DOI: 10.5258/soton/wp00759
title: Field testing of pre-Enumeration Areas created using semi-automated delineation approach, Democratic Republic of Congo.

description: This report details the main outcomes of the field testing of pre-Enumeration Areas (EAs) created from WorldPop and Flominder’s semi-automated EA approach that took place across three test sites in the provinces of Kinshasa and Kongo-Central, Democratic Republic of the Congo in December 2019. The field testing was conducted over four days by the BCR technical staff with participation from UNFPA and WorldPop staff. Generally, EA boundaries from one census will form the basis for the EAs in the next census, with updates needed to account for new settlements and changes in population density. However, in countries where there hasn’t been a census for many years, often due to conflict or insecurity, EA boundaries can be incomplete, outdated, or missing altogether. The delineation of EAs is, therefore, a crucial pre-census activity but can often be particularly challenging and highly resource intensive. Creating EAs requires consideration of population and area size within each unit to ensure that they have approximately equal-sized populations and are a manageable size to be covered by census enumeration staff. To respond to this challenge, WorldPop has developed a semi-automatic approach of delineating pre-EAs to support census cartography. This approach utilises high-resolution gridded population estimates and digitised geographic features, including administrative boundaries, and natural and man-made features, such as rivers and roads, to divide the regions into small areas which are then merged to meet criteria specified for population size and geographic area. The last census in DRC was conducted in 1984; consequently, a recent, national, digital EA dataset which can be used for cartography planning does not exist. GRID3 is supporting the realisation of a fully digital 2020 round census in the DRC and is working closely with the National Institute of Statistics and the DRC Census Bureau (Bureau Central de Recensement, BCR) to provide technical guidance regarding options for incorporating geospatial methodologies into census planning and census cartography. As the DRC Census Bureau prepares for the 2nd National Population and Housing Census (RGPH2), a new dataset of EA boundaries is needed. As part of GRID3’s work with the BCR, a field test was conducted to assess the feasibility of using a semi-automated approach for the delineation of pre-EA boundaries. A preliminary pre-EA dataset was produced for the three test sites (Site 1: Quartier Kingu, Kinshasa (urban), Site 2: Quartier Dumi, Kinshasa (sub-urban), Site 3: Secteur Kasangulu, Kongo-Central (rural)) that span both rural and urban contexts. The geographic area covered by the three sites totalled 1,190 km2 and was sub-divided into approximately 312 pre-EAs. The pre-EAs created for the three test sites were classified as classes 1-3 depending on the degree to which the pre-EA boundaries followed visible features (e.g. roads). Class 1 being those pre-EAs with boundaries which fully followed visible features, class 2 boundaries followed visible features in part, and class 3 which didn’t follow visible features at all. A visual assessment was carried out by comparing the pre-EA boundaries with recent high-resolution satellite imagery. A subset of the pre-EAs (15 pre-EAs), covering classes 1, 2 and 3 were selected, and assessed in the field to check how the boundaries related to ground features and their feasibility as units for population enumeration. Class 1 pre-EAs were only found in urban contexts and tended to be bounded fully by roads, which were found to be simple for the field teams to follow. In class 2 and class 3 pre-EAs, the field teams were generally able to follow roads or tracks throughout the pre-EA to reach settlements, and ascertain when they had reached the boundary of the pre-EA using the maps and GPS location indicator on the tablets. The pre-EA boundaries were also created to avoid splitting settlements and therefore even in rural areas, the field teams were able to know where housing units needed to be enumerated. A range of limitations with this work have been identified, both with the methods and equipment used in the field data collection and the methods and input data used to produce the pre-EA boundaries. Despite the identified limitations and the challenges encountered in the field, the findings from the field test were generally consistent, with the pre-EAs created by the semi-automated approach found to be suitable for population enumeration in the field. Overall the fieldwork was successfully conducted and expectations were met and even exceeded: the BCR found that the pre-EA outputs were found to help facilitate enumeration, as the BCR team could navigate within the pre-EA boundaries and know which housing units to enumerate. The findings of the field test indicate this semi-automated approach to creating pre-EAs has the potential to be used by the BCR to create pre-EAs in preparation for census cartography, and offers large savings in terms of time, labour and cost. Nonetheless, it would be expected that the pre-EA outputs created in the approach are carefully reviewed in the lab, and manually edited as needed prior to census cartography. Then whilst in the field, the pre-EA boundaries should be validated. Limitations associated with input datasets can be addressed through a comprehensive review of existing datasets, incorporating newly available feature extraction datasets as appropriate. Further development of the approach and potential solutions and suggestions to overcome the identified limitations are outlined and discussed in detail in the report. We expect the findings of the field test in DRC to be transferable to other similar contexts, with the approach having applicability in countries with no recent digital EAs. We also expect the approach could be adapted to update digital EA boundaries in contexts with outdated EA datasets, but this should be explored through further research and testing in such contexts. Worth noting that in close collaboration with GeoData at the University of Southampton, UNFPA and multiple national statistical offices around the world, WorldPop has now converted the automatic delineation script to a user-friendly tool which require minimal GIS skill to run.