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A workflow for geocoding South African addresses
There are many industries that have long been utilizing Geographical Information Systems (GIS) for spatial analysis. In many parts of the world, it has gained less popularity because of inaccurate geocoding methods and a lack of data standardization. Commercial services can also be expensive and as...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2016
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| _version_ | 1867614205476077568 |
|---|---|
| access_status_str | Open Access |
| author | Van Rensburg, Alexandria |
| author2 | Berman, Sonia |
| author_browse | Berman, Sonia Van Rensburg, Alexandria |
| author_facet | Berman, Sonia Van Rensburg, Alexandria |
| author_sort | Van Rensburg, Alexandria |
| collection | Thesis |
| description | There are many industries that have long been utilizing Geographical Information Systems (GIS) for spatial analysis. In many parts of the world, it has gained less popularity because of inaccurate geocoding methods and a lack of data standardization. Commercial services can also be expensive and as such, smaller businesses have been reluctant to make a financial commitment to spatial analytics. This thesis discusses the challenges specific to South Africa as well as the challenges inherent in bad address data. The main goal of this research is to highlight the potential error rates of geocoded user-captured address data and to provide a workflow that can be followed to reduce the error rate without intensive manual data cleansing. We developed a six step workflow and software package to prepare address data for spatial analysis and determine the potential error rate. We used three methods of geocoding: a gazetteer postal code file, a free web API and an international commercial product. To protect the privacy of the clients and the businesses, addresses were aggregated with precision to a postcode or suburb centroid. Geocoding results were analysed before and after each step. Two businesses were analysed, a mid-large scale business with a large structured client address database and a small private business with a 20 year old unstructured client address database. The companies are from two completely different industries, the larger being in the financial industry and the smaller company an independent magazine in publishing. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/16198 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:48:20.718Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/16198 A workflow for geocoding South African addresses Van Rensburg, Alexandria Berman, Sonia Computer Science There are many industries that have long been utilizing Geographical Information Systems (GIS) for spatial analysis. In many parts of the world, it has gained less popularity because of inaccurate geocoding methods and a lack of data standardization. Commercial services can also be expensive and as such, smaller businesses have been reluctant to make a financial commitment to spatial analytics. This thesis discusses the challenges specific to South Africa as well as the challenges inherent in bad address data. The main goal of this research is to highlight the potential error rates of geocoded user-captured address data and to provide a workflow that can be followed to reduce the error rate without intensive manual data cleansing. We developed a six step workflow and software package to prepare address data for spatial analysis and determine the potential error rate. We used three methods of geocoding: a gazetteer postal code file, a free web API and an international commercial product. To protect the privacy of the clients and the businesses, addresses were aggregated with precision to a postcode or suburb centroid. Geocoding results were analysed before and after each step. Two businesses were analysed, a mid-large scale business with a large structured client address database and a small private business with a 20 year old unstructured client address database. The companies are from two completely different industries, the larger being in the financial industry and the smaller company an independent magazine in publishing. 2016-01-02T05:21:50Z 2016-01-02T05:21:50Z 2015 Master Thesis Masters MPhil http://hdl.handle.net/11427/16198 eng application/pdf Department of Computer Science Faculty of Science University of Cape Town |
| spellingShingle | Computer Science Van Rensburg, Alexandria A workflow for geocoding South African addresses |
| thesis_degree_str | Master's |
| title | A workflow for geocoding South African addresses |
| title_full | A workflow for geocoding South African addresses |
| title_fullStr | A workflow for geocoding South African addresses |
| title_full_unstemmed | A workflow for geocoding South African addresses |
| title_short | A workflow for geocoding South African addresses |
| title_sort | workflow for geocoding south african addresses |
| topic | Computer Science |
| url | http://hdl.handle.net/11427/16198 |
| work_keys_str_mv | AT vanrensburgalexandria aworkflowforgeocodingsouthafricanaddresses AT vanrensburgalexandria workflowforgeocodingsouthafricanaddresses |