposing of waste was rejected due to the fact that the hot spot housing unit standard deviation of 3.05 or 5.42, and 1.91567 or 3.045711 falls outside the range values of -1.96 or 1.96. This is seen in some part of Sapele road area and Boundary road characterized with duplex and shopping complex. While, the other areas being less than the range values of -1.96 or 1.96 tend to depict no variation in the cost of disposing off the waste. Again, Figure 4.4 shows high cluster of waste depicted by symbol ‘HH’ in some part of the areas of Lawani, Oreghene and Esigie Street. Importantly, it was ascertained from the study that from a total of 312 housing unit sampled, the monthly revenue was estimated to be 799, 700 naira. While, the total amount per year is estimated to be 9,596,400 naira. In conclusions, the study shows that not every kind of waste generated is feasible for recycling as seen in table 4.9. The use of GIS has enabled one to understand the spatial clustering of waste generated in terms of area and house types where high volume of waste are likely to be generated in the study area as well as the revenue generated by the waste contractor.
