@article{adeleke_geographical_2022, title = {Geographical determinants and hotspots of out-of-school children in {Nigeria}}, volume = {4}, issn = {2544-7831}, url = {https://www.degruyter.com/document/doi/10.1515/edu-2022-0176/html}, doi = {10.1515/edu-2022-0176}, abstract = {In Nigeria, children lack access to primary school education, and this hinders their social, cognitive, emotional, and physical skills’ development. With one in every five of the world’s out-of-school children in Nigeria, achieving universal primary education by 2030 remains a challenge. Several studies have investigated the factors that have led to an increase in out-of-school children (OOSC); however, these studies are based on individual level and household predictors with little evidence on the geographical determinants. Hence, this study examines the relationship between OOSC and the socio-economic attributes of the geographical location where they reside. Findings of the spatial analysis show that Sokoto, Zamfara, Yobe, Taraba, and Plateau are the hotspots of out-of-school children. The result further reveals that there is spatial variation in the predictors of out-of-school children in the country. Poverty and internally generated revenue (IGR) predict more cases of school non-attendance in northern Nigeria while foreign direct investment determines the number of children that are out-of-school in the southern region. The study recommends spatially explicit policies to reduce the number of OOSC in Nigeria.}, language = {en}, number = {1}, urldate = {2023-03-11}, journal = {Open Education Studies}, author = {Adeleke, Richard and Alabede, Opeyemi}, month = jan, year = {2022}, note = {Publisher: De Gruyter Open Access}, keywords = {Nigeria, Spatial analysis, foreign direct investment, out-of-school children, poverty}, pages = {345--355}, } @article{jochem_tools_2021, title = {Tools for mapping multi-scale settlement patterns of building footprints: {An} introduction to the {R} package foot}, volume = {16}, issn = {1932-6203}, shorttitle = {Tools for mapping multi-scale settlement patterns of building footprints}, url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247535}, doi = {10.1371/journal.pone.0247535}, abstract = {Spatial datasets of building footprint polygons are becoming more widely available and accessible for many areas in the world. These datasets are important inputs for a range of different analyses, such as understanding the development of cities, identifying areas at risk of disasters, and mapping the distribution of populations. The growth of high spatial resolution imagery and computing power is enabling automated procedures to extract and map building footprints for whole countries. These advances are enabling coverage of building footprint datasets for low and middle income countries which might lack other data on urban land uses. While spatially detailed, many building footprints lack information on structure type, local zoning, or land use, limiting their application. However, morphology metrics can be used to describe characteristics of size, shape, spacing, orientation and patterns of the structures and extract additional information which can be correlated with different structure and settlement types or neighbourhoods. We introduce the foot package, a new set of open-source tools in a flexible R package for calculating morphology metrics for building footprints and summarising them in different spatial scales and spatial representations. In particular our tools can create gridded (or raster) representations of morphology summary metrics which have not been widely supported previously. We demonstrate the tools by creating gridded morphology metrics from all building footprints in England, Scotland and Wales, and then use those layers in an unsupervised cluster analysis to derive a pattern-based settlement typology. We compare our mapped settlement types with two existing settlement classifications. The results suggest that building patterns can help distinguish different urban and rural types. However, intra-urban differences were not well-predicted by building morphology alone. More broadly, though, this case study demonstrates the potential of mapping settlement patterns in the absence of a housing census or other urban planning data.}, language = {en}, number = {2}, urldate = {2021-03-18}, journal = {PLOS ONE}, author = {Jochem, Warren C. and Tatem, Andrew J.}, month = feb, year = {2021}, note = {Publisher: Public Library of Science}, keywords = {Cities, Open data, Radii, Rural areas, Settlement patterns, Software tools, Urban areas, Urban environments}, pages = {e0247535}, } @article{qader_semi-automatic_2021, title = {Semi-automatic mapping of pre-census enumeration areas and population sampling frames}, volume = {8}, copyright = {2021 Crown}, issn = {2662-9992}, url = {https://www.nature.com/articles/s41599-020-00670-0}, doi = {10.1057/s41599-020-00670-0}, abstract = {Enumeration Areas (EAs) are the operational geographic units for the collection and dissemination of census data and are often used as a national sampling frame for various types of surveys. In many poor or conflict-affected countries, EA demarcations are incomplete, outdated, or missing. Even for countries that are stable and prosperous, creating and updating EAs is one of the most challenging yet essential tasks in the preparation for a national census. Commonly, EAs are created by manually digitising small geographic units on high-resolution satellite imagery or physically walking the boundaries of units, both of which are highly time, cost, and labour intensive. In addition, creating EAs requires considering population and area size within each unit. This is an optimisation problem that can best be solved by a computer. Here, for the first time, we produce a semi-automatic mapping of pre-defined census EAs based on high-resolution gridded population and settlement datasets and using publicly available natural and administrative boundaries. We demonstrate the approach in generating rural EAs for Somalia where such mapping is not existent. In addition, we compare our automated approach against manually digitised EAs created in urban areas of Mogadishu and Hargeysa. Our semi-automatically generated EAs are consistent with standard EAs, including having identifiable boundaries for field teams to follow on the ground, and appropriate sizing and population for coverage by an enumerator. Furthermore, our semi-automated urban EAs have no gaps, in contrast, to manually drawn urban EAs. Our work shows the time, labour and cost-saving value of automated EA delineation and points to the potential for broadly available tools suitable for low-income and data-poor settings but applicable to potentially wider contexts.}, language = {en}, number = {1}, urldate = {2021-03-18}, journal = {Humanities and Social Sciences Communications}, author = {Qader, Sarchil and Lefebvre, Veronique and Tatem, Andrew and Pape, Utz and Himelein, Kristen and Ninneman, Amy and Bengtsson, Linus and Bird, Tomas}, month = jan, year = {2021}, note = {Number: 1 Publisher: Palgrave}, pages = {1--14}, } @article{afoakwah_how_2021, title = {How does school travel time impact children’s learning outcomes in a developing country?