Application of Google Street View Images in Identifying Mobility Patterns in Small and Intermediate Towns (Kiambu County, Kenya)
Google Street View (GSV) is a useful tool for providing a visual representation of the field setting, supplementing two-dimensional mapping data. GSV allows users to remotely access streets across the world, overlaying multiple images taken at different times, and identifying changes in locations ov...
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| Udgivet i: | URI:https://journals.openedition.org/sources, |
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| Hovedforfatter: | |
| Format: | Article ou chapitre numérique |
| Sprog: | Anglais |
| Udgivet: |
Sources
2024
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| Fag: | |
| Online adgang: | Accès Université d'Orléans et IFPM Accès Université d'Orléans et IFPM |
| Summary: | Google Street View (GSV) is a useful tool for providing a visual representation of the field setting, supplementing two-dimensional mapping data. GSV allows users to remotely access streets across the world, overlaying multiple images taken at different times, and identifying changes in locations over time. GSV images have been applied in various disciplines, including public health, urban design, architecture, geography, ecology, and criminology. Virtual fieldwork and virtual reality environments have been used in geographical research, training, and also in enabling access to distant, hazardous, and remote sites. The use of technology in research is expanding, with researchers conducting interviews remotely through online surveys, phone interviews, and video conferencing. This paper explores the use of Google Street View (GSV) in a systematic pre-fieldwork virtual observation of mobility patterns along the milk value chain in Kenya. Google Street View (GSV) images of a 47-kilometer transect between Ruiru town and Uplands village centre in Kiambu County, Kenya, are used to observe mobility-related activities linked to the milk value chain and identify issues that need further investigation during actual fieldwork. The study was part of a PhD study on mobility patterns in small and intermediate towns and their impact on urban-rural linkages. 76 GSV photographs were cropped from selected views on the computer screen and the link of their geographical location was copied for easier navigation during further inquiry. To corroborate the information from the GSV photos, actual fieldwork was carried out between 2019 and 2021 during which 57 respondents were interviewed, including farmers, milk vendors, and transporters. The paper discusses the potential applications of this tool and its limitations. GSV was used in this study to provide a pre-fieldwork exploratory overview of the various activities along the milk values chain. It provided an overview of the study area and helped identifying issues needing further investigation during actual fieldwork. GSV was also an alternative source of photographic data, especially of respondents on the move. A notable limitation of GSV was that it was not able to show the time variations of mobility, since the images were taken at a specific time. The images were also limited to major access roads and did not cover the adjoining roads connecting to the main transect. The paper concludes that while GSV images were a useful source of data for this research, particularly during the preliminary stages of reconnaissance, there was a need to supplement this visual data with actual field data. The virtual reconnaissance gives an idea of what is happening in an area and provides the researcher with queries about what should be ascertained during the actual fieldwork or from secondary sources of data. The study also notes that there are limitations to interpreting the visual data from some of the images without interviewing the actors or without having prior knowledge of the activities that are taking place. |
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