Mapping refugee populations at high-resolution by unlocking humanitarian administrative data

doi:10.21203/rs.3.rs-3772487/v1

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Mapping refugee populations at high-resolution by unlocking humanitarian administrative data

https://doi.org/10.21203/rs.3.rs-3772487/v1

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Background

Informing local decision-making, improving service delivery, and designing household surveys requires having access to high spatial resolution mapping of the targeted population. However, this detailed spatial information remains unavailable for specific population subgroups, such as refugees, a vulnerable group that would significantly benefit from focused interventions. Given the continuous increase in the number of refugees, reaching an all-time high of 35.3 million people in 2022, it is imperative to develop models that can accurately inform about their spatial locations, enabling better and more tailored assistance.

Methods

We leverage routinely collected registration data on refugees and combine it with high-resolution population maps, satellite imagery derived settlement maps and other spatial covariates to disaggregate observed refugee totals into 100m grid cells. We suggest a deterministic grid cell allocation inside monitored refugee sites based on building count and a random-forest derived grid cell allocation outside refugee sites based on geolocating the textual geographic information in the refugee register and on high-resolution population mapping. We test the method in Cameroon using the registration database monitored by the United Nations High Commissioner for Refugees.

Results

Using OpenStreetMap, 83% of the manually inputted information in the registration database could be geolocated. The building footprint layer derived from satellite imagery by Ecopia AI offers extensive coverage within monitored refugee sites, although manual digitization was still required in rapidly evolving settings. The high-resolution mapping of refugees on a 100m grid basis provides an unparalleled level of spatial detail, enabling valuable geospatial insights for informed local decision-making.

Conclusions

Gathering information on forcibly displaced persons in sparse data-setting environment can quickly become very costly. Therefore, it is critical to gain the most knowledge from operational data that is frequently collected, such as registration databases. Integrating it with ancillary information derived from satellite imagery paves the way for obtaining more timely and spatially precise information to better deliver services and enhance sampling frame for target data collection exercises that further improves the quality of information on people in need.

Gridded population

refugee

administrative data

building footprint

satellite imagery

High spatial resolution population mapping is of paramount importance in providing support for localized decision-making within various domains of public interest. Examples of such domains include urban planning, environmental hazard risk management (1), and public health (2), where optimal resource allocation among different geographical areas is a critical concern. This significance is further underscored in the context of populations impacted by crises. In 2022, the United Nations High Commissioner for Refugees (UNHCR) documented 29.4 million refugees under UNHCR mandate and an additional 5.9 million refugees under United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) mandate. The world has never had more refugees than today with the largest yearly increase since the start of global refugee statistics (+ 36%), and yet we know little about the precise location of their resettlement (3). Nonetheless, detailed spatial information not only enhances the efficiency of distributing humanitarian aid but also facilitates strategic decision-making regarding the placement of crucial facilities and helps guide and target collection of new and improved data.

Increasing the spatial resolution of population figures can be achieved through the development of fully georeferenced census but the quality, frequency and population targeted of the census in some part of the world requires alternative strategy to provide detailed population estimates. As a result, significant modeling efforts have been made using remotely sensed data and other geographic data to disaggregate coarse census totals into small grid cells. (4–6). This dasymetric mapping approach has mainly focused on enhancing the spatial distribution of population count, sometimes including age and sex subgroups (7, 8). The purpose of this research is to examine if the method is applicable for mapping subpopulations in a crisis context, such as refugees in the context of large-scale international forced migration.

Remote-sensing based methods have already been previously leveraged to inform about population in a crisis context to provide rapid estimation of forcedly displaced population sizes based on the manual enumeration of makeshift structures from satellite imagery (9). Building upon the success of this approach, subsequent research has focused on refining feature detection algorithms tailored to the mapping of refugee settlements (10, 11). However, to our knowledge, it remains yet to harness those refined spatial data for mapping a country-wide refugee population at a fine spatial scale. Such a resource not only facilitates policy micro-planning (2) but also inform the design of survey sampling focused on vulnerable population (12).