}, issn = {1569-5239}, url = {https://link.springer.com/epdf/10.1007/s11150-020-09533-8}, doi = {10.1007/s11150-020-09533-8}, abstract = {Nearly 88\% of children in sub-Saharan Africa will not be able to read by the time they complete primary school. We explore this phenomenon by using household data from the Ghana Living Standards Survey to examine the link between school travel time and children’s learning outcomes. Using district variations in school density to resolve endogeneity associated with children’s travel time to school and their learning outcomes, we find that more than 90\% of children travel on foot to school and this negatively affects their ability to read and write in English or French as well as their ability to read and write in their native languages. We further show that boys, children in rural areas and those who travel more than the 75th percentile travel time (30 minutes) have poorer learning outcomes. Our findings highlight number of class hours missed and poor health as the main channels through which school travel time affects learning outcomes. Policy initiatives to improve children’s learning should consider reducing the costs associated with their school travel time. Considering that governments have limited resources with competing needs, policies aimed at reducing travel time should generally target children who commute more than 30 minutes to school and those in rural locations.}, language = {en}, urldate = {2021-03-25}, journal = {Review of Economics of the Household}, author = {Afoakwah, Clifford and Koomson, Isaac}, year = {2021}, } @article{sakti_school_2021, title = {School location analysis by integrating the accessibility, natural and biological hazards to support equal access to education}, volume = {11}, doi = {10.3390/ijgi11010012}, abstract = {Abstract: This study proposes a new model for land suitability for educational facilities based on spatial product development to determine the optimal locations for achieving education targets in West Java, Indonesia. Single-aspect approaches, such as accessibility and spatial hazard analyses, have not been widely applied in suitability assessments on the location of educational facilities. Model development was performed based on analyses of the economic value of the land and on the integration of various parameters across three main aspects: accessibility, comfort, and a multinatural/biohazard (disaster) risk index. Based on the maps of disaster hazards, higher flood-prone areas are found to be in gentle slopes and located in large cities. Higher risks of landslides are spread throughout the study area, while higher levels of earthquake risk are predominantly in the south, close to the active faults and megathrusts present. Presently, many schools are located in very high vulnerability zones (2057 elementary, 572 junior high, 157 senior high, and 313 vocational high schools). The comfort-level map revealed 13,459 schools located in areas with very low and low comfort levels, whereas only 2377 schools are in locations of high or very high comfort levels. Based on the school accessibility map, higher levels are located in the larger cities of West Java, whereas schools with lower accessibility are documented far from these urban areas. In particular, senior high school accessibility is predominant in areas of lower accessibility levels, as there are comparatively fewer facilities available in West Java. Overall, higher levels of suitability are spread throughout West Java. These distribution results revealed an expansion of the availability of schools by area: senior high schools, 303,973.1 ha; vocational high schools, 94,170.51 ha; and junior high schools, 12,981.78 ha. Changes in elementary schools (3936.69 ha) were insignificant, as the current number of elementary schools is relatively much higher. This study represents the first to attempt to integrate these four parameters—accessibility, multi natural hazard, biohazard, comfort index, and land value—to determine potential areas for new schools to achieve educational equity targets. Keywords: school location; natural and biological hazards; accessibility model; COVID-19; West Java Province; Indonesia}, number = {1}, journal = {ISPRS International Journal of Geo-Information}, author = {Sakti, Anjar Dimara and Rahadianto, Muhammad Ario Eko and Pradhan, Biswajeet and Muhammad, Hubbi Nashrullah and Andani, I. Gusti Ayu and Sarli, Prasanti Widyasih and Abdillah, Muhammad Rais and Anggraini, Tania Septi and Purnomo, Andhika Dimas and Ridwana, Riki}, year = {2021}, note = {Publisher: MDPI KerkoCite.ItemAlsoKnownAs: 4682641:CB8PKGK6}, pages = {12}, } @techreport{grid3_education_2020, title = {Education {Coverage} in {Sierra} {Leone}}, url = {https://grid3.org/publications/education-coverage-in-sierra-leone}, author = {{GRID3}}, month = dec, year = {2020}, note = {KerkoCite.ItemAlsoKnownAs: 2129771:982SWX54}, keywords = {\_C:Luxembourg LUX, \_C:Sierra Leone SLE, \_C:United Kingdom GBR, \_\_C:filed:1, \_\_C:scheme:1}, } @article{mzuza_inclusion_2020, title = {Inclusion of {GIS} in student teacher training and its significance in higher education in southern {African} countries}, volume = {29}, issn = {1038-2046}, url = {https://doi.org/10.1080/10382046.2019.1684660}, doi = {10.1080/10382046.2019.1684660}, abstract = {Studies have been carried out on the use of geographical information systems (GIS) in teacher training, especially in the developed countries. In southern African countries, nevertheless, the scenario is different because GIS education appears to be a rather new field of study. This study therefore used systematic review to collect data. This method assists in finding and understanding the outcomes of other research conducted within the same field of study. The results reveal that only three countries (South Africa, Botswana and Malawi) teach GIS at their teacher-training universities and secondary schools. In Lesotho, GIS are only taught in secondary schools. In other countries, such as Zambia, Namibia and Zimbabwe, GIS are not taught at all at teacher-training universities and secondary schools but only at universities or departments that do not train teachers. There is no inclusion of GIS at the universities in Angola, Mozambique, Swaziland and Lesotho. Countries that use GIS have demonstrated that the course helps with decision-making, critical thinking and inquiry-based and learner-centred learning, which have the ability to improve the quality of education. Educators and policy-makers are encouraged to reinforce the inclusion of GIS and use of relevant pedagogical skills in teacher-training universities.}, number = {4}, urldate = {2021-03-07}, journal = {International Research in Geographical and Environmental Education}, author = {Mzuza, Maureen Kapute and Westhuizen, Christo Van der}, month = oct, year = {2020}, note = {Publisher: Routledge \_eprint: https://doi.org/10.1080/10382046.2019.1684660}, keywords = {Southern Africa, \_C:Angola AGO, \_C:Botswana BWA, \_C:Canada CAN, \_C:Finland FIN, \_C:India IND, \_C:Italy ITA, \_C:Kenya KEN, \_C:Lesotho LSO, \_C:Malawi MWI, \_C:Malaysia MYS, \_C:Mozambique MOZ, \_C:Namibia NAM, \_C:Netherlands NLD, \_C:Portugal PRT, \_C:Rwanda RWA, \_C:South Africa ZAF, \_C:Tanzania TZA, \_C:Turkey TUR, \_C:United States USA, \_C:Zambia ZMB, \_C:Zimbabwe ZWE, \_C:eSwatini SWZ, \_\_C:filed:1, \_\_C:scheme:1, geographical information systems (GIS), geography, motivation tool, technology}, pages = {332--346}, } @article{leasure_national_2020, title = {National population mapping from sparse survey data: {A} hierarchical {Bayesian} modeling framework to account for uncertainty}, volume = {117}, copyright = {Copyright © 2020 the Author(s). Published by PNAS.. https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).