{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/16198"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/16198","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"A workflow for geocoding South African addresses","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Van Rensburg, Alexandria"],"institution":"Department of Computer Science","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Berman, Sonia"],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015","date_published":"2015","updated_at":"2026-07-22T22:23:42Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/16198","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Berman, Sonia"]},{"key":"dc:creator","label":"Author","values":["Van Rensburg, Alexandria"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-01-02T05:21:50Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-01-02T05:21:50Z"]},{"key":"dc:date.issued","label":"Date","values":["2015"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Computer Science"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["MPhil"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/16198"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["A workflow for geocoding South African addresses"]}]}],"canonical_facts":{"dc:contributor.advisor":["Berman, Sonia"],"dc:creator":["Van Rensburg, Alexandria"],"dc:date.accessioned":["2016-01-02T05:21:50Z"],"dc:date.available":["2016-01-02T05:21:50Z"],"dc:date.issued":["2015"],"dc:description.abstract":["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. 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