A Multi‐objective Optimization Approach for Disaggregating Employment Data
Bibliographic Data
| ID | 21321772 |
|---|---|
| Authors | Chantel Ludick (0000-0002-5871-8125, Department of Geography, Geomatics and Meteorology University of Pretoria Pretoria South Africa, corresponding author), Quintin van Heerden (0000-0002-6217-9849, Council for Scientific and Industrial Research Pretoria South Africa) |
| Year | 2023 |
| Volume | 55 |
| Issue | 2 |
| Pages | 300-324 |
| Publication date | 2023-04-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Geographical Analysis (JOURNAL) |
| Journal identifiers | ISSN: 0016-7363 • E-ISSN: 1538-4632 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/gean.12328 |
| OpenAlex | W4283756789 |
| Language | EN |
| References cited | 18 |
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
Aggregate (composite) · Aggregate data · Data set · Geography · Machine learning · Metropolitan area · Multi-objective optimization · Operations research · Set (abstract data type) · Statistics · Artificial Intelligence · Computer Science · demographic modeling and climate adaptation · Engineering · Mathematics · Transportation Planning and Optimization · Urban Transport and Accessibility
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| Citation velocity | historical |
|---|---|
| Highly cited | No |