A second previously untapped data source for refugee mapping explored in this research is humanitarian administrative data. Refugees have been for long a blind spot of comprehensive active data collection exercise such as national census even in major refugee hosting countries (13). In parallel though large-scale passive data collection such as the digital registration of all individuals in need has now become a cornerstone of humanitarian data management systems. An example of such an initiative is the proGres database developed by the United Nations High Commissioner for Refugees (UNHCR), which records all refugees in a country with the aim of providing access to rights, effective protection, and assistance to those in need (14). While the primary purpose of this administrative registry is operational, it is also serves as an indirect valuable source of demographic information on the refugee population and its dynamic, for example to compile mortality statistics (15). However, this administrative data contains also precise subregional geographic information that are yet to be exploited for evidence-based decision making.

Country-wide detailed refugee mapping exercise is particularly important for refugees who reside outside of monitored camps, as locating, and effectively assisting them present greater challenges. We therefore propose a model that maps the registered refugee population by converting the information in the register database to geographical locations and combining it with geospatial layers derived from high resolution remote-sensing data. The area of focus is Cameroon where 471 386 refugees are registered as of April 2023 and the output is a gridded map at 100m resolution. The map of refugee population should not be considered as a precise estimate of the number of refugees contained in each grid cell but rather as an indication of the relative spatial distribution of refugees in Cameroon. The novelty of the proposed high-resolution mapping is twofold. First the extent of the register database combined with remote-sensing derived covariates enables a countrywide mapping at a spatial scale never seen before. Second by informing about the relative spatial concentration of refugees compared to the resident population, it paves a way for the inclusion of the unregistered refugee population in surveys and policy designs, provided their spatial allocation follows that of the registered refugee population.

Alongside this research, we also provide an assessment in a context of limited data of a freely available gazetteer to geolocate textual geographic data and of seven settlement maps to gain a better understanding of the quality of automated mapping of settlement layout in refugee sites from satellite imagery.

We demonstrate how to leverage diverse data sources, satellite imagery, and modeling techniques to provide nuanced insights into refugee spatial dispersion. We first explain why we choose the Cameron setting, then enumerate the different data sources explored and their global availability and finally present our modelling framework and potential for replicability.

Location: the refugee context in Cameroon

Cameroon is confronting a multi-faceted humanitarian and protection crisis caused by conflict, inter-communal violence, and the impacts of climate change. The location of refugees follows roughly this schema: refugees from Nigeria who fled Boko Haram violence are situated in the Far North region and refugees from Central African Republic (CAR) in the East region (see Fig. 1). Some refugees from CAR have been displaced for over a decade, and the political and security situation in CAR has not improved sufficiently to warrant their return (16). UNHCR monitors seven refugee sites along the border with CAR (see Fig. 1). Nigerians began seeking refuge in Cameroon in 2012. In response, Cameroon established a camp in Minawao to accommodate up to 20,000 refugees, but the camp’s capacity was nearly exceeded by 2014 (16). This is the only refugee site titled camp in Cameroon. By the end of 2015, violence along the Cameroon-Nigeria border displaced more than 90,000 Cameroonians and refugees who had settled in these areas and this pressure at the border never really stopped (16). At the end of March 2023, Cameroon had over 480,000 refugees and asylum seekers, including roughly 349,000 Central African Republic refugees and 128,000 Nigerians and more than one million internally displaced people (17).

Data sources

Data are involved at three stages of the high-resolution refugee mapping pipeline: first to define the target variable that is the refugee population through administrative records, second to geolocate the refugee population through a settlement map and a gridded population map and third to model its spatial variation through multiple spatial covariates.