}, issn = {0027-8424, 1091-6490}, shorttitle = {National population mapping from sparse survey data}, url = {https://www.pnas.org/content/117/39/24173}, doi = {10.1073/pnas.1913050117}, abstract = {Population estimates are critical for government services, development projects, and public health campaigns. Such data are typically obtained through a national population and housing census. However, population estimates can quickly become inaccurate in localized areas, particularly where migration or displacement has occurred. Some conflict-affected and resource-poor countries have not conducted a census in over 10 y. We developed a hierarchical Bayesian model to estimate population numbers in small areas based on enumeration data from sample areas and nationwide information about administrative boundaries, building locations, settlement types, and other factors related to population density. We demonstrated this model by estimating population sizes in every 10- m grid cell in Nigeria with national coverage. These gridded population estimates and areal population totals derived from them are accompanied by estimates of uncertainty based on Bayesian posterior probabilities. The model had an overall error rate of 67 people per hectare (mean of absolute residuals) or 43\% (using scaled residuals) for predictions in out-of-sample survey areas (approximately 3 ha each), with increased precision expected for aggregated population totals in larger areas. This statistical approach represents a significant step toward estimating populations at high resolution with national coverage in the absence of a complete and recent census, while also providing reliable estimates of uncertainty to support informed decision making.}, language = {en}, number = {39}, urldate = {2021-03-18}, journal = {Proceedings of the National Academy of Sciences}, author = {Leasure, Douglas R. and Jochem, Warren C. and Weber, Eric M. and Seaman, Vincent and Tatem, Andrew J.}, month = sep, year = {2020}, pmid = {32929009}, note = {Publisher: National Academy of Sciences Section: Social Sciences}, keywords = {Bayesian statistics, demography, geographic information systems, international development, remote sensing}, pages = {24173--24179}, } @article{jochem_classifying_2020, title = {Classifying settlement types from multi-scale spatial patterns of building footprints}, issn = {2399-8083}, url = {https://doi.org/10.1177/2399808320921208}, doi = {10.1177/2399808320921208}, abstract = {Urban settlements and urbanised populations continue to grow rapidly and much of this transition is occurring in less developed countries. Remote sensing techniques are now often applied to monitor urbanisation and changes in settlement patterns. In particular, increasing availability of very high resolution imagery ({\textless}1 m spatial resolution) and computing power is enabling complete sets of settlement data in the form of building footprints to be extracted from imagery. These settlement data provide information on the changes occurring in cities, particularly in countries which may lack other data on urbanisation. While spatially detailed, extracted building footprints typically lack other information that identify building types or can be used to differentiate intra-urban land uses or neighbourhood types. This work demonstrates an approach to classifying settlement types through multi-scale spatial patterns of urban morphology visible in building footprint data extracted from very high resolution imagery. The work uses a Gaussian mixture modelling approach to select and hierarchically merge components into clusters. The results are maps classifying settlement types on a high spatial resolution (100 m) grid. The approach is applied in Kaduna, Nigeria; Kinshasa, Democratic Republic of the Congo; and Maputo, Mozambique and demonstrates the potential of computational methods to take advantage of large spatial datasets and extract meaningful information to support monitoring of urban areas. The model-based approach produces a hierarchy of potential clustering solutions, and we suggest that this can be used in partnership with local knowledge of the context when creating settlement typologies.}, language = {en}, urldate = {2021-03-18}, journal = {Environment and Planning B: Urban Analytics and City Science}, author = {Jochem, Warren C and Leasure, Douglas R and Pannell, Oliver and Chamberlain, Heather R and Jones, Patricia and Tatem, Andrew J}, month = may, year = {2020}, note = {Publisher: SAGE Publications Ltd STM}, keywords = {Urban morphology, classification, land use, spatial analysis, urban analytics}, pages = {2399808320921208}, } @article{utazi_geospatial_2020, title = {Geospatial variation in measles vaccine coverage through routine and campaign strategies in {Nigeria}: {Analysis} of recent household surveys}, volume = {38}, issn = {0264-410X}, shorttitle = {Geospatial variation in measles vaccine coverage through routine and campaign strategies in {Nigeria}}, url = {https://www.sciencedirect.com/science/article/pii/S0264410X20303017}, doi = {10.1016/j.vaccine.2020.02.070}, abstract = {Measles vaccination campaigns are conducted regularly in many low- and middle-income countries to boost measles control efforts and accelerate progress towards elimination. National and sometimes first-level administrative division campaign coverage may be estimated through post-campaign coverage surveys (PCCS). However, these large-area estimates mask significant geographic inequities in coverage at more granular levels. Here, we undertake a geospatial analysis of the Nigeria 2017–18 PCCS data to produce coverage estimates at 1 × 1 km resolution and the district level using binomial spatial regression models built on a suite of geospatial covariates and implemented in a Bayesian framework via the INLA-SPDE approach. We investigate the individual and combined performance of the campaign and routine immunization (RI) by mapping various indicators of coverage for children aged 9–59 months. Additionally, we compare estimated coverage before the campaign at 1 × 1 km and the district level with predicted coverage maps produced using other surveys conducted in 2013 and 2016–17. Coverage during the campaign was generally higher and more homogeneous than RI coverage but geospatial differences in the campaign’s reach of previously unvaccinated children are shown. Persistent areas of low coverage highlight the need for improved RI performance. The results can help to guide the conduct of future campaigns, improve vaccination monitoring and measles elimination efforts. Moreover, the approaches used here can be readily extended to other countries.}, language = {en}, number = {14}, urldate = {2021-03-18}, journal = {Vaccine}, author = {Utazi, C. Edson and Wagai, John and Pannell, Oliver and Cutts, Felicity T. and Rhoda, Dale A. and Ferrari, Matthew J. and Dieng, Boubacar and Oteri, Joseph and Danovaro-Holliday, M. Carolina and Adeniran, Adeyemi and Tatem, Andrew J.}, month = mar, year = {2020}, keywords = {Geospatial analysis, Measles vaccine, Post-campaign coverage survey, Routine immunization, Supplementary immunization activities}, pages = {3062--3071}, } @article{cobb_geospatial_2020, title = {Geospatial {Analysis}: {A} {New} {Window} {Into} {Educational} {Equity}, {Access}, and {Opportunity}}, volume = {44}, issn = {0091-732X}, shorttitle = {Geospatial {Analysis}}, url = {https://doi.org/10.3102/0091732X20907362}, doi = {10.3102/0091732X20907362}, abstract = {A robust body of geographic education policy research has been amassing over the past 25 years, as researchers from a variety of disciplinary backgrounds have recognized the value of examining education phenomena from a spatial perspective. In this chapter, I synthesize 42 studies that examine education issues using a geographic information system, or GIS. The review is framed by the major thread that runs through this body of research: educational equity, access, and opportunity. I summarize the research within seven theme-based research topics and offer examples of geospatial analysis as applied to education. The chapter includes a discussion of the major barriers and limitation facing GIS researchers and offers thoughts about the future.