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Application of Google Street View Images in Identifying Mobility Patterns in Small and Intermediate Towns (Kiambu County, Kenya)

Utilisation des images de Google Street View pour identifier les formes de mobilité dans les petites et moyennes villes (comté de Kiambu, Kenya)
Uso de imagens do Google Street View na identificação de padrões de mobilidade em cidades pequenas e intermédias (Kiambu County, Quénia)
Jackson Kago

Abstracts

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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Author’s notes

This research was funded through a PhD scholarship by the French Embassy in Nairobi, Kenya, and a research grant from the French Institute for Research in Africa (IFRA).

Full text

Data related to this article: “Application of Google Street View Images in Identifying Mobility Patterns in Small and Intermediate Towns (Kiambu County, Kenya): Google Hyperlinks and Screenshots.”
https://doi.org/​10.34847/​nkl.ec8301xc.
Includes a list of the URLs of the Google Street Views used in this research work, as well as the 18 screenshots from these views reproduced in this article.
See also at the end of this article, the Appendix section: “GSV 360° images used in this article.”

Introduction

  • 1 Discussing the use of aerial or satellite images goes beyond the scope of this paper.

1Google Street View (GSV) has revolutionised geographical knowledge production by allowing remote viewing of activities taking place along a street. GSV was launched in 2007 in the United States of America and is now used widely across the world. Before the advent of this technology, systematic observation of street level activities1 was carried out by walking and observing activities in the field that necessitated travel to the study area, which could be costly and time-consuming especially for long-distance or foreign travel. The actual observation can be tiring, and the adverse effects of weather such as hot climate, rain, or wind could affect the researcher. A quick exploratory observation of the research area can be undertaken by driving around the study area using private vehicles, taxis, or motorcycles—“windshield surveys.” During the drive, the data could be recorded in photographs or videos. One challenge of such a drive-through is that it is not easy for the observer to note the issues on-site while simultaneously taking photographs. Reminiscing about a reconnaissance trip to Kampala, Uganda, for a PhD research in the 1990s, a French researcher I interviewed recalled that he took a taxi and drove around the city taking photos because he was new in the city and had limited time to walk around, observe and understand the general context of Kampala. Comparing this experience with the use of GSV, such a survey could have been undertaken virtually. When photos are taken in a hurry, for instance from a moving vehicle, the quality may be compromised, a challenge that is highlighted in this study. Additionally, the researcher might not have time to observe and contextualise the activities taking place when making a rapid reconnaissance visit. As Campanella (2017, 4) points out, “Street View is free, convenient, quick, data-rich, spatially integrated, temporally deep, regularly updated, and constantly expanding.”

  • 2 Google explains its procedures as follows: “When multiple nearby 360 photos are connected through (...)

2Three broad stages in fieldwork are accepted in ethnography: pre-fieldwork preparation, the actual fieldwork, and post-fieldwork (Caine et al. 2009; Carlson 2007; Patton 2014). Pre-fieldwork preparation is important before undertaking the actual fieldwork. It includes identifying the actors, understanding the context, analysing strategies to gain entry into the field, and collecting pre-fieldwork data such as maps. GSV can be useful in providing a visual of the field setting to supplement two-dimensional mapping data such as satellite imagery and aerial photos. By using GSV, one can remotely access streets around the world by searching those locations on Google Maps and getting a 360 degree image of the location. Google has in addition been able to overlay several images taken at a specific location at different times, enabling the viewer to identify changes in that location over time. In future, this will be a rich source of historical visual data that can be likened to time series analysis of satellite images. This time-series street-level imagery has been used to measure changes in the physical appearance of neighbourhoods (Naik et al. 2017). GSV images are taken by Google’s personnel or individual contributors2 creating a wealth of information. It has been applied in various disciplines such as public health, urban design, architecture, geography, ecology, and criminology (Bader et al. 2017; Curtis et al. 2013; Kim et al. 2021; Li et al. 2017, 2018; Odgers et al. 2012; Rundle et al. 2011; B.T. Taylor et al. 2011). Furthermore, virtual fieldwork and virtual reality environments have been applied in geographical research and training, making it possible to access distant, hazardous, and remote sites; and also assist students with physical challenges to access geographic information (Bond et al. 2022; Carmichael and Tscholl 2013; Cliffe 2017; Cook 2015; McMorrow 2005; Stokes et al. 2012; R. Taylor 2005). Through the use of high-resolution imagery obtained from Google Earth, Thomas et al. (2008) carried out an archaeological reconnaissance of Afghanistan. The actual fieldwork was impossible because of the ongoing armed conflict, and even in safer areas, there were challenges of inaccessible terrain. Myers (2010) was able to use Google Earth Imagery to carry out a virtual mapping exercise of Camp Delta, a top-secret US military base and prison in Guantanamo Bay, Cuba. Virtual trips have been made possible through Google Earth which integrates GSV and is applied in geographical training (Cliffe 2017; Fearnley and Bunting 2011; France et al. 2015; Kingston et al. 2012; Whitmeyer et al. 2012). The use of technology in research is expanding, and researchers can conduct interviews remotely through online surveys, phone interviews, and video conferencing, which have various advantages and limitations. With emerging technological advancements and broadening internet connectivity, the use of virtual research-based tools is most likely to increase.

3This paper discusses the application of GSV as part of my PhD research along a 47-kilometre transect between Ruiru town and Uplands village centre in Kiambu County, Kenya. The first section of the paper gives an introduction and background of the application of GSV and visual research methods. The second section outlines the methodology and approach used in this study, while the third section reports the application of GSV in the study. Section four of the paper discusses the potential application and limitations of this source of visual data and gives a critique of the potential use of GSV as a data source in research. The last section is the conclusion.

Methodology

  • 3 Since the completion of this research, the section from Githunguri to Uplands has now been covered (...)
  • 4 Equipe EXARMAS. 2019.“ EXARMAS#11 : Illustrer son terrain au Kenya.” EXARMAS. EXposition des Arts à (...)