Refugee population

The data has been retrieved in April 2023 from the proGres version 4 registration database, UNHCR's corporate registration, identity and case management tool that holds the individual data of forcibly displaced and stateless persons. First rolled out in 2018, it is built on Microsoft customer relationship management and is the backbone of multiple interoperable tools and application ranging from the rapid off-line inputing of refugees information when they seek assistance to the biometric checks during food distributions. The information recorded covers not only basic demographic characteristics but also identification of specific needs and recording of events such as resettlement acceptation or voluntary departure for all countries where UNHCR operates. Considered as the most up-to-date list on refugees in a country, the proGres database is regularly used as a sampling frame to conduct surveys and to provide statistics on the current size of the refugee population. The aggregated statistics from the database are openly available on UNHCR website (3) and through a R package (18) but accessing the individual records requires special authorisation. In countries where government also undertake refugee registration, UNHCR database can contain only a fraction of the refugees, but this is not the case in Cameroon where it can be considered as a refugee census. Out of the 471 386 refugees recorded in Cameroon, 337 223 refugees were reported as living outside of UNHCR-monitored refugee sites. The registration database provided us with refugee aggregates at admin 3 level and partial information on refugee precise locations based on textual input.

Gazetteer

To link the manually inputted textual information in proGres registration database with spatial coordinates, we retrieved 37 531 point locations with names from OpenStreetMap in June 2023 (19) that is point feature with key “place” and value “all” using the QuickOSM plug-in from QGIS (20).

Covariates We gathered 20 covariates to inform the spatial allocation of refugees inside Cameroon:

Covariates linked to the available infrastructure are derived from OpenStreetMap: distance to health, education, local roads, major roads, marketplaces, places of worship, road intersections (19) and from NASA Visible Infrared Imaging Radiometer Suite: mean intensity of nightlights in 2022 (21)
Covariates linked to insecurity are derived from the Armed Conflict Location and Event Database (22): distance to conflict locations in 2019, 2020, 2021 and 2022; and from the UNHCR sites location (19): distance to monitored refugee sites
A covariate linked to the level of population density is derived from the WorldPop bottom-up gridded population dataset: a sum of population counts in a 1km window
Covariates linked to the morphology of settlement are derived from the Ecopia building footprint: the mean and coefficient of variation of the area and perimeter of buildings contained in each grid cell, the date of the satellite imagery used, the classification in urban and rural of the buildings (23)

Settlement map To map refugee population inside monitored sites, we assessed the accuracy of five different settlement maps that were extracted from satellite imagery by various institutions. They differ in their format (building polygons or gridded settled area), their spatial resolution and their temporal resolution as summarised in Table 1.

Base high-resolution population

We used as base layer for the refugee population located outside of monitored refugee sites, a 100m gridded population layer, product of a collaboration between the Cameroon National Statistics Office and the WorldPop Research group produced from household survey dating from 2021 and 2022 and building footprints dating from 2021. The method used was a Bayesian Hierarchical Geostatistical Model.

Table 1

Attributes of the settlement maps assessed for refugee mapping inside sites
Name	Source	Date selected	Type	Spatial Resolution	Coverage	Temporal Resolution	Resource
World Settlement Footprint	German Space Agency	2019	Raster	10m	Global	4 years	Link
Global Human Settlement Layer	Joint Research Centre - European Commission	2020	Raster	100m	Global	~ 5 years	Link
Microsoft Building Footprints	Microsoft	2022	Polygon		Global	Ad hoc	Link
Google Building Footprints (v1)	Google	2021	Polygon		Africa, South Asia and Latin America	Ad hoc	Link
Ecopia Building Footprints	Ecopia AI Maxar Technologies	2021	Polygon		Africa	Ad hoc	Link

Mapping refugee distributions with high spatial resolution

The goal is to provide a workflow for mapping with high-resolution refugee across Cameroon. We have access to reliable refugee totals for the eight monitored sites and for the 360 provinces (arrondissements). To gain more spatial insights we want to disaggregate those totals into a fine resolution grid following the format of gridded population which consist in estimating population counts over a complete partitioning of the country of interest into same-sized small grid cell (see Leyk (6) for a review of the concept). As summarised in Fig. 2, we developed two different methods to disaggregate refugee totals into grid cells depending on if the recorded refugee location was inside or outside of the UNHCR-led sites.