}, language = {en}, number = {1}, urldate = {2021-03-07}, journal = {Review of Research in Education}, author = {Cobb, Casey D.}, month = mar, year = {2020}, note = {Publisher: American Educational Research Association}, keywords = {Lebanon\_event\_2021, \_C:Brazil BRA, \_C:Canada CAN, \_C:Chile CHL, \_C:Japan JPN, \_C:Netherlands NLD, \_C:New Zealand NZL, \_C:United Kingdom GBR, \_C:United States USA, \_\_C:filed:1, \_\_C:scheme:1}, pages = {97--129}, } @article{boo_grid-based_2020, title = {A grid-based sample design framework for household surveys}, volume = {4}, issn = {2572-4754}, url = {https://gatesopenresearch.org/articles/4-13/v1}, doi = {10.12688/gatesopenres.13107.1}, abstract = {Traditional sample designs for household surveys are contingent upon the availability of a representative primary sampling frame. This is defined using enumeration units and population counts retrieved from decennial national censuses that can become rapidly inaccurate in highly dynamic demographic settings. To tackle the need for representative sampling frames, we propose an original grid-based sample design framework introducing essential concepts of spatial sampling in household surveys. In this framework, the sampling frame is defined based on gridded population estimates and formalized as a bi-dimensional random field, characterized by spatial trends, spatial autocorrelation, and stratification. The sampling design reflects the characteristics of the random field by combining contextual stratification and proportional to population size sampling. A nonparametric estimator is applied to evaluate the sampling design and inform sample size estimation. We demonstrate an application of the proposed framework through a case study developed in two provinces located in the western part of the Democratic Republic of the Congo. We define a sampling frame consisting of settled cells with associated population estimates. We then perform a contextual stratification by applying a principal component analysis (PCA) and k -means clustering to a set of gridded geospatial covariates, and sample settled cells proportionally to population size. Lastly, we evaluate the sampling design by contrasting the empirical cumulative distribution function for the entire population of interest and its weighted counterpart across different sample sizes and identify an adequate sample size using the Kolmogorov-Smirnov distance between the two functions. The results of the case study underscore the strengths and limitations of the proposed grid-based sample design framework and foster further research into the application of spatial sampling concepts in household surveys.}, language = {en}, urldate = {2021-03-18}, journal = {Gates Open Research}, author = {Boo, Gianluca and Darin, Edith and Thomson, Dana R. and Tatem, Andrew J.}, month = jan, year = {2020}, pages = {13}, } @article{zhang_contemporary_2020, title = {Contemporary patterns and issues of school segregation and white flight in {U}.{S}. metropolitan areas: towards spatial inquiries}, issn = {0343-2521, 1572-9893}, shorttitle = {Contemporary patterns and issues of school segregation and white flight in {U}.{S}. metropolitan areas}, url = {http://link.springer.com/10.1007/s10708-019-10122-1}, doi = {10.1007/s10708-019-10122-1}, language = {en}, urldate = {2021-03-07}, journal = {GeoJournal}, author = {Zhang, Charlie H. and Ruther, Matt}, month = jan, year = {2020}, keywords = {Lebanon\_event\_2021, \_C:Chile CHL, \_C:United Kingdom GBR, \_C:United States USA, \_\_C:filed:1, \_\_C:scheme:1}, } @article{lloyd_using_2020, title = {Using {GIS} and {Machine} {Learning} to {Classify} {Residential} {Status} of {Urban} {Buildings} in {Low} and {Middle} {Income} {Settings}}, volume = {12}, copyright = {http://creativecommons.org/licenses/by/3.0/}, url = {https://www.mdpi.com/2072-4292/12/23/3847}, doi = {10.3390/rs12233847}, abstract = {Utilising satellite images for planning and development is becoming a common practice as computational power and machine learning capabilities expand. In this paper, we explore the use of satellite image derived building footprint data to classify the residential status of urban buildings in low and middle income countries. A recently developed ensemble machine learning building classification model is applied for the first time to the Democratic Republic of the Congo, and to Nigeria. The model is informed by building footprint and label data of greater completeness and attribute consistency than have previously been available for these countries. A GIS workflow is described that semiautomates the preparation of data for input to the model. The workflow is designed to be particularly useful to those who apply the model to additional countries and use input data from diverse sources. Results show that the ensemble model correctly classifies between 85\% and 93\% of structures as residential and nonresidential across both countries. The classification outputs are likely to be valuable in the modelling of human population distributions, as well as in a range of related applications such as urban planning, resource allocation, and service delivery.}, language = {en}, number = {23}, urldate = {2021-03-18}, journal = {Remote Sensing}, author = {Lloyd, Christopher T. and Sturrock, Hugh J. W. and Leasure, Douglas R. and Jochem, Warren C. and Lázár, Attila N. and Tatem, Andrew J.}, month = jan, year = {2020}, note = {Number: 23 Publisher: Multidisciplinary Digital Publishing Institute}, keywords = {building classification, building footprint, machine learning, residential, superlearner}, pages = {3847}, } @article{combe_design_2020, title = {The {Design} of {Teacher} {Assignment}: {Theory} and {Evidence}.}, url = {https://www.dropbox.com/s/92xsi3rg1jx1pzc/CTT.pdf?dl=0}, abstract = {To assign teachers to schools, a modified version of the well-known deferred acceptance mechanism has been proposed in the literature and is used in practice. We show that this mechanism fails to be fair and efficient for both teachers and schools. We identify a class of strategyproof mechanisms that cannot be improved upon in terms of both efficiency and fairness. Using a rich dataset on teachers’ applications in France, we estimate teachers preferences and perform a counterfactual analysis. The results show that these mechanisms perform much better than the modified version of deferred acceptance. For instance, the number of teachers moving from their positions more than triples under our mechanism.