4The paper uses GSV photographs taken in 2018 and 2019 to undertake a systematic pre-fieldwork observation of various mobility-related activities related to the milk value chain along a 47-kilometre transect. Virtual pre-fieldwork was carried out after undertaking a literature review to have an understanding of the general activities and actors in the milk value chain. This exploratory study was part of my PhD study on, “Mobility Patterns in Small and Intermediate Towns and its Implication on Urban-rural Linkages: A Focus on the Milk Value Chain” (Kago 2022). A major part of the exploratory study with GSV was undertaken while in Bordeaux, France, during a 3-month stay between October and November 2018 as part of the PhD. In order to corroborate the information from the GSV photos, actual fieldwork was carried out between 2019-2021. 57 respondents were interviewed including farmers, milk vendors, and transporters. At the time of the study, the GSV images were available for the section of the transect between Ruiru to Githunguri3 (See View 1 and 2). 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. 25 of the photographs were used in the thesis, together with 64 other photographs taken during the actual fieldwork. An initial comparison of the images taken through GSV and those taken during the actual field work was explored during a photo exhibition titled “Illustrer son terrain au Kenya. Photos personnelles et usage de Google Street View ” held between 26th November 2019 and 16th January 2020 at Maison des Suds, Bordeaux, France.4

View 1. Above: The Ruiru-Uplands transect (47 km); below: The Ruiru-Githunguri section (25.4 km) (OpenStreetMap)

View 1. Above: The Ruiru-Uplands transect (47 km); below: The Ruiru-Githunguri section (25.4 km) (OpenStreetMap)

Source: OpenStreetMap, 2024. Links: above: below.

5

View 2. The Ruiru-Uplands transect (47 km) (Google Maps)

Images © Landsat / Copernicus, Images © 2024 TerraMetrics, Geodata © 2024 Google. 

Area of Study: The Transect

6The study area is a 47 kilometre transect from Uplands village centre to Ruiru town (See Map 1 and Map 2, View 1 and View 2 above) in the peri-urban part of the City of Nairobi. The upper part of the transect is characterised by a high population density, with smallholder farms of an average of 0.045 hectares, and larger plantations on the lower part of the transect near Ruiru town of an average of 69.5 hectares (County Government of Kiambu 2018). The transect is located along the slopes of the Aberdare Ranges and has a diversity of geophysical and geo-climatic features from the highest point sloping towards the lower points in Ruiru town. The altitude ranges from 1,500 to 2,400 metres above sea level. The upper sections of the transect are characterised by steep V-shaped valleys that run at an average depth of 50 metres. This renders the implementation of infrastructure difficult, while the lower locations are flat or have gentle slopes. The dairy farmers along the transect practice zero grazing, where animals are enclosed in a structure, rather than left to graze openly. This kind of dairy farming is necessitated by the reducing land size among smallholder farmers.

Map 1: Study area

Map 1: Study area

Source: Author.

7The section between Uplands and Githunguri is a tea-farming zone, while the section between Githunguri and Ruiru is a coffee-farming zone. The diminished returns from farming tea and coffee has led to diversification towards other farming activities, including dairy farming. The observation of tea hawking activities indicates a decline in the tea sector, cash flow issues, and a lack of confidence in payments to be received from the tea buyers. The phenomenon of tea hawking is a national challenge in the growth of the tea sector, where farmers in need of quick cash sell unprocessed tea to middlemen who pay immediately, rather than wait for the monthly payment from tea buyers. The exploitative prices the middlemen usually pay for the tea lead to low returns for the farmers. Not only is tea hawking illegal in Kenya, it is also an indication of poverty and its prevalence sustains the cycle of poverty. Figure 1 shows tea hawking taking place, an activity that would have been difficult to capture during an actual fieldwork study due to its illegal nature.

Figure 1: Tea hawking in Githunguri

Figure 1: Tea hawking in Githunguri

Location: geo:-1.0639472,36.764294. May 2018.
Original view: https://maps.app.goo.gl/​ZJ6sbk3KXemzFr187.

© Google Earth/GSV, 2018.

8While the image gives an idea that there may be an exchange taking place, the whole story behind the image is not available to the researcher, necessitating more investigation during actual fieldwork. Notably, one of the respondents in green is keeping watch of the passing GSV vehicle, raising privacy concerns about the use of GSV. By default, all human faces and vehicle registration plates that might appear on the images are blurred before being published on GSV, however, the issue of privacy remains a concern, as I will discuss below.

Application of Google Street View in the study

9This section discusses various ways in which GSV was applied in this study in the identification of the various activities related to the milk value chain, from production to distribution and their occurrence along the transect.

Identifying movements related to the milk value chain

10Milk production necessitates the mobility of farmers to other surrounding areas in search of animal feeds.

Sourcing of fodder for animals

11These activities were visible with GSV along the transect using different means of transport that depended on the flock size, affordability, distance, terrain, and seasons, among others. The movement of smallholder farmers was corroborated by actual fieldwork that revealed that the smallholder farmers from the northwestern, upper part of the transect get grass from the large coffee plantations on the southeastern, lower part of the transect (see Figure 2 and Map 1). The upper part of the transect is densely populated with smaller parcels of land compared to the lower parts of the transect which have large coffee plantations. In-between the coffee plantations, some sections are not suitable for growing coffee because of poor drainage or the nature of the soils. These sections are left as grassland on which the smallholder farmers cut and collect grass for their cattle (with the permission of farm management).

Figure 2: Transportation of grass along Ruiru-Uplands Road

Figure 2: Transportation of grass along Ruiru-Uplands Road

Location: geo:-1.1136152,36.8980425. May 2018.
Original view: https://maps.app.goo.gl/​gZCnFxUA9aTuZQnw6.

© Google Earth/GSV, 2018.

12Additionally, due to the decline in the coffee sector, some of the large coffee plantations are neglected, resulting in the growth of bushes and grass, which the farmers use. Some of the coffee bushes are being cleared and the land converted to real estate and industrial development, giving rise to the growth of grass and weeds which the farmers use as fodder (see Figure 3). “Tatu City” (see Map 1), formerly Tatu Coffee Estate, an ongoing real estate and industrial new town, is one such zone where farmers pay 800-1,500 KES (Kenyan shillings) to cut grass from inside the plantation, depending on the mode of transport they are using (which correlates with the amount of grass they can collect). The farmers use various modes of transport ranging from trucks, pickups, motorcycles, bicycles, and donkey-pulled carts.

