Ludick, Chantel JVan Heerden, Quintin2022-08-192022-08-192022-07Ludick, C.J. & Van Heerden, Q. 2022. A multi-objective optimization approach for disaggregating employment data. <i>Geographical Analysis.</i> http://hdl.handle.net/10204/124750016-73631538-4632https://doi.org/10.1111/gean.12328http://hdl.handle.net/10204/12475In many countries, including South Africa, data on employment is rarely available on a downscaled level, such as building level, and is only available on less detailed levels, such as municipal level. The aim of this research was to develop a methodology to disaggregate the employment data that is available at an aggregate level to a disaggregate, detailed building level. To achieve this, the methodology consisted of two parts. First, a method was established that could be used to prepare a base data set to be used for disaggregating the employment data. Second, a multiobjective optimization approach was used to allocate the number of employment opportunities within a municipality to building level. The algorithm was developed using an Evolutionary Algorithm framework and applied to a case study in a metropolitan municipality in South Africa. The results showed favorable use of multiobjective optimization to disaggregate employment data to building level. By enhancing the detail of employment data, planners, policy makers, modelers and other users of such data can benefit from understanding employment patterns at a much more detailed level and making improved decisions based on disaggregated data and models.FulltextenEmployment dataEmployment data disaggregatingArticleLudick, C. J., & Van Heerden, Q. (2022). A multi-objective optimization approach for disaggregating employment data. <i>Geographical Analysis</i>, http://hdl.handle.net/10204/12475Ludick, Chantel J, and Quintin Van Heerden "A multi-objective optimization approach for disaggregating employment data." <i>Geographical Analysis</i> (2022) http://hdl.handle.net/10204/12475Ludick CJ, Van Heerden Q. A multi-objective optimization approach for disaggregating employment data. Geographical Analysis. 2022; http://hdl.handle.net/10204/12475.TY - Article AU - Ludick, Chantel J AU - Van Heerden, Quintin AB - In many countries, including South Africa, data on employment is rarely available on a downscaled level, such as building level, and is only available on less detailed levels, such as municipal level. The aim of this research was to develop a methodology to disaggregate the employment data that is available at an aggregate level to a disaggregate, detailed building level. To achieve this, the methodology consisted of two parts. First, a method was established that could be used to prepare a base data set to be used for disaggregating the employment data. Second, a multiobjective optimization approach was used to allocate the number of employment opportunities within a municipality to building level. The algorithm was developed using an Evolutionary Algorithm framework and applied to a case study in a metropolitan municipality in South Africa. The results showed favorable use of multiobjective optimization to disaggregate employment data to building level. By enhancing the detail of employment data, planners, policy makers, modelers and other users of such data can benefit from understanding employment patterns at a much more detailed level and making improved decisions based on disaggregated data and models. DA - 2022-07 DB - ResearchSpace DP - CSIR J1 - Geographical Analysis KW - Employment data KW - Employment data disaggregating LK - https://researchspace.csir.co.za PY - 2022 SM - 0016-7363 SM - 1538-4632 T1 - A multi-objective optimization approach for disaggregating employment data TI - A multi-objective optimization approach for disaggregating employment data UR - http://hdl.handle.net/10204/12475 ER -25901