}, language = {en}, author = {Combe, Julien and Tercieux, Olivier and Terrier, Camille}, year = {2020}, note = {KerkoCite.ItemAlsoKnownAs: 2129771:NBGHXE2S}, keywords = {Lebanon\_event\_2021, \_C:Czech Republic CZE, \_C:France FRA, \_C:Germany DEU, \_C:Ireland IRL, \_C:Italy ITA, \_C:Mexico MEX, \_C:Peru PER, \_C:Portugal PRT, \_C:Spain ESP, \_C:Turkey TUR, \_C:United States USA, \_C:Uruguay URY, \_\_C:filed:1, \_\_C:scheme:1, ⛔ No DOI found}, pages = {88}, } @techreport{flowminder_foundation_covid-19_2020, title = {{COVID}-19: {Supporting} the {Government} of {Sierra} {Leone} with mobility data}, url = {https://www.flowminder.org/media/vg5ov5s5/sle_africell_report_v1-2_dec20.pdf}, author = {Flowminder Foundation}, year = {2020}, keywords = {\_C:Sierra Leone SLE}, } @techreport{mackintosh_education_2020, type = {Research and {Policy} {Paper}}, title = {Education {Workforce} {Management} in {Sierra} {Leone}}, language = {en}, institution = {Education Commission}, author = {Mackintosh, Alasdair and Ramirez, Ana and Atherton, Paul and Collis, Victoria and Mason-Sesay, Miriam and Bart-Williams, Claudius}, year = {2020}, note = {KerkoCite.ItemAlsoKnownAs: 2129771:2U4XHCEW 2129771:4JVXKYSH 2129771:8GWCPEXT 2129771:C4BFMJUQ 2339240:28FQ3DS8 2405685:QEWLRFDX 2601447:YEE5ED32}, keywords = {\_C:Australia AUS, \_C:Chile CHL, \_C:China CHN, \_C:Eritrea ERI, \_C:France FRA, \_C:Gambia GMB, \_C:Iceland ISL, \_C:India IND, \_C:Italy ITA, \_C:Kenya KEN, \_C:Lebanon LBN, \_C:Lesotho LSO, \_C:Mozambique MOZ, \_C:Netherlands NLD, \_C:Nigeria NGA, \_C:Pakistan PAK, \_C:Senegal SEN, \_C:Sierra Leone SLE, \_C:Tanzania TZA, \_C:Togo TGO, \_C:Uganda UGA, \_C:Zambia ZMB, \_\_C:filed:1, \_\_C:scheme:1, ⛔ No DOI found}, pages = {32}, } @techreport{mackintosh_education_2020, type = {Research and {Policy} {Paper}}, title = {Education {Workforce} {Recruitment} and {Matching} in {Sierra} {Leone}}, url = {https://educationcommission.org/wp-content/uploads/2020/12/4-EW-Recruitment-and-Matching-Paper.pdf}, language = {en}, urldate = {2021-01-30}, institution = {Education Commission}, author = {Mackintosh, Alasdair and Ramirez, Ana and Atherton, Paul and Collis, Victoria and Mason-Sesay, Miriam and Bart-Williams, Claudius}, year = {2020}, note = {KerkoCite.ItemAlsoKnownAs: 2129771:CW55SAM3 2129771:SLC5ADTU 2339240:8YVNU64V 2405685:KZBP674C}, keywords = {\_C:Bangladesh BGD, \_C:Ethiopia ETH, \_C:France FRA, \_C:Gambia GMB, \_C:Ghana GHA, \_C:India IND, \_C:Malawi MWI, \_C:Mexico MEX, \_C:Sierra Leone SLE, \_C:Tanzania TZA, \_C:Zimbabwe ZWE, \_\_C:filed:1, \_\_C:scheme:1, ⛔ No DOI found}, pages = {32}, } @techreport{mackintosh_education_2020, address = {New York, NY}, type = {Research and {Policy} {Paper}}, title = {Education {Workforce} {Spatial} {Analysis} in {Sierra} {Leone}}, url = {https://educationcommission.org/wp-content/uploads/2020/12/2-EW-Spatial-Analysis-Paper.pdf}, language = {en}, number = {2}, institution = {Education Workforce Initiative}, author = {Mackintosh, Alasdair and Ramirez, Ana and Atherton, Paul and Collis, Victoria and Mason-Sesay, Miriam and Bart-Williams, Claudius}, year = {2020}, note = {KerkoCite.ItemAlsoKnownAs: 2129771:352YTJXA 2129771:B6HL4K8H 2129771:IDUHPLI9 2129771:KY2Y4AEB 2129771:PDLNHLL4 2129771:RZJA4ZWV 2339240:Q42KZNBD 2339240:V7GZZAMR 2405685:2HRVB9KF 2405685:B5LNLGZL 2405685:FS263PV6 2486141:JNVMUL8V 2601447:VTLIFB9A 4042040:ZQNXW7RV}, keywords = {\_C:India IND, \_C:Sierra Leone SLE, \_\_C:filed:1, \_\_C:scheme:1, ⛔ No DOI found}, pages = {31}, } @techreport{mackintosh_education_2020, address = {New York, NY}, type = {Research and {Policy} {Paper}}, title = {Education {Workforce} {Supply} and {Needs} in {Sierra} {Leone}}, url = {https://educationcommission.org/wp-content/uploads/2020/12/3-EW-Supply-and-Needs-Paper.pdf}, language = {en}, number = {3}, institution = {Education Workforce Initiative}, author = {Mackintosh, Alasdair and Ramirez, Ana and Atherton, Paul and Collis, Victoria and Mason-Sesay, Miriam and Bart-Williams, Claudius}, year = {2020}, note = {KerkoCite.ItemAlsoKnownAs: 2129771:23HCCGWI 2129771:6QUWDNYH 2129771:8MNC2N4I 2129771:NVEG4QQ7 2129771:X4L45EA5 2339240:2KB28MDX 2405685:52ZGRDGR 2405685:8MIWRFD2 2486141:SXAGH5JI 2601447:ZQXDP3AD 4556019:J9ZYVZC6}, keywords = {\_C:Canada CAN, \_C:Chile CHL, \_C:Finland FIN, \_C:Ghana GHA, \_C:Korea, Republic KOR, \_C:Malawi MWI, \_C:Nigeria NGA, \_C:Peru PER, \_C:Sierra Leone SLE, \_C:Singapore SGP, \_C:South Africa ZAF, \_C:Viet Nam VNM, \_\_C:filed:1, \_\_C:scheme:1, \_yl:c, ⛔ No DOI found}, pages = {41}, } @article{tieken_rethinking_2019, title = {Rethinking the {School} {Closure} {Research}: {School} {Closure} as {Spatial} {Injustice}}, volume = {89}, issn = {0034-6543}, shorttitle = {Rethinking the {School} {Closure} {Research}}, url = {https://doi.org/10.3102/0034654319877151}, doi = {10.3102/0034654319877151}, abstract = {Recent mass closings of schools have rocked cities across the United States. Though these urban closures—and widespread community protests—have made headlines, rural schools have also long experienced and opposed the closure of their schools. A large body of research examines these urban and rural closures from a variety of perspectives, including their economic motivations and policy implications. This review reexamines this literature, looking across context to show how school closure can produce spatial injustice. Advocates argue that closures further academic opportunity, efficiency, and equality. But our analysis shows that closures are unevenly distributed, disproportionately affecting places where poor communities and communities of color live, and they can bring negative effects, harming students and adults and reducing their access to an important educational and community institution. We conclude with recommendations for research and practice.}, language = {en}, number = {6}, urldate = {2021-03-07}, journal = {Review of Educational Research}, author = {Tieken, Mara Casey and Auldridge-Reveles, Trevor Ray}, month = dec, year = {2019}, note = {Publisher: American Educational Research Association}, keywords = {Lebanon\_event\_2021, \_C:Georgia GEO, \_C:Germany DEU, \_C:Mexico MEX, \_C:United Kingdom GBR, \_C:United States USA, \_\_C:filed:1, \_\_C:scheme:1, rural, school closure, spatial injustice, urban}, pages = {917--953}, } @article{mann_role_2019, title = {The {Role} of {Place}, {Geography}, and {Geographic} {Information} {Systems} in {Educational} {Research}}, volume = {5}, issn = {2332-8584}, url = {https://doi.org/10.1177/2332858419869340}, doi = {10.1177/2332858419869340}, abstract = {Despite the strong relationship between geography and education policy, educational research tends to draw from other fields of inquiry such as economics, political science, and history. This special topics collection centers the usefulness of geography and place in educational policy research. The introduction explains the rationale for the collection and discusses the themes and articles in the collection. We conclude with a call for researchers, policy makers, and colleges of education to enhance their capacity in incorporating geographic thinking into educational policy research.