Figure 3: A farmer buying grass from casuals who cut it from farms previously used to grow coffee along Kigumo Road

Figure 3: A farmer buying grass from casuals who cut it from farms previously used to grow coffee along Kigumo Road

Location: geo:-1.090055,36.9428445. May 2018.
Original view: https://maps.app.goo.gl/​2mFmicYXwDuWcYNU6.

© Google Earth/GSV, 2018.

Milk collection

  • 5 The images may have been taken about 10 to 11 am given the activities that were taking place and t (...)

13GSV allowed for the identification of the delivery of milk from the farms to the collection centres through non-motorised modes of transport ― given the short distances of about one kilometre and low quantities of milk involved. Farmers indeed use wheelbarrows, bicycles, and handcarts to deliver milk, depending on the amount of milk they produce. Milk collection occurs at specific times in the morning and evening, namely: 2 am to 5 am in the morning, and 1 pm to 3 pm in the afternoon. Thus, GSV did not capture milk collection activities, except at one section of the transect between Githunguri and Matuguta village centre.5 On the other sections of the transect, the milk collection centres were visibly closed. Through GSV, it was possible to identify farmers travelling back to their farms after delivering milk to the collection centre (see Figure 4).

Figure 4: Non-motorised modes of transport used in milk collection

Figure 4: Non-motorised modes of transport used in milk collection

Location: geo:-1.0675221,36.7641333. May 2018.
Original view: https://maps.app.goo.gl/​e5zCnR8SoD5hjuhU9.

© Google Earth/GSV, 2018.

14GSV also captured farmers waiting to have their milk measured and recorded at the Mung’u Junction Milk Collection Centre between Githunguri and Githiga towns (see figures 5 and 6).

Figure 5: Mung’u Junction Milk Collection Centre

Figure 5: Mung’u Junction Milk Collection Centre

Location: geo:-1.0739163,36.7531906. May 2018.
Original view: https://maps.app.goo.gl/​e5zCnR8SoD5hjuhU9.

© Google Earth/GSV, 2018.

Figure 6: Mung’u Junction Milk Collection Centre

Figure 6: Mung’u Junction Milk Collection Centre

© Jackson Kago, 2018.

15For this particular study, there were no images captured at night, whereas milk collection also takes place at night. Milk collection converges about 100-150 farmers when they are delivering milk at the centre early in the morning and in the afternoon. This convergence of people at the milk collection centres, coupled with increased cash flow from the sale of milk, attracts non-farm business activities such as animal feeds outlets, restaurants, general shops, mobile money transfer outlets, hay businesses, and chicken waste outlets, among others. These activities ignite the growth of the villages and market centres and their subsequent transformation into small towns.

  • 6 For copyright purposes it is very unlikely that Google will allow the download of full quality ima (...)

16The quality of the photos that I was able to crop from GSV was not as high as those I had taken during fieldwork. Figure 6 shows the same view of the milk collection centre captured during my fieldwork. In addition, there was more flexibility in capturing my own images compared to acquiring images from GSV in terms of zooming in to get the required details and moving closer to the area of interest. Zooming into a GSV image on the screen lowers its quality. Nonetheless, the GSV images used in my dissertation capture particular scenes or activities that I did not encounter during fieldwork, thus offering an alternative data source for photographic images. Perhaps Google could create options for users to download better-quality images6.

Identifying the frequency of occurrence of milk-related activities

17Further, GSV allowed for the frequency of occurrence of the milk value chain activities in certain locations along the transect to be noted. This was a useful step in guiding the actual fieldwork, because it gave an idea of what was taking place at various sections along the transect. GSV has been applied in other studies to quantify the rate of occurrence of certain phenomena. Li et al. (2018) used GSV to quantify the shade provision of street trees in urban landscapes. The series of photographs in Figure 7 shows the cutting and selling of grass along Kigumo Road. The grass was cut from abandoned farms where coffee was previously cultivated. One sack was sold for about 200 KES (see Figure 7).

Figure 7: Cutting and transporting grass along the transect

Figure 7: Cutting and transporting grass along the transect

May 2018. Location:
7a: geo:-1.1054042,36.9603564. https://maps.app.goo.gl/​nxTURk5cna6xJtix5.
7b: geo:-1.1033338,36.958434. https://maps.app.goo.gl/​FrdKRbkLK7AQp2KWA.
7c: geo:-1.097047,36.9530279. https://maps.app.goo.gl/​VM2xFtW39XKP4jgs8.
7d: geo: -1.0846133,36.9346798. https://maps.app.goo.gl/​KAfDumhCC11j1Y4S8.
7e: [unretrieved].

© Google Earth/GSV, 2018.

18GSV was also useful in locating the sale of chicken droppings, a cheaper source of feeds for dairy cattle. The area along the transect does not have large chicken farms, except a few around Ruiru town. Thus, the chicken waste was obtained from small-scale poultry growers in other regions such as Ngong, Thika, Kitengela, and Isinya, which have large chicken farms. Figure 8 shows a cluster of four outlets of chicken waste along Ruiru-Uplands Road near Githunguri town. At a glance, it may not be possible to tell what the structure is about, thus the visual image taken with GSV needed to be corroborated with interviews to relate the visual information with actual field data and give it meaning.

Figure 8: Sale of recycled chicken waste along Ruiru-Uplands Road

Figure 8: Sale of recycled chicken waste along Ruiru-Uplands Road

Location: geo:-1.0604012,36.7830435. May 2018.
Original view: https://maps.app.goo.gl/​auBXoNeKVCMTCA9a6.

© Google Earth/GSV, 2018.