Mapping refugees inside UNHCR-led refugee sites

The mapping of refugees inside UNHCR-led refugee sites consists in spatially disaggregating the refugee totals observed for each site into all the grid cells covered by the site. To do so, we first checked the site boundaries against different satellite imagery (Google, ESRI and Bing basemap visualised in April 2023) to update them in case of late changes. We assessed the different settlement maps to select the most accurate in terms of number of buildings delineated and extent of settlement mapped. We manually digitised structures that could be seen from satellite imagery but were not in the satellite-imagery derived building footprint products. We overlayed a 100m grid over the sites and computed the number of structure footprints in each grid cell. We disaggregated the total number of refugees registered in each site into the 100m grid cells by using the number of structures as weights, adopting thus a deterministic dasymetric allocation of refugees (see Fig. 2).

Mapping refugee outside UNHCR-led refugee sites

The mapping of refugees outside sites consists in disaggregating at grid cell level the refugee count observed at admin 3 level as provided by the proGres database. There are two primary steps for this approach: (1) geolocating the records from the administrative register and (2) combining the geolocation with other spatial covariates to model the spatial allocation of the entire refugee population.

To geolocate the records from the registration database, we cleaned the text recorded in the freely inputed field about refugee location by removing arabic character, upper case and trailing spaces and replacing internal whitespace and apostrophe by hyphen. We then merged the refugee locations with the point locations from OpenStreetMap that went through the same cleaning process. We then converted the refugee point location layer to a continuous spatial indicator by creating a buffer for each refugee point based on the size of the refugee population assigned to this location. We then computed a distance layer from all grid cells to the buffered refugee location in order to avoid a strict subjective spatial cut-off. It is not possible to use the geolocation of refugee directly because: (1) the point location provided by OpenStreetMap may not always correspond exactly with the location provided in the registry, and (2) that would entail removing the refugees who could not be located with OpenStreetMap.

To address the second step that consists in modelling the refugee count at grid cell level, we adapted a similar procedure as developed by Stevens (4) and widely applied by the WorldPop research group (5) for disaggregating total population count into grid cells:

We select gridded covariates that are related to refugee locations (see Data section), in addition to the layer geolocating refugees with OpenStreetMap as describe in previous paragraph.

We estimate through a random-forest model the relationship at admin 3 level between the selected gridded covariates and the log of the total number of refugees divided by the total number of people, that is the number of refugees per inhabitant.

We predict the number of refugees per inhabitant using the fitted model for every grid cell contained in Cameroon and located outside of UNHCR-led refugee sites.

We multiply the estimated number of refugees per inhabitant by the estimated number of inhabitants to obtain the estimated number of refugees for every grid cell.

To ensure that the sum of the grid cells with refugees is exactly equal to the refugee totals reported at admin3 level, we calibrate the number of refugees per grid cell with the reported totals at admin3 level.

Modelling the refugees inside of UNHCR-led refugee sites

Disaggregating refugees totals inside the UNHCR-monitored refugee sites depends strictly on the quality of the detected structures from satellite imagery. Figure 3 shows a visual assessment of the different feature extraction products and provides a clear example of the limitations of each settlement map in detecting structures specific to refugee sites. The main one relates to leveraging outdated raw satellite image: all the five maps have missed the recent extension in the north of the site. Microsoft building footprints layer despite providing detailed information on building delineation massively under-detect structures. We conclude that the best accurate data source for obtaining a comprehensive image of the distribution of buildings within UNHCR-led refugee facilities is by adding a manual delineation of newer structures to the most spatially detailed layer that is Ecopia AI as shown in Fig. 3. Since the method of the disaggregation of refugee totals inside site relies on the number of structures in each grid cell, the manual digitisation can be restrained to dropping a point for each structure.

