TABLE OF CONTENTS
List of Tables
1.0 Introduction
1.1 Research objectives
1.2 Research Questions
2.0 Materials and Methods
2.1 Materials
2.2 Methods
3.0 Results
4.0 Discussion
5.0 Summary
6.0 Appendices
6.1 Appendix A: Flow Chart
6.2 Appendix B: Map
6.3 Log File
7.0 References List
1.0 Introduction
Flooding is a natural occurrence in which water submerges otherwise dry land. Flooding harms human activities, posing a risk. Overlay analysis is a simple method for determining the best area that is affected by a phenomenon, in this case flooding. The technique uses inputs which are different yet occupying the same area such as housing settlements, soil types, and topography (Yenigun & Ecer, 2012, p430).
1.1 Research objectives
1. To assess the flood risk within the suburbs in Adelaide in Australia
2. To calculate the population affected by flooding in the suburbs
1.2 Research Questions
1. Is there flooding risk within Adelaide Australia?
2. What proportion of the population will be affected by flooding?
2.0 Materials and Methods
2.1 Materials
The study employs vector datasets to answer the study objectives. This data includes; Suburbs data that is a polygon representing the suburbs of Adelaide, Australia and Flood risk areas for the Adelaide area. The Suburbs data contains attributes on the population of the suburbs while the flood risk data just consists of the different flood risk categorizations.
The data is integrated, processed, and analyzed using the ArcGIS software to show the number of people likely to be affected by various flood risks in the suburbs.
2.2 Methods
With the data available various methods of data analysis were investigated to assess the flood risk within the suburbs of Adelaide, Australia and determine what proportion of the population is affected by flooding in each of the flood risk areas. Since all the data is vector data, Vector Overlay Analysis methods were investigated.
Overlay analysis is a GIS technique that superimposes numerous datasets representing diverse phenomena occupying the same spatial dimension for identifying the spatial connections between them and generate new information. (de Smith et al, 2015). Vector overlay analysis involves creation of composite maps created from combining diverse vector datasets such as points, lines and polygons with their attributes thus generating new maps that show the relationships between the layers (Clarke, 2003, p30). Three most common vector overlay methods were investigated namely; clip, intersect, and union.
Clip analysis is a method that involves extraction of input features that overlay a feature used as the clipping feature. The output generates a new dataset that is a subset of data of the original dataset bounded to the boundaries of the clip feature (esri, 2017). In this study, executing clip analysis resulted into the suburbs that are within the flood risk zones where suburbs data was clipped to the flood risk area data.
Intersect analysis methods involve a computation of geometric intersection between features where all the layers that overlap each and their attributes are written to a new dataset. These datasets contain all the characteristics of the involved datasets (Bailey, 1988, p.13).
Union analysis method on the other hand, involves computation of geometric intersections between participating datasets creating a new dataset that has all the characteristics of the participating datasets even if their extents do not align (Decker et al, 2009, p.58).
In general, the overlay analysis involved setting up the geodatabases in an accessible location, displaying the data on ArcMap, investigating the attributes of the datasets, changing reference coordinates of the map to a projected coordinate and calculating metric areas covered by the different suburbs and flood risks. It was followed by analysis which involves; clipping the suburbs data within the flood risk zones, intersecting the suburbs data with flood risk data, and creating a union between the suburb data and flood data. Finally, intersect analysis was picked as the best describing the situation and from its output population affected by different flood risks was calculated.
3.0 Results
Clip analysis resulted into a dataset being created that was a subset of the suburbs data which implied that attributes from the flood risk data were discarded.
Table 1: Flood risk clipThis means there was no risk data to compute the population in each flood risk zone. Even so, the clip analysis showed that the area under flood risk was summing up to 23.602362 Ha (Appendix B).
Union analysis resulted into a combination of the flood risk area and the entire suburbs data, which meant some areas outside the flood risk zone were included in the output. This was a misleading situation since we needed only areas within the flood zone.
Table 2: Flood risk unionIntersect analysis resulted in an output where both the flood risk area and suburb area exactly coincided with attributes picked from both the datasets. This dataset correctly depicted the information we needed.
Table 3: Flood risk intersectIt was realized that from the analysis, the suburb area with a risk of flood risk 1 was equal to 13.484724 Ha affecting 5393 people and that with a risk of flood risk 2 was equal to 10.117639 Ha affecting 4047 people. The total area under flood risk was seen as 23.602362 Ha (Appendix B).
4.0 Discussion
These results above suggest that Adelaide suburbs have a risk of flooding that would affect the population of 9440 people. The formula used is Population/metric area * metric area output (Equation 1).
The flood zone at risk is 23.60 Ha and hence the people in these areas should be on alert whenever there are heavy rains pouring. The particular flood risk areas were identified by superimposing various factors using the overlay mapping technique. The results identify the potential risk areas and the number of people who are at risk of flooding within Adelaide suburbs. The relationship between the flood risk zones and the potential affected population are evaluated using the technique of overlay mapping. These results are expected to aid planning and disaster prevention managers in making informed decisions to avert hazards. The flooding risk can be reduced by implementing projects which alter the area’s topography.
5.0 Summary
The analysis from the overlay mapping technique indicates that the rainfall patterns influence the risk of flooding in the suburbs. The population would be affected and hence further evaluations on the effective methods to reduce the flood risk should be considered. The valid method for the case is intersect which indicates the area at risk and the population numbers. Further research on the rainfall trends in the areas and other factors contributing to flooding should be assessed. The results are important as they offer guidelines for future planning of the suburb area of Adelaide.
6.0 Appendices
6.1 Appendix A: Flow Chart
6.2 Appendix B: Map
6.3 Log File
7.0 References List
Bailey, R. G., 1988. Problems with using overlay mapping and their implications for Geographic Information Systems. Environmental Management, vol. 12, (no. 1), p.11-17.
Deckers, P, et al., 2009. A GIS for Flood Risk Management in Flanders. Geospatial techniques in Urban Hazard and Disaster Analysis, 2nd ed., Springer, Dordrecht.
De Smith, W, et al. 2015. Computer Analysis of Images and Patterns: 15th International Conference Proceedings, Caip, York, UK.
Esri. 2017. Intersect—Help ArcGIS Desktop. Available at http://pro.arcgis.com/en/pro-app/tool-reference/analysis/intersect.htm. [Accessed 9 Nov. 2017].
Fowler, G., Takeuchi, Y. 2009. Mapping, climate and geographic information for risk analysis. Plant pest risk analysis: concepts and application, vol. 3, (no. 1) p.151-163.
Yeniguna, K, and Ruken E. 2013. Overlay Mapping trend analysis and its application in Euphrates Basin, Turkey. Meteorological Applications, vol. 20.