19Having viewed the image virtually with GSV before the actual fieldwork, I was compelled to find out what was taking place in the structures and fill in the details of the story behind the image. Following my inquiries, I interviewed Mr. Mucheru, who owned one of the businesses. He informed me that he buys the feeds in Gatukuyu near Thika town, an area that specialises in poultry farming, and then transports them in his pickup for sale in Githunguri town. While the digital walk-through along the transect may leave information gaps, it helps to create questions that can be answered during the actual fieldwork. Photographs have been used in visual surveys to elicit information from respondents. While the study did not apply this photo elicitation technique, the images documented through GSV before the fieldwork were able to ignite inquiry on various aspects. The images ignited questions such as “What is happening?”, “Why is this taking place?”, “Who are the actors?” GSV already partially answered the question of where the phenomenon was taking place, or the frequency of a particular phenomenon, thus giving the researcher an advantage during fieldwork.

20GSV was also useful in establishing the critical role of the towns and market centres in the milk value chain. I was indeed able to identify a cluster of 39 businesses (see Map 2) directly related to milk production in Githunguri town centre. With GSV, it was possible to undertake a slow and uninterrupted virtual walk along the streets with long pauses and “virtual gazes” at the shops to identify the names of the businesses, what was being sold, the loading and offloading of products, without raising suspicion or unnecessary curiosity from the business owners and passers-by. Staring at a street façade could make a researcher self-conscious and lead to a rushed walk along the street missing some details. Self-consciousness in ethnographical research has been documented in the 20th century in terms of the personal bias it may create because of among others gender, age, race, ethnicity, religion, cultural background, and existing personal factors like anxiety during fieldwork (Coffey 1999; Nash et al. 1972; Stoeltje et al. 1999). Coffey (1999) indicates that it is important for researchers to be aware of ways in which fieldwork can affect researchers and how the researchers themselves can affect the field.

Map 2: Dairy farming-related business activities in Githunguri town centre

Map 2: Dairy farming-related business activities in Githunguri town centre

Source: Jackson Kago, adapted from Google Maps, 2018.

21Through the study of the streets, it became apparent that Githunguri emerges as a specialised “milk” town oriented towards milk production and attracts suppliers and service providers leading to the further development of the town. This was evident in the number of businesses that are involved directly or indirectly in milk production (see Figure 9). The duplication of these similar retail facilities was an indication of the demand for farm inputs and of a thriving dairy sector. Further fieldwork data revealed that urban centres are points of convergence, as farmers move to these areas to buy goods and get services, and a point of divergence of goods and services, from the centres to the rural areas ― for instance as veterinary doctors visit the farmers for artificial insemination and clinical services for their animals.

Figure 9: Sale of animal feeds in Githunguri town

Figure 9: Sale of animal feeds in Githunguri town

Location: -1.0571251,36.7766525. May 2018.
View: https://maps.app.goo.gl/​29M7ko1eebxc9yip6.

© Google Earth/GSV, 2018.

22GSV provides “stitched” images of activities taking place in space that a researcher can refer to continuously, even after the end of fieldwork. During fieldwork, a researcher relies on memory to get a mental picture of what they observed in space. While this is aided by scattered images that are taken during the fieldwork (a researcher may shy away from taking “excessive” photos of actions happening in space to avoid raising reactions from the respondents), certain parts of the scene may be excluded from the mental image. It may also not be easy to recall all the details of what was observed along particular streets in the absence of panoramic photographs. GSV provides “a large visual repository for retrieval without an actual experience to encode it in a particular way” (Gilge 2016, 481) that a researcher can refer to when touring remotely.

23The clustering of milk-related businesses is not confined to Githunguri town but also shapes the structure of the village and market centres like Ngewa and Githiga, which have also experienced similar linear growth, with the presence of animal feed shops and sale of hay along the roads. GSV made it possible to identify clusters of retail outlets along the main roads dealing with processed animal feeds, hay, and chicken waste (see Figure 10). Business operators prefer roadside locations because of visibility and accessibility.

Figure 10: Sale of hay at Ngewa Market Centre along the Ruiru-Uplands transect

Figure 10: Sale of hay at Ngewa Market Centre along the Ruiru-Uplands transect

Location:-1.0931704,36.8654678. May 2018.
Original view: https://maps.app.goo.gl/​Q1DMrBzDiETdEt6X6.

© Google Earth/GSV, 2018

24Lastly, GSV allows for the identification of clusters of support businesses, for instance, transport providers such as pickup vehicles and bodaboda (motorcycle) operators, strategically located at road junctions within the towns and village centres (see Figure 11). Furthermore, it was possible with GSV to compare the kind of growth taking place in Githunguri “milk” town, and Ruiru town, which is driven by real estate and industrial growth due to its close proximity to Nairobi City. The presence of a cluster of hardware shops and furniture shops in Ruiru town (see figure 12) indicates its residential function, as a dormitory town of Nairobi.

Figure 11: Pickup trucks awaiting transport business in Githunguri town

Figure 11: Pickup trucks awaiting transport business in Githunguri town

Location: geo:-1.0577454,36.7757861. May 2018.
Original view: https://maps.app.goo.gl/​6dW38vqEezfQJwtFA.

© Google Earth/GSV, 2018.

Figure 12: A cluster of carpentry shops along Ruiru-Kamiti Road in Ruiru town

Figure 12: A cluster of carpentry shops along Ruiru-Kamiti Road in Ruiru town

Location: geo:-1.1496265,36.9530532. May 2018.
View (different time): https://maps.app.goo.gl/​LwzwLWuKSh5tw9xVA.

© Google Earth/GSV, 2018.

Avoiding bureaucracy in the acquisition of photographs

25Through a Google Maps search of the East African Breweries Limited (EABL) headquarters located in Ruaraka about 35 kilometres from Githunguri town and a virtual walk along the roads adjoining the company, I was able to identify pickup trucks and lorries waiting to be loaded with machicha (brew waste - a popular feed for cows among the farmers interviewed), and pickup trucks leaving the dispensing point (see Figure 13).

Figure 13: A lorry at the machicha dispensing station at EABL in Ruaraka

Figure 13: A lorry at the machicha dispensing station at EABL in Ruaraka

Location: geo:-1.2353859,36.8820851. February 2018.
Original view: https://maps.app.goo.gl/​wwsf6AFi5VKGR9U77.

© Google Earth/GSV, 2018.

