# **1. Introduction**

In most countries of the world, population data count and distribution data in the form of census enumerations are used for population segmentation, planning, and a myriad of other functions that support the government. In Low and Middle-Income Country (LMIC) settings, spatially referenced data on populations are crucial for targeting interventions, monitoring the impact of population growth, health and environmental applications, poverty mapping and transport and city planning (Hay et al. 2005; Balk et al. 2006; Tatem et al. 2007). Enumeration areas (EAs) are the operational geographic units for the collection of census data (United Nations Statistics Division 2010). The set of all EAs of a country constitutes a partition of that country, with EAs not overlapping with each other. In principle, EAs are designed such that each unit contains a similar population size and conforms to certain constraints imposed by the logistics of counting large numbers of people with limited resources. Given data on population density at a sufficiently high spatial resolution, the process of designing EAs can be automated, which could substantially accelerate census mapping.

In some LMIC contexts, and particularly in conflicted affected areas, EAs are based on old population data or do not exist, a fact that has far-reaching consequences. In particular, the lack of properly-defined EAs means that there is no nationally representative sampling frame. National sampling frames are used to draw representative samples from the population to understand the geographic distribution of population characteristics (Turner 2003). Without sample weights provided by a national sampling frame, survey data sampled from a country’s population are likely to be biased, typically undersampling vulnerable populations (Thomson et al. 2012; Ellard-Gray et al. 2015). Therefore, the creation of EAs to form a sampling frame creates significant benefits to generate nationally representative data to allow evidence-based analysis informing governments and NGOs. Especially in fragile contexts where data are even more important due to the need of timely and evidence-based planning in volatile situations, up-to-date sampling frames are necessary.

However, defining new EAs is a challenging process that needs to consider a number of factors, including the quality of available data and the logistics of sampling; EAs need to make sense relative to the real-world distribution of people, buildings, and infrastructure. Good design of census EAs has a large impact on the efficiency of the census, a process which can cost on the order of hundreds of millions of dollars (Loots 2015). To better facilitate efficient census processes, EAs should lead to (i) better organization and management of the census itself and to (ii) small area statistics which meet common user needs (Martin 2002).

Historically the creation of census EAs has been an expensive problem solved by brute-force approaches such as physically walking to map EA boundaries, which can require thousands of staff and many years to complete (Lu 2009; Yacyshyn and Swanson 2011). For instance, in Zambia, the 2010 census mapping exercise was expected to take about two years to be completed at a total cost of US $ 7 million (United Nation Secretariat 2007). Since the advent of Geographic Information Systems (GIS) and high-resolution satellite photography, census cartographers in some countries have been able to manually digitize EA boundaries from satellite imagery, leading to better control of EA delineation. However, manual digitization of EAs is still cost-, time-, and labor-intensive. It is also prone to human error and can have poor accuracy (Alazab et al. 2009; Balinski et al. 2010; Cocking et al. 2011). In addition, poor quality or outdated satellite imagery can make it difficult or impossible to identify the boundaries or house blocks that align with actual house blocks and EA boundaries that are misaligned with real-world structures while natural boundaries can cause significant challenges in the field enumeration process. Furthermore, drawing or growing EAs manually with respect to population size and area constraints is challenging and can result in ‘nonsensical’ boundaries (e.g. a single structure split into different EAs). Finally, EAs or sampling frames in countries with large population 2