Modelling the refugees outside of UNHCR-led refugee sites

The first step to model refugees outside UNHCR-led refugee sites is to convert the text fields describing spatially relevant information into a spatial mapping format. The procedure adopted led to 83% of the refugee population to be associated to a point location. The point location is not necessarily the exact location of the text field, even less the actual residence of each refugee, but more an indication of the area they are likely to have stayed. Touboro is the province (admin3) with the most refugees not being mapped (10 981 refugees = 20% of the total refugee population in the province), followed by Mora (4620 refugees = 50% of the total refugee population), Meiganga (3844 refugees = 15% of the total refugee population), Batouri (3649 refugees = 38% of the total refugee population) and Makary (2915 refugees = 21% of the total refugee population).

The second step to disaggregate refugee totals outside of monitored sites involves the use of a random-forest model that explained 62% of the total variance in the proportion of refugees per inhabitant at administrative level 3. The goodness-of-fit is poorer than the disaggregation models for total population count as produced by the WorldPop research team that can explain up to 99% of variance in population count (5). When we use as target variable the number of refugees per hectare, the model fit jumps to 74% of explained variance. Nonetheless we want to model the variation linked specifically to the refugee population and introduce the country population related spatially varying allocation post hoc by multiplying the predicted number of refugees per inhabitant with the number of inhabitants.

Figure 4 illustrates the covariates that are the most important to model refugee locations. The top 2 are, as expected, the one geolocating refugees from the administrative register and the distance to refugee sites. If removed, they reduce by 28.7% and 18.5% the goodness-of-fit of the model. The following set of covariates that reduce by 10.8 to 11.4% the goodness-of-fit of the model are related to the morphology of the settlement layout (the mean perimeter and surface of the buildings) and the infrastructure (the distance to local roads). We can notice as well that recent conflict locations (2022 and 2021) are more important than past conflict locations (2018 and 2019).

The final step consists in combining the gridded refugee population inside and outside of monitored sites as represented in Fig. 5 to obtain a comprehensive gridded mapping product of refugees at 100m spatial resolution. Compared to the standard mapping procedure of UNHCR as displayed in Fig. 5, we see a clear gain in visualising spatial variation of refugee locations.

In the context of large-scale international forced displacement, administrative records and products derived from satellite imagery represent valuable untapped geographic information to address the specific needs of massive refugee population. The proposed high-resolution dasymetric mapping resulting in the disaggregation of refugee totals into grid cell-based maps, holds significant potential for harnessing these new data sources to produce country-wide maps of refugees at high spatial resolution and unlock evidence-based decision-making processes for local communities.

Data promise: a constantly updated information source with low cost for repurposing

The initial data assessment enabled by our modeling pipeline involved visually comparing several settlement maps in their ability to identify a very specific settlement type: monitored refugee sites. Our findings first showed the importance of using up-to-date satellite imagery in a context of rapid changes. Secondly Microsoft building footprints layer, either because of the date of the imagery used or the algorithm implemented was shown to massively under-detect structures in refugee sites which is not surprising as the coverage limitation of the Microsoft layer has already been reported in the literature (20). Lastly, we confirmed the primacy of Ecopia AI fine-grained feature extraction product layer that has already been demonstrated specifically for refugee settlements in Uganda when compared with grid cells-based settlement map (21).

From this mapping exercise based on novel data from registration systems, we learned that the spatial textual information inputted manually in the refugee register database can be successfully linked with geocoded information. Such information are coined as explicit geo-text data provided in an unstructured form that requires a specific modelling pipeline called geoparsing to generate the correct spatial footprint, involving a spatial gazetteer (24). To increase the accuracy of the refugee register geoparsing, the standardisation of the spatial free text needs to improve with more robust data collection system that agrees on naming standards and spatial resolution of the textual information (a middle ground between “Cameroon” and “behind the tree”) (25). Our assessment highlighted the top five provinces that have the poorest match between the register and the spatial gazetteer due to either missing coordinates from OpenStreetMap or lack of standardised naming convention and thus highlighted places where a collaboration between local mapping and local refugee recording team could lead to further cross-fertilization of the register and the spatial data systems. Geoparsing has broader impact in terms of data management system. For example, the registration database could enable storing alongside the records their spatial information in a spatial format to geocode in real time refugee residence and allow direct processing and modelling pipeline.

