There is a persistent gap in the availability of accurate estimates of the scale of urban displacement. Even when such information is locally available, it is generally insufficient to inform prevention, response and durable solutions. Other types of data on characteristics (including gender, age and disability status), living conditions (including income and access to services) and capacities are also required if we are to build a more solid evidence base from which to make informed decisions to address urban displacement.
There are many challenges involved in obtaining data on urban displacement. The first one is the lack of general consensus around what constitutes an urban area. Thresholds and criteria vary across countries, which causes a major methodological barrier to presenting a global picture of the urban or rural nature of displacement. This challenge may take a long time to be resolved but adapting a country’s own criteria would at least help to standardise data collection at the national level.
A second challenge is obtaining geo-located data on internally displaced people (IDPs) in urban settings. Cities are complex environments, and displacement to, from and within them is highly dynamic, making it difficult to know the precise location of displaced populations at any point in time. In cities, IDPs tend be dispersed and many seek anonymity because of potential threats to their security. This is a particular challenge for humanitarian first responders trying to provide adequate protection and assistance.
There are, however, good examples of geo-located data on displacement. In Iraq, the IOM Displacement Tracking Matrix has coordinates on IDP sites for the entire country. An analysis undertaken by IDMC using this data showed that in 2018 around 70% of people who had been internally displaced by conflict and violence in Iraq were living in urban areas, while 96% of the IDP sites recorded in rural areas of the country were actually located within 10 kilometres of an urban area. This data (anonymised to protect individual identities) offers a useful picture of the relationship of internal displacement to cities and urbanisation processes. Even though obtaining comprehensive location-related data like this is rare, the example shows that it is indeed possible to assess the scale of displacement in cities.
A third challenge is the lack of longitudinal data. Investments in data collection all too often decrease after the emergency phase of a crisis. This is a major hindrance to assessing IDPs’ living conditions over time, and limits a full understanding of the causes and characteristics of protracted displacement in cities. Such information would be valuable for development actors, as they could adapt their urban development and planning interventions to take into consideration urban IDPs and host communities.
Tackling data challenges
The use of alternative data sources and technologies, including mobile phone data, satellite imagery analysis and community mapping, could help to overcome these challenges. For example, in Papua New Guinea, the UN collaborated with private phone companies to use anonymised mobile phone data to measure the patterns and duration of displacement following the February 2018 earthquake. This enabled them to obtain detailed information about when people moved, from where and to where, and for how long they remained displaced. Complementing these assessments with qualitative information from affected communities would help shed light on the reasons behind people’s movements and their decisions to return.
The ability to measure the scale, patterns, location and duration of urban displacement, while useful, is not sufficient, however, to fully capture the phenomenon. The gap in understanding that then ensues can limit the capacity to develop solutions adapted to each context. Profiling exercises can help shed light on both the impacts that displacement can have on urban IDPs and their hosts, and the capacity of local authorities and other stakeholders to address displacement-affected communities’ needs and to support self-reliance.
A key part of building the evidence base on urban displacement lies in understanding how cities have the capacity to absorb large populations, and how markets, housing and service provision are affected – both positively and negatively – by displacement. Sustaining such data collection and analysis efforts over time can be challenging. For this reason, capacity development of local authorities and the participation of urban IDPs and host communities in data collection around their vulnerabilities and needs are essential.
An exercise conducted in Mogadishu in 2014 and 2015 (in collaboration with the local authorities) mapped and enumerated informal settlements, which helped to identify IDPs and differentiate them from their hosts. The analysis highlighted the specific challenges IDPs faced in different areas of the city where little or no information on their conditions previously existed. The exercise only covered displaced populations in specific settlements and not in the entire city, but its results have been useful to the local authorities in Mogadishu in supporting durable solutions. Such data collection exercises are usually rare, however, despite being key to assessing and understanding scale, characteristics and conditions.
In order to achieve better outcomes and reduce the likelihood of protracted urban displacement, it is important to be aware of existing resources, skills and community services. In this respect, involving urban IDPs and host communities in broader urban planning and development processes will help identify those priorities – in service delivery, infrastructure and housing development – that will contribute most to achieving durable solutions to urban displacement.
Vicente Anzellini firstname.lastname@example.org
Coordinator, Global Report on Internal Displacement
Clémence Leduc email@example.com
Research and Monitoring Fellow
Internal Displacement Monitoring Centre (IDMC) www.internal-displacement.org
 Using the framework of the EU Global Human Settlement Layer: https://ec.europa.eu/jrc/en/global-human-settlement-layer
 Prahara P et al (2019) Comparing population displacement estimates from mobile network data and other sources. Working paper: evidence from the Highlands earthquake in Papua New Guinea http://www.internal-displacement.org/global-report/grid2019/downloads/background_papers/Hodge_FinalPaper.pdf
 Joint IDP Profiling Service (2019) Displacement profiling in urban areas: Methodological approaches for collecting and analysing data on internal displacement in cities https://www.jips.org/uploads/2019/05/JIPS-IDMC-GRID2019-UrbanProfiling.pdf
 See pp90–1 of IDMC (2019) Global Report on Internal Displacement 2019 www.internal-displacement.org/global-report/grid2019/