}, language = {en}, number = {3}, urldate = {2021-03-07}, journal = {AERA Open}, author = {Mann, Bryan and Saultz, Andrew}, month = jul, year = {2019}, note = {Publisher: SAGE Publications Inc}, keywords = {Lebanon\_event\_2021, \_C:United States USA, \_\_C:filed:1, \_\_C:scheme:1, geographic information systems, geography}, pages = {2332858419869340}, } @article{asim_moving_2019, title = {Moving teachers to {Malawi}’s remote communities: {A} data-driven approach to teacher deployment}, volume = {65}, issn = {0738-0593}, shorttitle = {Moving teachers to {Malawi}’s remote communities}, url = {https://www.sciencedirect.com/science/article/pii/S0738059318300555}, doi = {10.1016/j.ijedudev.2018.12.002}, abstract = {There are severe geographical disparities in pupil-teacher ratios (PTR) across Malawi, with most teachers concentrated near commercial centers and in rural schools with better amenities. Most of the variation in PTR is concentrated in small sub-district areas, suggesting a central role for micro-geographic factors in teacher distribution. Employing administrative data from several government sources, regression analysis reveals that school-level factors identified by teachers as desirable are closely associated with PTR, including access to roads, electricity, and water, and distance to the nearest trading center, suggesting a central role for teachers’ interests in PTR variation. Political economy network mapping reveals that teachers leverage informal networks and political patronage to resist placement in remote schools, while administrative officials are unable to stand up to these formal and informal pressures, in part because of a lack of reliable databases and objective criteria for the allocation of teachers. This study curates a systematic database of the physical placement of all teachers in Malawi and links it with data on school facilities and geo-spatial coordinates of commercial centers. The study develops a consistent and objective measure of school remoteness, which can be applied to develop policies to create rules for equitable deployments and targeting of incentives. Growing awareness of disparities in PTRs among district education officials is already showing promising improvements in targeting of new teachers. Simulation results of planned policy applications show significant potential impacts of fiscally-neutral approaches to targeted deployments of new cohorts, as well as retention of teachers through data-calibrated incentives.}, language = {en}, urldate = {2021-03-07}, journal = {International Journal of Educational Development}, author = {Asim, Salman and Chimombo, Joseph and Chugunov, Dmitry and Gera, Ravinder}, month = mar, year = {2019}, keywords = {Data-driven model, Deployments, Lebanon\_event\_2021, Malawi, Political economy, Schools, Teachers, \_C:Brazil BRA, \_C:Gambia GMB, \_C:India IND, \_C:Indonesia IDN, \_C:Italy ITA, \_C:Malawi MWI, \_C:Mozambique MOZ, \_C:Norway NOR, \_C:Philippines PHL, \_C:Singapore SGP, \_\_C:filed:1, \_\_C:scheme:1}, pages = {26--43}, } @misc{namit_digital_2019, title = {Digital {School} {Census} in 10 {Weeks}? {How} it was done in {Sierra} {Leone}}, shorttitle = {Digital {School} {Census} in 10 {Weeks}?}, url = {https://blogs.worldbank.org/education/digital-school-census-10-weeks-how-it-was-done-sierra-leone}, abstract = {Note: This blog is specifically about Sierra Leone’s successful transition to a digital school census but has broader implications for other countries who plan to adopt digital tools at a wider scale to collect data and monitor education and healthcare facilities in their countries. In April of last year, the new Minister of Finance of Sierra Leone approached the World Bank with a strong commitment to prioritize education and an intriguing request.}, language = {en}, urldate = {2020-08-14}, journal = {World Bank Blogs}, author = {Namit, K. and Thi Mai, T.}, month = feb, year = {2019}, note = {KerkoCite.ItemAlsoKnownAs: 2129771:8RLHW9WQ 2129771:QWF5JY27 2129771:WNSMNZQR 2339240:PP5768J6 2339240:TGTJZWCC 2405685:EPMH5GMB 2405685:EY9VEBRN 2405685:KXNLTUXL 2405685:NUSFD2XU 2405685:PLIT3YBG 2405685:TIEFATQQ 4803016:S4RTD2LL}, keywords = {\_C:Sierra Leone SLE}, } @inproceedings{blair-freese_geo-referenced_2019, address = {Seattle, WA, USA}, title = {Geo-{Referenced} {Infrastructure} and {Demographic} {Data} for {Development}}, isbn = {978-1-72811-780-5}, url = {https://ieeexplore.ieee.org/document/9033027/}, doi = {10.1109/GHTC46095.2019.9033027}, language = {en}, urldate = {2021-03-18}, booktitle = {2019 {IEEE} {Global} {Humanitarian} {Technology} {Conference} ({GHTC})}, publisher = {IEEE}, author = {Blair-Freese, Io}, month = oct, year = {2019}, pages = {1--1}, } @misc{project_connect_ai_2019, title = {{AI} assisted school mapping and discussion}, url = {http://devseed.com/unicef-school-docs/}, language = {en}, urldate = {2021-03-14}, journal = {Project Connect}, author = {Project Connect}, year = {2019}, } @article{smith_mapping_2019, title = {Mapping schools' {NAPLAN} results: a spatial inequality of school outcomes in {Australia}}, volume = {57}, issn = {1745-5863, 1745-5871}, shorttitle = {Mapping schools' {NAPLAN} results}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1745-5871.12317}, doi = {10.1111/1745-5871.12317}, abstract = {This article identifies spatial dimensions of educational outcomes using maps of the 2016 Grade 5 reading results for Australia’s National Assessment Program–Literacy and Numeracy for all Australian schools. A geographical information system (GIS) was used to overlay schools’ results onto suburbs’ advantage or disadvantage to visualise spatial patterns. We then examined the extent to which school results “cluster” in socio-economically advantaged and disadvantaged suburbs and considered the consistency of spatial patterns for results across major cities. That work illustrates both how GIS can foreground educational inequality and how “the spatial” is more than corollary for student socio-economic status. Results show substantial differences between urban and remote areas and towns of different size. Maps of cities visualise spatial “clustering” patterns of school results, with most schools in advantaged suburbs having high results and almost no schools in disadvantaged suburbs having high results. Educational outcomes strongly align to local sociodemographic characteristics, and parallel host communities’ levels of advantage or disadvantage. Differences between public and private schools are less significant than within-sector differences for schools in advantaged or disadvantaged locales. Patterns in all cities are consistent—schools in advantaged suburbs predominantly have high results, whereas non-government schools generally perform better than government schools in disadvantaged suburbs. Most concerning is the persistent and increasing trajectory of results in advantaged, and more so in disadvantaged suburbs, of all cities since the first National Assessment Program–Literacy and Numeracy in 2008. Ameliorating spatial inequality between primary schools is one of the greatest challenges for Australians.}, language = {en}, number = {2}, urldate = {2021-03-07}, journal = {Geographical Research}, author = {Smith, Crichton and Parr, Nick and Muhidin, Salut}, month = may, year = {2019}, keywords = {Lebanon\_event\_2021, \_C:Australia AUS, \_C:United Kingdom GBR, \_\_C:filed:1, \_\_C:scheme:1}, pages = {133--150}, } @article{alegana_national_2018, title = {National and sub-national variation in patterns of febrile case management in sub-{Saharan} {Africa}}, volume = {9}, copyright = {2018 The Author(s)}, issn = {2041-1723}, url = {https://www.nature.com/articles/s41467-018-07536-9}, doi = {10.1038/s41467-018-07536-9}, abstract = {Given national healthcare coverage gaps, understanding treatment-seeking behaviour for fever is crucial for the management of childhood illness and to reduce deaths. Here, we conduct a modelling study triangulating household survey data for fever in children under the age of five years with georeferenced public health facility databases (n = 86,442 facilities) in 29 countries across sub-Saharan Africa, to estimate the probability of seeking treatment for fever at public facilities. A Bayesian item response theory framework is used to estimate this probability based on reported fever episodes, treatment choice, residence, and estimated travel-time to the nearest public-sector health facility. Findings show inter- and intra-country variation, with the likelihood of seeking treatment for fever less than 50\% in 16 countries. Results highlight the need to invest in public healthcare and related databases. The variation in public sector use illustrates the need to include such modelling in future infectious disease burden estimation.