Data Wrangling for South African Smart City Crime Data
Pages 198 - 209
Abstract
South Africa (S.A.) is currently facing economic and social challenges that could benefit from the implementation of international smart city guidelines. Crucial to transforming a city into a smart city is the collection and access to reliable data. One of the main problems experienced by S.A. cities is the limited access to data, resulting from a traditionally fragmented approach to data collection, sharing and use. Crime-related data is one of the most commonly collected datasets in smart cities. In S.A., crime data is predominantly collected by the S.A. Police Services (SAPS) and security companies. While the latter are not readily available for public use, SAPS crime data is consolidated and disseminated at the national level. Initial data exploration, however, shows that temporal, spatial and structural inconsistencies in the data limits the usefulness of available crime data. In this study, the inconsistencies in SAPS crime data are summarised, and standard data wrangling techniques are implemented and evaluated to clean the data. The study proposes a data wrangling model for S.A. crime data. Furthermore, this study will further developments that could benefit S.A. cities in general as they transform into smart cities.
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Published In
September 2020
258 pages
ISBN:9781450388474
DOI:10.1145/3410886
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Association for Computing Machinery
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Published: 14 September 2020
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SAICSIT '20
SAICSIT '20: Conference of the South African Institute of Computer Scientists and Information Technologists 2020
September 14 - 16, 2020
Cape Town, South Africa
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Overall Acceptance Rate 187 of 439 submissions, 43%
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