26Machicha is a byproduct of beer-making and is a barley product that is rich in energy and protein from spent yeast. It was a popular animal feed among the farmers interviewed. Usually, field study of such a zone involving the taking of photographs would necessitate a bureaucratic procedure to get the necessary permissions from the brewery itself, irrespective of whether or not permission was obtained from the National Research Agency. This is due to the economic importance of the company to the Kenyan economy and the threats of terrorism. Thus, taking photographs in such an area would be controlled or viewed with suspicion. The area is also patrolled by numerous security personnel, alongside signs of “restricted area,” “no entry,” and “no parking allowed” displayed across various points along the road. Although a researcher would be interested in taking a photo to illustrate the activities taking place in such a location, it is unlikely they would be able to do that.

27Approximately 250 farmers and private traders known as transporters are registered by an agent of the company. Each farmer or machicha trader was allocated one or two days a week to collect the product from the factory. However, in March 2020, the company selected a distributor who set up distribution points in various parts of Kiambu County, where farmers could access it more conveniently.

Police involvement in the milk value chain

28The presence of police barriers in sections of the road was visible with GSV. Locating them with GSV during the pre-fieldwork virtual observation ignited thoughts of what impact the police could have on the mobility of the actors. This issue was investigated further during the fieldwork. Due to accusations that police have been involved in corruption in the past, photographing them would have been mistaken for an act of investigating or spying on them, endangering the person taking the photographs or leading to harassment. Fieldwork data revealed that the presence of the police created mixed feelings among commuters, depending on the time they were travelling. Their presence at night was appreciated by the farmers who indicated that it gave them a sense of security as they travelled to get machicha at EABL in Ruaraka. However, during the day, the presence of the police created fear and resentment among the farmers, transport operators (privately owned transport vans or matatus, and motorcycles or boda bodas) and milk vendors. The transport operators felt that the police harassed them and demanded bribes, thus they avoided police roadblocks or checks and where possible, used alternative routes. Farmers are vulnerable to police harassment and arrest, suspectedly because they own old vans and trucks which the police are likely to find faulty and label them as un-roadworthy. Milk vendors mostly operate illegally due to strict rules governing the dairy sector that prohibit the sale of raw milk, therefore making them susceptible to arrest by the police and the Kenya Dairy Board (KDB). On days when KDB is undertaking regulatory checks or “crackdowns,” the vendors do not supply milk or would rather use alternative routes to avoid arrest. One such route is Ng’enda Road, where GSV made it possible to identify two milk vendors on their way home. The milk vendors communicate with each other if they notice KDB vehicles patrolling the area.

Identifying the respondents on the move

29Milk distribution can either be formal or informal, with the former being undertaken by milk processors licensed by KDB, and informal milk distribution being carried out by milk vendors who mainly sell raw milk. Due to the prohibition of the sale of raw milk, it was difficult to get interviews from the vendors at the beginning of the research as they were concerned that I might have been a government agent who wanted to arrest them. While their presence was evident in Ruiru town as they delivered milk using motorcycles, they would not stop when I waved them down. It took the intervention of a restaurant operator to get an interview with two milk vendors who then introduced me to others in their trade and helped to build confidence among them. Adler and Adler (2003) refer to such respondents as reluctant respondents, whose unwillingness to be interviewed is driven by their illegal activities, fear of being reported, or their membership in secret societies. Northey et al. (2015, 83) point out that, “Gaining entry into a group is itself challenging. The prevailing view is that a researcher should let the group know that he or she is a researcher on a data-gathering mission.” Through exploratory observation using GSV, the routes that the milk vendors were using were notable along Ruiru-Ng’enda Road, implying that the source of milk sold in Ruiru town was from the Gatundu area, a finding corroborated by the fieldwork data.

30The images obtained from GSV showed that they could have been taken mid-morning when the vendors were returning home. This was discernible from the direction the vendor was travelling, as well as the arrangement of the jerrycans on the motorbike (see Figure 14). Usually, the milk vendors arrive in Ruiru town between 5 - 7 am to avoid police, reduce the risk of perishability, and ensure that the milk is delivered on time for use by consumers. Figure 14 was the best photograph I could find on GSV of a milk vendor on the move. The images of milk vending that were taken during fieldwork were of a motorcycle with jerrycans parked outside a restaurant belonging to a vendor who had just delivered milk, as well as images of milk jerrycans packed in gunny sacks waiting to be transported by the milk vendors by matatu. The milk vendors did not want to be captured on camera in the course of their routine activities due to the illegality of their business, making the photos plain without their presence. Although the GSV image may be blurred, it is still the best alternative available which illustrates this kind of activity.

Figure 14: Milk vendor transporting empty jerrycans after delivery

Figure 14: Milk vendor transporting empty jerrycans after delivery

Location: geo:-1.0713601,36.9206931. May 2018.
Original view: https://maps.app.goo.gl/​szncqmkeSoUUQQew9.

©Google Earth/GSV, 2018.

31Photographing the milk vendors and matatus while on the move was a difficult task. I would come across them while driving or walking in Ruiru town, and by the time I got ready to take a photograph, the vehicle would disappear. In other cases, I could take the photograph through the windshield while driving, which lowered the quality of the image. The challenge of photographing respondents on the move was a limiting factor in getting quality images of farmers transporting grass using bicycles or pickup trucks. The 360-degree cameras that Google uses can overcome the challenges of photographing from a moving vehicle and still produce quality images with minimum distortion (Anguelov et al. 2010).

32GSV images showed that milk vendors were also using matatus (see Figure 15), which led to the idea of tracing them at the Ruiru matatu terminus. This prompted a digital walkthrough around the Ruiru matatu terminus that revealed the presence of jerrycans packed in gunny sacks awaiting transportation. Transportation by matatu was common among female vendors who were not able to use motorcycles due in part to the heavy load involved, as well as the widely held consideration that riding motorcycles and bicycles in the area is a masculine activity. The latter information could not be obtained from GSV and further inquiry had to be undertaken through actual fieldwork.

