Gresa is an executive director of Space SyntaKs, a research institute from Pristina which focuses on social phenomena in public spaces. The organisation formed from a group of activists who wanted to improve the availability of spatial data and practical training for GIS skills in Kosovo.
SafoMeter is one of the first projects Space SyntaKs decided to work on. The project is focused on evaluating the safety of public spaces, especially for marginalised groups like women and ethnic or religious minorities.
Gresa: We started looking at what has been done in Kosovo with the issue. In the last four or five years we had a lot of cases of physical and sexual violence against women in the city. We found that there were specific characteristics of places where incidents were happening. We also wanted to see what parts of normal life were impacted by safety in public spaces.
Gresa and the project team identified eight characteristics that appeared to impact the safety of public spaces:
Once they had identified the characteristics they wanted to focus on for their research, they needed to collect data. They found that aside from data on the presence of public institutions, there was no data available for the other criteria and they would need to collect this information themselves.
Space SyntaKs brought in a team of students from the faculties of Geography and Architecture at the University of Pristina to help with the data collection. They decided to use Mergin Maps, which was still called Input at the time, for the data collection on the recommendation of Besfort Guri. They had a two-week training programme for the students to teach them how to collect the physical and qualitative survey data, so they needed an app that was easy to use but was also able to handle complex spatial data. The students also had prior experience with QGIS, so being able to link the project from the app to QGIS was also a major benefit.
In Mergin Maps, point data was used to mark the location of street lights, security cameras and public institutions. They also used forms to link attributes about the functionality and ownership of the lights and cameras. Forms were also used to collect the qualitative survey data and link it to the public spaces. The flexibility of having all of the data in one place and synchronised automatically meant that they could move on to the data analysis stage more easily.
Since there was a large amount of data that needed to be collected, they originally set out to collect a sample of data for a few neighbourhoods in Pristina. While it initially took a little time for the students to get used to field data collection, they had a lot of fun working with the Mergin Maps app and were able to work quite efficiently. Due to the time saved from using the app, they managed to collect enough data for the entire urban area of the city within the three month period they had set aside for data collection.
Once the data was collected, they were able to create an index that scored the public spaces based on the physical and social perceptions of safety in each space. The scores ranged from 0-10, with 10 indicating the highest perception of safety and lower scores indicating spaces that were less safe.
The initial results were not very promising. Among all of the spaces scored in the study, the highest score for a single space was 5.57 which meant that the safest space in the city was at best a ‘medium’ level of safety for marginalised people based on their study criteria. One of the biggest problems highlighted by the survey was that lighting in public spaces across the city was poor. Several factors contributed to this, including issues that the municipal government was having with external contractors to repair lights that were not working. There had also been cases of sexual assault and bad weather that occurred just before the study that were impacting local perceptions of safety during the time of the study.
In spite of some of the events that may have impacted the study results, the overall indicators for safety in public spaces were not very promising. They found that there were not enough connected areas with consistently high perceptions of safety, instead there were spaces deemed to be unsafe scattered throughout the entire city. Gresa said, “If I’m going out at night and I want to go back home I would choose this route because it looks safer. It’s not possible because you have spots of safe zones and then you have hotspots of unsafe zones spread throughout the city. You can’t even make a generalisation and say ‘This neighbourhood is safe’ because within that neighbourhood you have a lot of unsafe areas.”
Gresa says that they would like to develop the website so people can explore the data in more detail using filters. For example, if someone wanted to compare the perceived safety of a place during the day, they could disable the street light indicators to get an adjusted score. They also hope to add functionality so users can fill out some of the survey data themselves. Space SyntaKs would also like to conduct the surveys more frequently so the data can remain up-to-date and they can evaluate changing perceptions over time. Gresa also says that they are planning to develop some focus groups with women throughout the city to see more in-depth how their perceptions align with the results of the initial survey data. They hope to get support from the municipality and other stakeholders to get the funding to continue the research.
If you would like to read more about the SafoMeter project, you can find the paper from the initial study here.