This exercise has demonstrated that registration systems can contain a wealth of operations data that can be repurposed efficiently with little cost and time. More generally leveraging refugee administrative data which constitutes a passive ‘ready-made’ data source (26), not purposefully collected for mapping, is a good example of the data revolution that demography is currently experiencing whereby data routinely collected for operations are leveraged to gain insights on population sizes and locations (27). Other similar undertakings in crisis setting have been to monitor population displacement from social media in Ukraine (28) or through mobile phone data analytics in Turkey (29). More broadly there has been a growing awareness inside refugee-monitoring institutions on the wealth of data routinely generated through operations that could be leveraged to provide socio-demographic insights provided that centralised and standardised data systems are being developed (30).

Model promise

Dasymetric mapping that is a non-uniform spatial cartography has a long history as a technique for refining the spatial representation of population by excluding unpopulated areas from the map. It has been however so far restricted to total population count, sometimes broken down by age and sex (7, 8, 31), or in data-rich context, to map transient population (32) or race (33). To our knowledge however, no population dasymetric mapping research has focused on the refugee’s subgroup in particular and none has been using register data. Indeed all high-resolution gridded cartography of social, economic or health-related indicators efforts could been previously done only from sampled data by spatial interpolation, for example for vaccination coverage map (34) or directly derived from satellite imagery with the creation of custom indices for example the ratio between people and night lights to map poverty with the lack of ground-truth validation (35). In this research in contrast, the administrative data provides us with comprehensive data on refugee totals for the entire country and free text information on refugee residence that supports the geolocalisation of the administrative records. We demonstrated that the dasymetric mapping approach, first developed to map census population can be applied for specific subgroups stemming from register databases as long as reliable subgroups population totals are available and their spatial variation can be linked with high-resolution spatial covariates. Furthermore, the dasymetric approach allowed us to produce for the first time a high-resolution grid-based allocation of refugees across the country which offers a visualisation of the spatial distribution of refugees and more specifically the areas where they concentrate. This kind of map is key to optimize the local delivering of primary services such as food or medical support. It has also implication when designing household and needs assessment surveys. Indeed sampling hard-to-reach population is a complex issue tackled mainly by purposeful interviewee selection or network-based sampling methods such as snowball sampling or respondent-driven sampling (36). By estimating a gridded map of refugee we can instead adopt survey sampling methods developed for gridded population (12, 37). Lastly although the gridded map is based on refugee totals and locations derived from the UNHCR registered refugee population, the spatial pattern and hotspots area can also help reach out the non-registered population provided they follow similar settling behaviour.

Model shortcomings

The random forest approach that we used to link the spatial variation of refugee totals with remote sensing derived covariates showed a poorer fit (62% of variance explained) than what is usually obtained when mapping the general population (often above 99% variance explained). A first explanation for this poorer fit is that we model a different type of variable that is the number of refugees per inhabitant rather than the number of people per hectare, such that the variations of refugee location that are similar to the country’s population are already accounted for. The remaining of the unexplained variance has the following roots: (1) the available spatial covariates are not able to capture all the spatial variations of refugee location and (2) the sample size available to train the model is smaller than when modelling an entire country’s population, as there are only 146 administrative level 3 units in Cameroon that currently have refugees. It was expected than the spatial variation of refugee location, especially self-settled, is harder to capture than the general population because of higher mobility patterns, smaller sizes and more driven by social features that are difficult to capture by remote-sensing approaches, such as the presence of community ties. However it would be interesting to see if including covariates mapping the operational delivery of services specifically targeting vulnerable populations – that were not available to us – could help increase the explained variance. A last source explaining the poorer fit of the model lies in the quality of the input variable that is the register that might varies according to locations. It is nonetheless impossible to assess it with the current data available.