Figure 15: A matatu transporting empty jerrycans of milk along Ruiru to Kagumo Road

Figure 15: A matatu transporting empty jerrycans of milk along Ruiru to Kagumo Road

Location: geo:-1.0921968,36.9483637. May 2018.
Original view: https://maps.app.goo.gl/​y5bmmz4Uf4QzPzFz9.

© Google Earth/GSV, 2018.

Benefits and limitations of GSV

33Images captured by the researcher during fieldwork are first-hand materials that are widely recognised as a contribution to the rigour of the research undertaken. However, virtual photography is gaining traction. GSV provides a platform for digital observation and for the selection of views that a researcher considers to be important to the research. In a way, this can be considered as an act of capturing an image. Ornatowska (2019, 63) refers to it as images that are “re-photographed” “off the computer screen.” The photos used in this research conveniently capture aspects that are related to the research, while others are of better quality than those taken during actual fieldwork. The World Wide Web offers a large repository of photos that researchers can make use of, including GSV or Google Maps. GSV was appropriate for the study because of its apt focus on mobility. Although the GSV images available for the study area only cover the main roads, they give a good picture of the nature of activities that were taking place. In addition, GSV covered most of the towns and emerging market centres that play a significant role in the functioning of the milk value chain, the subject of the study. GSV enabled a remote reconnaissance visit of the area of study providing an idea of what to expect during the fieldwork. It is cost-effective and less time-consuming compared to, for instance, taking a taxi through the area of study. This remote reconnaissance is useful in preparation for the actual fieldwork. Through activities observed on GSV, the researcher can undertake further interrogation of the underlying issues and queries.

34GSV offers photos of locations where a researcher would ordinarily feel like they are intruding on the respondents’ privacy. For instance, in the analysis of the streets in Githunguri town to identify the businesses related to milk production, it could have felt intrusive to get images of all those businesses without raising the alarm from the owners. It could also have created self-consciousness on the side of the researcher. Northey, Tepperman, and Albanese (2015) note that in field observation, one of the obstacles is to “get close enough to the situation to understand it, yet stay distant enough to see it objectively.” The researcher needs to be able to observe the activities as they normally take place and is thus able to get a true picture. The mere presence of the researcher in the field could change the behaviour of the actors, which can be referred to as the “Hawthorne effect”. Researchers carrying out ethnographic research involving participant observation are keen on reducing this effect by establishing a trusting relationship and rapport with the respondents (Miller 2015; Oswald et al. 2014). While the research utilised participant observation in the actual fieldwork, some of the GSV images display milk-related activities devoid of secondary researcher’s distractions. The use of GSV is like viewing the street from a window where the observer views the activities without their presence being felt. Ornatowska (2019, 63) indicates that the GSV images should “capture the reality with neither intention nor interest.” However, despite the researcher being away from the scene, the presence of the two vehicles used by Google, one vehicle mounted with a camera and the other being a security vehicle, is sometimes recognised by people, which is evident from the stares observed in some of the images (see Figure 1). Previously, Google used vehicles with no visible signs of identification when taking images; they are now required to use vehicles with logos, thus attracting attention from the people they photograph (Tanca 2018).

  • 7 “Geo Guidelines: Google Maps, Google Earth, and Street View.” Brand Resource Centre. About Google. (...)

35The other benefit of GSV is that Google gives a general consent for the use and personalisation of their images as long as they are attributed to Google and restricted to non-commercial use. “Due to limited resources and high demand, we’re unable to sign any letter or contract specifying that your project or use has our explicit permission. As long as you follow the guidance on this page, and attribute the content correctly, feel free to move forward with your project”7,the guideline reads. Thus, GSV images provide a wealth of photographic data that researchers can use without going through bureaucratic processes of seeking permission. Further, Google blurs the faces of people, signage, and number plates to hide the identity of people and vehicle owners. Building facades can also be blurred upon request. However, there have been ethical concerns regarding the use of Google Earth and GSV images, given that other people could still be able to identify the respondents, by their business premises, residences, or even based on clothing and the specifications of the means of transport they are using (Myers 2010; Nodari et al. 2012; Rundle et al. 2011). A researcher using GSV images has to be conscious of the ethical issues regarding their use. While the images may be blurred, it may still be possible to recognise the respondents based on the location where the images are taken.

36In general, GSV offers images taken over different periods, which is useful in a study investigating changes over time in a certain neighbourhood. While this would have been useful in observing visible changes in the villages, markets, and urban centres, the GSV images available at the time of research were only those taken in 2018 and 2019. In this study, I attempted to identify the frequency of occurrence of activities within a certain zone: Figure 7 shows a clear pattern of grass resale along Kigumo Road. However, capturing images at specific times can also be a limitation. This is because milk value chain activities take place throughout the day, and some aspects of production, collection, processing, and distribution were not captured through GSV. The patterns of mobility also differ depending on the day of the week. For instance, during market days, there is increased mobility as farmers travel to the urban centres to sell their produce. They are likely to use that trip to also purchase animal feeds and seek other services in the towns. “A photograph records a brief moment of time” (Goldstein, 2007, 70), and cannot be generalised to depict normal occurrences at all times. Given that Google does not indicate specific dates when images are taken—only the month and year are provided—it is not possible to understand issues that may be associated with the particular day or exact hour when the images were taken.

  • 8 See for example this page showing (in April 2024) the photographs of a part of the road uncovered (...)

37GSV images were only available for main roads, leaving out the adjoining secondary roads that also have a story to tell. As a result, with GSV, it was not possible to get information away from the main transect. The GSV images represent a roadside view of the transect, while the interior view of the farm life and social setting of the areas is hidden from the viewer, and has to be extracted through actual fieldwork. In addition, GSV did not cover the section of the transect between Githunguri and Uplands, excluding an 11-kilometre part of the transect at the time of research. As indicated above, it seems that more roads along the Ruiru to Uplands transect are now covered8. Lastly, interpretation of some of the images may not be possible without supplementary field data. Data from interviews with the actors gives a better understanding of the activities that are portrayed in the images. Thus, while a virtual reconnaissance gives an idea of the activities taking place in an area and ignites further inquiry, it is necessary to supplement this visual data with actual fieldwork data. The virtual reconnaissance cannot be a substitute for actual fieldwork (Kent et al. 1997; McMorrow 2005; R. Taylor 2005). As Tanca (2018, 53) points out, virtual reconnaissance is more of a “starting point than an end point.” During actual fieldwork, a researcher can utilise all their five senses, which is not possible when using GSV. Furthermore, it is impossible to replicate human vision with photographs (Goldstein 2007).

