Positional accuracy results from VGI for rural areas

The geospatial data collected by the volunteers from the first rural community of Dloaa was analysed to compare the positional accuracy of the two different methods in relation to the official data collected by professional surveyors in the Al-Hillah land administration office. A customised dashboard tool developed in Matlab for the calculation and visualisation of the RMSE and other measures was used to quantify the discrepancies between VGI and official data (Figure 6.23). However, the geospatial data collected from the Aries community was not compared with the official surveying data because the latter was out of date and did not represent the new situation in the area. For this reason, the geospatial data was verified by crowd-sourced agreement on the boundaries.

Figure 6.23 Calculation and visualisation tool for RMSE with VGI points captured by GPS and compared to parcel coordinates from official mapping in the Mahkama peri-urban area of

The results of the positional accuracy testing for the Dloaa rural communities are shown in Table 6.37 below.

Table 6.37 Root mean square error (RMSE) for rural community land corners for the two datasets Rural community No. of points tested RMSE smartphone and official RMSE iPad and official RMSE paper photo and official Dloaa 180 3.23m - 3.41m

The result for positional accuracy obtained via RMSE values for the Dloaa community reveal that the use of the smartphone for collecting coordinates was slightly better than using pen and paper images. However, there are no iPad results because the local community volunteers were not interested in using this technology. The following Figure 6.24 and 6.25 show the three collected VGI layers for each selected rural area of Aries and Dloaa respectively.

6.9.4 Completeness

Local community volunteers in the rural areas were trained and asked to go out in pairs to count and demarcate all the plots within a given sub-section of the AOI in the community. The total number of plots identified has been compared with official data for each case study site, and significant differences between the official and VGI quantities have been found, with 80 rural plots on the official map and 728 plots observed by the volunteers.

In the rural areas, some parcels have not changed in occupancy or use for many years, but other locations have changed in land use from agricultural to residential, and numerous housing plots have been created in single fields (see Figure 6.26 below). It is important to identify any changes in rural areas because, where change takes place, it is happening in potentially productive land and changing the very nature of the place from agricultural to residential. This has significant implications for food production and for the municipal services an area needs such as schools, clinics and other infrastructure.

These findings highlight the fact that rural areas are potentially highly dynamic. Thus, incomplete formal data is very problematic. It may be due to the preconceptions of professionals that rural areas are static, or it may simply be a lack of manpower and resources to regularly update data. There may also be a problem related to practices in the rural communities of altering plots without reference to the authorities. However, more data is needed and more emphasis should be placed on engaging with rural communities to encourage them to seek permission to build and then to register changes. Engaging rural communities in VGI might help to bridge the gap between communities and the authorities, educating people in the importance of accurate land registration and, at the same time, providing more complete data.

Figure 6.26 Photographs demonstrating the change from agricultural land to residential areas.

6.9.5 Currency of data

The local volunteers in the rural communities were asked about their opinions of the official cadastral maps of their communities and the difference that they could find between the maps and current developments. There was common agreement about some changes which have been collated and are presented in the following points.

In Aries, the local community volunteers agreed that the current official map dates back to 1950, which was long before the agriculture reform project in the 1980s. This project involved the creation of a full new irrigation system. Accordingly, some clear boundaries that separated plots of agricultural lands were removed. In this case, some of the owners still used the old boundaries while others changed them with the agreement of land owners. For this reason, the geospatial data collected for this area did not match the official records because it is totally different. However, we depended on the crowd-sourced agreement of local community volunteer agreement on the current boundaries and the attribute data.

For the second rural community of Dloaa, the local community volunteers agreed that there had been some changes in the community which did not match the cadastral map, but the general boundaries were deemed to be valid and the original boundaries were still visible on the ground and could be used for the purpose of comparison. These changes indicate that there was an old river shown on the map diminishing to a dry channel over the past 20 years and the boundaries of the land near the river had changed; A new smaller river was created but this did not appear on the map; Some plots had been divided into smaller ones and changed to be residential but

they still appeared as one piece of land on the map and other land had changed from being fertile land with lots of palm trees to barren land, and its status needed to be updated.

6.10 Chapter summary

The results were consistent across each site within each category, verifying that using the iPad for spatial data collection in both urban and peri-urban areas is the best choice in regard to positional accuracy. However, the iPad was not applied in the rural areas, where local people were reluctant to use it. Here, the results for alternative methods of smartphone and analogue mapping were similar to each other.

Several other strands of research have been followed in this project. The gender imbalance in volunteering has been noticeable, particularly in the rural areas where no females volunteered, although as a whole it is not dissimilar to that reported for the OSM project (Glasze and Perkins, 2015). The educational level of the volunteers was directly related to their place of residence, and urban dwellers had a higher level of education and were also happier to use the hi-tech iPad method of data collection.

The potential for VGI within this system is, therefore, worthy of investigation. The analysis has shown the relative accuracy of different data collection methods in different contexts. It can be argued that, in some cases, it may be more important to collect interim data which the community can agree on and take ownership of, even if that means using a slightly less accurate method rather than to focus simply on spatial accuracy. It can be concluded that, in areas of conflict or when official systems are under extreme stress, VGI may be the only realistic method of collecting data. In these cases, it may be more important to encourage people to use a method appropriate to their preference and ability, and to sacrifice some degree of spatial accuracy. This chapter has, therefore, concentrated on the consideration of different methods of data collection that can suit different types of people in varying geographical contexts. The research provided an opportunity for an encouragingly high number of volunteers to participate, with varying levels of education and experience. The promising levels of accuracy and completeness of the VGI data and its possible inclusion in a fit-for-purpose LAS are of significant interest to the authorities of Al-Hillah.

Chapter 7. Lessons from the process of VGI in stressed and complex