Output technical shortcomings

We designed a method that is conservative and allocates refugees to all populated grid cells to avoid removing potential refugee location from the map. This approach though results in stretching out the spatial distribution of refugee populations producing a continuous mapping, with many grid cells estimating a very low refugee count (83% have less than 1 refugee allocated). In addition, the choice for a continuous mapping does not work well when refugee totals are low and administrative area are large. More generally the map is very dependent on the accuracy of two data inputs: the grided population map and the refugee registration data. The gridded population influences the refugee map through two mechanisms. First the grided population defines the spatial extent of the refugee mapping that is the set of grid cells considered as populated, which is dictated itself by the settlement map. New extent of settlement not detected from the satellite imagery will be missed by the population map and thus by the refugee map if it happens outside of the monitored sites that can be visually inspected. Rapid urban extension nonetheless can be closely linked with the influx of refugee population as seen in the urban development of Amman following the influx of Palestinian refugees (38). This issue is not prevalent in Cameroon as refugees tend to gather in the centre of the main cities rather than in the outskirts (see the example for Douala in Fig. 5). It is nonetheless possible to integrate in the refugee population model, the mapping of rapid settlement expansion as undertaken in North Jordan facing the Syrian refugee influx (39). The second impact of the population map is related to its role as denominator at the administrative unit level – for computing the refugee density before fitting the refugee population model– and at the grid cells – for predicting the refugee population. Conversely, inaccuracies in the numerator that is the refugee totals derived from the administrative register will have a direct impact on the accuracy of the dasymetric model if the ratio of the number of refugees per inhabitant is not correct. The quality of the refugee register influences the mapping through a second channel: the accuracy of the recorded geographic information from which is derived the main spatial covariate driving the subnational allocation.

Output political shortcomings

Lastly, it is essential to emphasize the sensitivity of the resulting outcome. A map presenting an estimated refugee count within each 100m grid cell of a country entails significant risks. Firstly, the statistical underpinnings of the modeling do not substantiate precise population size claims at this scale; rather, they provide insight into the relative spatial distribution of the refugee population. Secondly, the map could be utilized in policies that may adversely affect refugee populations. One technical approach involves aggregating the 100m grid cell counts to create a map with reduced spatial resolution and converting the counts into a bespoke index. An institutional approach entails imposing restrictions on access rights to ensure responsible usage as mentioned in article 42 of the UNHCR general data protection policy: “UNHCR shall take appropriate measures to identify and assess the potential risks, harms and benefits of automated decision-making and to prevent or mitigate any risks or harm identified for the data subjects” (40).

The study shows that the current demographic data revolution has unlocked the possibility for high-resolution mapping of refugee populations through the integration of administrative data, gridded population data, and settlement maps within a grid-based dasymetric mapping framework. The map can then be leveraged to design surveys on refugee living conditions, optimise operations and more broadly inform on areas of need for humanitarian relief due to influxes of incoming refugees.

Ethics approval and consent to participate

Ethical approval for this study was obtained from the University of Southampton Faculty Ethics Committee under submission ID 80630.

Consent for publication

Not applicable.

Availability of data and materials

The data that support the findings of this study are available from the United Nations High Commission for Refugees, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the United Nations High Commission for Refugees.

Competing interests

The authors declare that they have no competing interests.

Funding

Endorsed team of the Data Innovation Fund 2022-2023 which is a collaborative initiative between UNHCR’s Innovation Service, Global Data Service (GDS), and Division of Information and Telecommunications (DIST) aimed to empower UNHCR teams to address challenges and create opportunities to enhance UNHCR’s work in data analytics in responsible and appropriate ways.

Acknowledgements

The authors express their sincere gratitude to the management offices of the WorldPop research group and the United Nations High Commissioner for Refugees (UNHCR) for their invaluable support throughout the project. Special appreciation is extended to the dedicated team of UNHCR staff members responsible for meticulously recording the individual information of refugees. Their diligence and dedication significantly contributed to the availability and accuracy of the data essential for this study.

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No competing interests reported.

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Mapping refugee populations at high-resolution by unlocking humanitarian administrative data

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