}, language = {en}, number = {1}, urldate = {2021-03-18}, journal = {Nature Communications}, author = {Alegana, Victor A. and Maina, Joseph and Ouma, Paul O. and Macharia, Peter M. and Wright, Jim and Atkinson, Peter M. and Okiro, Emelda A. and Snow, Robert W. and Tatem, Andrew J.}, month = nov, year = {2018}, note = {Number: 1 Publisher: Nature Publishing Group}, pages = {4994}, } @article{juran_geospatial_2018, title = {Geospatial mapping of access to timely essential surgery in sub-{Saharan} {Africa}}, volume = {3}, copyright = {© Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.}, issn = {2059-7908}, url = {https://gh.bmj.com/content/3/4/e000875}, doi = {10.1136/bmjgh-2018-000875}, abstract = {{\textless}h3{\textgreater}Introduction{\textless}/h3{\textgreater} {\textless}p{\textgreater}Despite an estimated one-third of the global burden of disease being surgical, only limited estimates of accessibility to surgical treatment in sub-Saharan Africa exist and these remain spatially undefined. Geographical metrics of access to major hospitals were estimated based on travel time. Estimates were then used to assess need for surgery at country level.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Methods{\textless}/h3{\textgreater} {\textless}p{\textgreater}Major district and regional hospitals were assumed to have capability to perform bellwether procedures. Geographical locations of hospitals in relation to the population in the 47 sub-Saharan countries were combined with spatial ancillary data on roads, elevation, land use or land cover to estimate travel-time metrics of 30 min, 1 hour and 2 hours. Hospital catchment was defined as population residing in areas less than 2 hours of travel time to the next major hospital. Travel-time metrics were combined with fine-scale population maps to define burden of surgery at hospital catchment level.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Results{\textless}/h3{\textgreater} {\textless}p{\textgreater}Overall, the majority of the population (92.5\%) in sub-Saharan Africa reside in areas within 2 hours of a major hospital catchment defined based on spatially defined travel times. The burden of surgery in all-age population was 257.8 million to 294.7 million people and was highest in high-population density countries and lowest in sparsely populated or smaller countries. The estimated burden in children \<15 years was 115.3 million to 131.8 million and had similar spatial distribution to the all-age pattern.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Conclusion{\textless}/h3{\textgreater} {\textless}p{\textgreater}The study provides an assessment of accessibility and burden of surgical disease in sub-Saharan Africa. Yet given the optimistic assumption of adequare surgical capability of major hospitals, the true burden of surgical disease is expected to be much greater. In-depth health facility assessments are needed to define infrastructure, personnel and medicine supply for delivering timely and safe affordable surgery to further inform the analysis.{\textless}/p{\textgreater}}, language = {en}, number = {4}, urldate = {2021-03-18}, journal = {BMJ Global Health}, author = {Juran, Sabrina and Broer, P. Niclas and Klug, Stefanie J. and Snow, Rachel C. and Okiro, Emelda A. and Ouma, Paul O. and Snow, Robert W. and Tatem, Andrew J. and Meara, John G. and Alegana, Victor A.}, month = aug, year = {2018}, pmid = {30147944}, note = {Publisher: BMJ Specialist Journals Section: Research}, pages = {e000875}, } @article{wardrop_spatially_2018, title = {Spatially disaggregated population estimates in the absence of national population and housing census data}, volume = {115}, copyright = {Copyright © 2018 the Author(s). Published by PNAS.. http://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).}, issn = {0027-8424, 1091-6490}, url = {https://www.pnas.org/content/115/14/3529}, doi = {10.1073/pnas.1715305115}, abstract = {Population numbers at local levels are fundamental data for many applications, including the delivery and planning of services, election preparation, and response to disasters. In resource-poor settings, recent and reliable demographic data at subnational scales can often be lacking. National population and housing census data can be outdated, inaccurate, or missing key groups or areas, while registry data are generally lacking or incomplete. Moreover, at local scales accurate boundary data are often limited, and high rates of migration and urban growth make existing data quickly outdated. Here we review past and ongoing work aimed at producing spatially disaggregated local-scale population estimates, and discuss how new technologies are now enabling robust and cost-effective solutions. Recent advances in the availability of detailed satellite imagery, geopositioning tools for field surveys, statistical methods, and computational power are enabling the development and application of approaches that can estimate population distributions at fine spatial scales across entire countries in the absence of census data. We outline the potential of such approaches as well as their limitations, emphasizing the political and operational hurdles for acceptance and sustainable implementation of new approaches, and the continued importance of traditional sources of national statistical data.}, language = {en}, number = {14}, urldate = {2021-03-18}, journal = {Proceedings of the National Academy of Sciences}, author = {Wardrop, N. A. and Jochem, W. C. and Bird, T. J. and Chamberlain, H. R. and Clarke, D. and Kerr, D. and Bengtsson, L. and Juran, S. and Seaman, V. and Tatem, A. J.}, month = apr, year = {2018}, pmid = {29555739}, note = {Publisher: National Academy of Sciences Section: Perspective}, keywords = {census, geostatistics, population, remote sensing, surveys}, pages = {3529--3537}, } @techreport{theunynck_distance_2018, title = {Distance to {School} and the educational consequences}, institution = {World Bank}, author = {Theunynck, Serge}, year = {2018}, } @article{yoon_thinking_2018, title = {Thinking {Critically} in {Space}: {Toward} a {Mixed}-{Methods} {Geospatial} {Approach} to {Education} {Policy} {Analysis}}, volume = {47}, issn = {0013-189X, 1935-102X}, shorttitle = {Thinking {Critically} in {Space}}, url = {http://journals.sagepub.com/doi/10.3102/0013189X17737284}, doi = {10.3102/0013189X17737284}, language = {en}, number = {1}, urldate = {2021-03-07}, journal = {Educational Researcher}, author = {Yoon, Ee-Seul and Lubienski, Christopher}, month = jan, year = {2018}, keywords = {Lebanon\_event\_2021, \_C:Canada CAN, \_C:China CHN, \_C:New Zealand NZL, \_C:Russian Federation RUS, \_C:United States USA, \_\_C:filed:1, \_\_C:scheme:1}, pages = {53--61}, } @techreport{asim_moving_2017, type = {World {Bank} {Policy} {Report}}, title = {Moving teachers to {Malawi}’s remote communities: {A} data-driven approach to teacher deployment}, shorttitle = {Moving teachers to {Malawi}’s remote communities}, abstract = {There are severe geographical disparities in pupil-teacher ratios (PTR) across Malawi, with most teachers concentrated near commercial centers and in rural schools with better amenities. Most of the variation in PTR is concentrated in small sub-district areas, suggesting a central role for micro-geographic factors in teacher distribution. Employing administrative data from several government sources, regression analysis reveals that school-level factors identified by teachers as desirable are closely associated with PTR, including access to roads, electricity, and water, and