Conclusion

38While GSV images were a useful source of data for this study, especially at the initial reconnaissance stage, it was necessary to support 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 either during the actual fieldwork or from secondary sources of data. The study also notes that there are limitations to interpreting some of the images without interviewing the actors or without having prior knowledge of the activities that are taking place.

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Appendix

GSV 360° images used in this article

Tea hawking in Githunguri

geo:-1.0639472,36.764294. May 2018.
https://maps.app.goo.gl/ZJ6sbk3KXemzFr187.

Transportation of grass along Ruiru-Uplands Road

geo:-1.1136152,36.8980425. May 2018.
https://maps.app.goo.gl/gZCnFxUA9aTuZQnw6.

A farmer buying grass from casuals who cut it from farms previously used to grow coffee along Kigumo Road

geo:-1.090055,36.9428445. May 2018.
https://maps.app.goo.gl/2mFmicYXwDuWcYNU6.

Non-motorised modes of transport used in milk collection

geo:-1.0675221,36.7641333. May 2018.
https://maps.app.goo.gl/e5zCnR8SoD5hjuhU9.

Mung’u Junction Milk Collection Centre

geo:-1.0739163,36.7531906. May 2018.
https://maps.app.goo.gl/e5zCnR8SoD5hjuhU9.

Cutting and transporting grass along the transect (a)

geo:-1.1054042,36.9603564. May 2018.
https://maps.app.goo.gl/nxTURk5cna6xJtix5.

Cutting and transporting grass along the transect (b)

geo:-1.1054042,36.9603564. May 2018.
https://maps.app.goo.gl/FrdKRbkLK7AQp2KWA.

Cutting and transporting grass along the transect (c)

geo:-1.097047,36.9530279. May 2018.
https://maps.app.goo.gl/VM2xFtW39XKP4jgs8.

Cutting and transporting grass along the transect (d)

geo: -1.0846133,36.9346798. May 2018.
https://maps.app.goo.gl/KAfDumhCC11j1Y4S8.

Sale of recycled chicken waste along Ruiru-Uplands Road

geo:-1.0604012,36.7830435. May 2018.
https://maps.app.goo.gl/auBXoNeKVCMTCA9a6.

Sale of animal feeds in Githunguri town

Location: -1.0571251,36.7766525. May 2018.
https://maps.app.goo.gl/29M7ko1eebxc9yip6.

Sale of hay at Ngewa Market Centre along the Ruiru-Uplands transect

Location:-1.0931704,36.8654678. May 2018.
https://maps.app.goo.gl/Q1DMrBzDiETdEt6X6.

Pickup trucks awaiting transport business in Githunguri town

geo:-1.0577454,36.7757861. May 2018.
https://maps.app.goo.gl/6dW38vqEezfQJwtFA.

A cluster of carpentry shops along Ruiru-Kamiti Road in Ruiru town

geo:-1.1496265,36.9530532. May 2018.
https://maps.app.goo.gl/LwzwLWuKSh5tw9xVA.

A lorry at the machicha dispensing station at EABL in Ruaraka

geo:-1.2353859,36.8820851. February 2018.
https://maps.app.goo.gl/wwsf6AFi5VKGR9U77.

Milk vendor transporting empty jerrycans after delivery

geo:-1.0713601,36.9206931. May 2018.
https://maps.app.goo.gl/szncqmkeSoUUQQew9

A matatu transporting empty jerrycans of milk along Ruiru to Kagumo Road

geo:-1.0921968,36.9483637. May 2018.
Original view: https://maps.app.goo.gl/y5bmmz4Uf4QzPzFz9

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Notes

1 Discussing the use of aerial or satellite images goes beyond the scope of this paper.

2 Google explains its procedures as follows: “When multiple nearby 360 photos are connected through navigation. These photos may be collected by Google or by Street View contributors.” (“Understand Street View photo symbols.” Google Maps Help. https://support.google.com/maps/answer/10443241?hl=en [archive]).

3 Since the completion of this research, the section from Githunguri to Uplands has now been covered by GSV, showing the very fast expansion of GSV worldwidel—and in Kenya.

4 Equipe EXARMAS. 2019.“ EXARMAS#11 : Illustrer son terrain au Kenya.” EXARMAS. EXposition des Arts à la MAison des Suds. https://exarmas.org/exarmas11/ [archive].

5 The images may have been taken about 10 to 11 am given the activities that were taking place and the shadows.

6 For copyright purposes it is very unlikely that Google will allow the download of full quality images from GSV. However, for those who might be interested in sourcing an image from GSV, instructions can be found here: https://www.quora.com/How-do-you-download-an-image-from-Google-Maps

7 “Geo Guidelines: Google Maps, Google Earth, and Street View.” Brand Resource Centre. About Google. https://about.google/intl/en-GB_ALL/brand-resource-center/products-and-services/geo-guidelines/, consulted 19 April 2024 [archive].

8 See for example this page showing (in April 2024) the photographs of a part of the road uncovered in 2018, pictured in December 2021, July 2022, March 2023, and December 2023: https://www.google.com/maps/@-1.0633933,36.7202659,3a,75y,90t/data=!3m7!1e1!3m5!1saeLUr75CfaVYUrTYMPCdLQ!2e0!5s20211201T000000!7i16384!8i8192?entry=ttu.

