Assessing Education from Space: Using Satellite Earth Observation to Quantify Overcrowding in Primary Schools in Rural Areas of Nigeria

Abstract

Nigeria is a country with a rapidly growing youthful population and the availability of good quality education for all is a key priority in the sustainable development of the country. An important element of this is the need to improve access to high-quality primary education in rural areas. A key indicator for progress on this is the provision of adequate classroom space for the more than 20 million learners in Nigerian public schools because overpopulated classrooms are known to have a strong negative impact on the performance of both pupils and their teachers. However, it can be challenging to rapidly monitor this indicator for the over 60 thousand primary schools, especially in rural areas. In this research, we used satellite Earth Observation (EO) and Nigerian government data to determine the size of available teaching spaces and evaluate the degree of overcrowding in a sample of 1900 randomly selected rural primary schools across 19 Nigerian states spanning all regions of the country. Our analysis shows that 81.4% of the schools examined were overcrowded according to the minimum standard threshold for school size of at least 1.2 m2 of classroom space per pupil defined by the Federal Government of Nigeria. Such overcrowding can be expected to have a negative impact on educational performance, on achieving universal basic education and UN Sustainable Development Goal (SDG) 4 (Quality Education), and it can lead to poverty. While measuring floor area can be performed manually on site, collecting, and reporting such data for the number of rural primary schools in a large and populous country such as Nigeria is a serious, time-consuming administrative task with considerable potential for errors and data gaps. Satellite EO data are readily available including for remote areas, are reproducible and are easy to update over time. This paper provides a proof-of-concept example of how such EO data can contribute to addressing this socio-economic dimension of the SDGs framework.

Evaluation of Soil Moisture-Based Satellite Precipitation Products over Semi-Arid Climatic Region

“…In contrast, precipitation observations serve as the primary input data required for SM modeling. However, all other products were inappropriate with respect to spatial and temporal variation of precipitation over the study area, with similar outcomes found in the previous findings of certain studies [37][38][39]. The temporal estimations of SM2Rain were in better agreement with gauge estimation, which is consistent with the findings of a particular study [10,24].…”

Assessment of Multi-Satellite Precipitation Products over the Himalayan Mountains of Pakistan, South Asia

“…Figure 14 depicts the assessment of four SPPs in terms of their ability to capture occurrence of precipitation events over the study area. Roebber [32] presented a performance diagram to graphically summarize the estimated values of the categorical indices (POD, SR, CSI, and Bias). The Roebber Performance Diagram (or simply performance diagram) was used in several previous investigations of SPPs in different countries [33].…”

“…It is essential to check their accuracy prior to their direct application [29]. Several scientists have evaluated the error characteristics of SPPs in many areas, including Africa [24,30], South America [25,31], Nigeria [32], Austria [6], China [33], Europe [12,25], Iran [17], India [10,34], United Arab Emirates [35], Taiwan [23], Brazil [36], and Pakistan [2,37,38]. Hamza et al [38] used both PERSIANN-CDR over the Hindukush Mountains along with other products (IMERG, TRMM and SM2-rain); he concluded that the PERSIAAN-CDR performance is more satisfactory then TRMM-3B42V7 and SM2Rain-ASCAT.…”

Using Data from Earth Observation to Support Sustainable Development Indicators: An Analysis of the Literature and Challenges for the Future

“…In addition, the usage of night-time satellite data can help monitor aspects of education facilities, such as access to electricity [182,183] and drinking water [184], which are closely linked to SDG 4.a.1 indicator (schools with basic service as electricity and internet). Furthermore, Andries et al [185] used VHR satellite data and enrolment data to calculate the primary school classroom area per pupil in rural Nigeria. They found that over 70% of the 1900 schools evaluated could be classified as overcrowded (according to Nigerian government metrics), this being highly correlated with poverty and literacy rates.…”

“…They found that over 70% of the 1900 schools evaluated could be classified as overcrowded (according to Nigerian government metrics), this being highly correlated with poverty and literacy rates. Andries et al [185] also analysed their area per pupil result via the MMF approach, which characterised this EO support as partial for the SDG indicators 4.1.2. (Completion rate in primary education, lower secondary education, upper secondary education) and 4.6.1 (Proportion of population in each age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sex).…”