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List of illustrations

Title View 1. Above: The Ruiru-Uplands transect (47 km); below: The Ruiru-Githunguri section (25.4 km) (OpenStreetMap)
Credits Source: OpenStreetMap, 2024. Links: above: below.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-1.png
File image/png, 1.6M
Title Map 1: Study area
Credits Source: Author.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-2.png
File image/png, 392k
Title Figure 1: Tea hawking in Githunguri
Caption Location: geo:-1.0639472,36.764294. May 2018.Original view: https://maps.app.goo.gl/​ZJ6sbk3KXemzFr187.
Credits © Google Earth/GSV, 2018.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-3.png
File image/png, 1.7M
Title Figure 2: Transportation of grass along Ruiru-Uplands Road
Caption Location: geo:-1.1136152,36.8980425. May 2018.Original view: https://maps.app.goo.gl/​gZCnFxUA9aTuZQnw6.
Credits © Google Earth/GSV, 2018.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-4.png
File image/png, 1.3M
Title Figure 3: A farmer buying grass from casuals who cut it from farms previously used to grow coffee along Kigumo Road
Caption Location: geo:-1.090055,36.9428445. May 2018.Original view: https://maps.app.goo.gl/​2mFmicYXwDuWcYNU6.
Credits © Google Earth/GSV, 2018.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-5.png
File image/png, 1.4M
Title Figure 4: Non-motorised modes of transport used in milk collection
Caption Location: geo:-1.0675221,36.7641333. May 2018.Original view: https://maps.app.goo.gl/​e5zCnR8SoD5hjuhU9.
Credits © Google Earth/GSV, 2018.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-6.png
File image/png, 2.3M
Title Figure 5: Mung’u Junction Milk Collection Centre
Caption Location: geo:-1.0739163,36.7531906. May 2018.Original view: https://maps.app.goo.gl/​e5zCnR8SoD5hjuhU9.
Credits © Google Earth/GSV, 2018.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-7.png
File image/png, 1.8M
Title Figure 6: Mung’u Junction Milk Collection Centre
Credits © Jackson Kago, 2018.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-8.jpg
File image/jpeg, 3.2M
Title Figure 7: Cutting and transporting grass along the transect
Caption May 2018. Location: 7a: geo:-1.1054042,36.9603564. https://maps.app.goo.gl/​nxTURk5cna6xJtix5.7b: geo:-1.1033338,36.958434. https://maps.app.goo.gl/​FrdKRbkLK7AQp2KWA.7c: geo:-1.097047,36.9530279. https://maps.app.goo.gl/​VM2xFtW39XKP4jgs8. 7d: geo: -1.0846133,36.9346798. https://maps.app.goo.gl/​KAfDumhCC11j1Y4S8. 7e: [unretrieved].
URL http://journals.openedition.org/sources/docannexe/image/1301/img-9.jpg
File image/jpeg, 1.3M
Title Figure 8: Sale of recycled chicken waste along Ruiru-Uplands Road
Caption Location: geo:-1.0604012,36.7830435. May 2018.Original view: https://maps.app.goo.gl/​auBXoNeKVCMTCA9a6.
Credits © Google Earth/GSV, 2018.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-10.png
File image/png, 1.2M
Title Map 2: Dairy farming-related business activities in Githunguri town centre
Credits Source: Jackson Kago, adapted from Google Maps, 2018.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-11.png
File image/png, 387k
Title Figure 9: Sale of animal feeds in Githunguri town
Caption Location: -1.0571251,36.7766525. May 2018.View: https://maps.app.goo.gl/​29M7ko1eebxc9yip6.
Credits © Google Earth/GSV, 2018.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-12.png
File image/png, 1.7M
Title Figure 10: Sale of hay at Ngewa Market Centre along the Ruiru-Uplands transect
Caption Location:-1.0931704,36.8654678. May 2018.Original view: https://maps.app.goo.gl/​Q1DMrBzDiETdEt6X6.
Credits © Google Earth/GSV, 2018
URL http://journals.openedition.org/sources/docannexe/image/1301/img-13.png
File image/png, 1.6M
Title Figure 11: Pickup trucks awaiting transport business in Githunguri town
Caption Location: geo:-1.0577454,36.7757861. May 2018.Original view: https://maps.app.goo.gl/​6dW38vqEezfQJwtFA.
Credits © Google Earth/GSV, 2018.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-14.png
File image/png, 1.2M
Title Figure 12: A cluster of carpentry shops along Ruiru-Kamiti Road in Ruiru town
Caption Location: geo:-1.1496265,36.9530532. May 2018.View (different time): https://maps.app.goo.gl/​LwzwLWuKSh5tw9xVA.
Credits © Google Earth/GSV, 2018.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-15.png
File image/png, 1.1M
Title Figure 13: A lorry at the machicha dispensing station at EABL in Ruaraka
Caption Location: geo:-1.2353859,36.8820851. February 2018.Original view: https://maps.app.goo.gl/​wwsf6AFi5VKGR9U77.
Credits © Google Earth/GSV, 2018.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-16.png
File image/png, 1.1M
Title Figure 14: Milk vendor transporting empty jerrycans after delivery
Caption Location: geo:-1.0713601,36.9206931. May 2018.Original view: https://maps.app.goo.gl/​szncqmkeSoUUQQew9.
Credits ©Google Earth/GSV, 2018.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-17.png
File image/png, 914k
Title Figure 15: A matatu transporting empty jerrycans of milk along Ruiru to Kagumo Road
Caption Location: geo:-1.0921968,36.9483637. May 2018.Original view: https://maps.app.goo.gl/​y5bmmz4Uf4QzPzFz9.
Credits © Google Earth/GSV, 2018.
URL http://journals.openedition.org/sources/docannexe/image/1301/img-18.png
File image/png, 1.5M
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References

Electronic reference

Jackson Kago, “Application of Google Street View Images in Identifying Mobility Patterns in Small and Intermediate Towns (Kiambu County, Kenya)”Sources: Materials & Fieldwork in African Studies [Online], 7 | 2024, Online since 13 June 2024, connection on 06 November 2025. URL: http://journals.openedition.org/sources/1301; DOI: https://doi.org/10.4000/11tg3

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About the author

Jackson Kago

Department of Spatial and Environmental Planning, Kenyatta University. https://orcid.org/0000-0002-0295-3580

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Copyright

CC-BY-SA-4.0

The text only may be used under licence CC BY-SA 4.0. All other elements (illustrations, imported files) may be subject to specific use terms.

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