distance to the nearest trading center, suggesting a central role for teachers’ interests in PTR variation. Political economy network mapping reveals that teachers leverage informal networks and political patronage to resist placement in remote schools, while administrative officials are unable to stand up to these formal and informal pressures, in part because of a lack of reliable databases and objective criteria for the allocation of teachers. This study curates a systematic database of the physical placement of all teachers in Malawi and links it with data on school facilities and geo-spatial coordinates of commercial centers. The study develops a consistent and objective measure of school remoteness, which can be applied to develop policies to create rules for equitable deployments and targeting of incentives. Growing awareness of disparities in PTRs among district education officials is already showing promising improvements in targeting of new teachers. Simulation results of planned policy applications show significant potential impacts of fiscally-neutral approaches to targeted deployments of new cohorts, as well as retention of teachers through data-calibrated incentives.}, language = {en}, urldate = {2021-03-07}, author = {Asim, Salman and Chimombo, Joseph and Chugunov, Dmitry and Gera, Ravinder}, month = nov, year = {2017}, keywords = {Data-driven model, Deployments, Lebanon\_event\_2021, Malawi, Political economy, Schools, Teachers, \_C:Brazil BRA, \_C:Gambia GMB, \_C:Ghana GHA, \_C:India IND, \_C:Indonesia IDN, \_C:Italy ITA, \_C:Malawi MWI, \_C:Mozambique MOZ, \_C:Philippines PHL, \_C:Singapore SGP, \_\_C:filed:1, \_\_C:scheme:1}, } @article{lubienski_geo-spatial_2017, title = {Geo-spatial analyses in education research: the critical challenge and methodological possibilities}, volume = {55}, copyright = {© 2016 Institute of Australian Geographers}, issn = {1745-5871}, shorttitle = {Geo-spatial analyses in education research}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1745-5871.12188}, doi = {10.1111/1745-5871.12188}, abstract = {The usefulness of spatial perspectives in education research is well known, particularly in fields such as school choice that are operationalised in multiple institutional, demographic, and local geographies. But the modes of spatial inquiry, even as they can potentially lend themselves to integrated research strategies, tend to be fragmented and isolated, failing to take into account multiple dimensions of contextual factors. Our purpose is to provide critical deliberations on geo-spatial methods in school choice research and suggest an integrative approach to enhance research on school choice from a geographic perspective. This paper first demonstrates the linkage of spatial approaches to school choice, and then surveys geo-spatial research as typically leveraged on this issue. We argue that there are inherent limitations to the typical conceptions of space in geo-spatial analyses and discuss two of the major challenges to these conceptions as provided by critical theorists and geographers. But we also point out that these challenges suggest alternatives that themselves have serious shortcomings. The concluding discussion sets out some of the possibilities of a more integrated approach to spatial inquiry in education research, and school choice more specifically.}, language = {en}, number = {1}, urldate = {2021-03-07}, journal = {Geographical Research}, author = {Lubienski, Christopher and Lee, Jin}, year = {2017}, note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/1745-5871.12188 KerkoCite.ItemAlsoKnownAs: 2129771:555XYA4A 2129771:M3F439Y7}, keywords = {Lebanon\_event\_2021, \_C:Australia AUS, \_C:Canada CAN, \_C:Chile CHL, \_C:Georgia GEO, \_C:Ireland IRL, \_C:Japan JPN, \_C:New Zealand NZL, \_C:Sweden SWE, \_C:United Kingdom GBR, \_C:United States USA, \_\_C:filed:1, \_\_C:scheme:1, access, education policy, equity, school choice, spatial inquiry}, pages = {89--99}, } @article{stanley_relationship_2017, title = {The {Relationship} {Between} {School} {Distance} {And} {Academic} {Achievement} {Of} {Primary} {School} {Pupils} {In} {Ovia} {North}-{East} {Lga}, {Edo} {State}, {Nigeria}.}, volume = {1}, abstract = {The aim of this study was to investigate the relationship between school distance and academic achievement of primary school pupils in Ovia North-East LGA. As a guide to this study, four research questions as well as three hypotheses were tested the correlational research design was adopted for this study. The population of the study comprised all primary schools in Ovia North-East LGA. There are one hundred and one primary schools out of which twenty schools (20\%) were sampled using the random sampling technique and one hundred teachers were used as sample for the study. The research instrument was a structured questionnaire on relationship between school distance and academic achievement of primary school pupils (S.D.A.A.Q). The reliability of the instrument was established through the use of test re-test method. The collected data were analyzed using simple percentage and Pearson Product Moment Correlation (PPMC) statistic was used in testing the hypotheses at 0.05 level of significance. The instrument was trial tested in Uselu primary school, Egor Local Government Area in Edo State using 20 teachers. The reliability coefficient was 0.86. The findings of the study revealed that: pupils in Ovia North-East LGA covered long distance to school; there is significant relationship between school distance and academic achievement of primary school pupils; there is a relationship between school distance and academic achievement of male primary school pupils While for the female, there is no significant relationship between school distance and academic achievement. It was thus recommended that primary schools should be located in different strategic locations in the Local Government Area to reduce the distance covered by the pupils. Alternatively, school buses should be made available to convey the pupils to and from schools to overcome the problem of late coming and tiredness on the part of the pupils thereby by enhancing their academic achievement. Parents and guardians should locate schools closer to their place of residence so as to prevent the pupils from walking long distance to schools which may reduce the level of their academic achievement. Furthermore, the Government should ensure that the available primary schools are made conducive for effective learning so as to improve the academic achievement of the pupils. Again, pupils should be motivated as well as reinforced on regular basis to encourage them to attend school regularly.}, language = {en}, number = {5}, author = {Stanley, Udoka and Emmanuel, Nelly and Igboh, Benedict}, year = {2017}, pages = {9}, } @inproceedings{yani_obstacles_2016, title = {The {Obstacles} of {Geographical} {Information} {System} ({GIS}) {Development}: {A} {Study} of {Teachers}' {Distribution} in {Sukabumi}, {Indonesia}}, isbn = {978-94-6252-279-4}, shorttitle = {The {Obstacles} of {Geographical} {Information} {System} ({GIS}) {Development}}, url = {https://www.atlantis-press.com/proceedings/icemal-16/25867339}, doi = {10.2991/icemal-16.2016.15}, abstract = {Geographical Information System (GIS) is an application used to process data in form of thematic maps which is arranged overlapping to produce informations needed by the user. Employing descriptive method, this study indicated that basically the GIS deve}, language = {en}, urldate = {2021-03-07}, publisher = {Atlantis Press}, author = {Yani, Ahmad and Rosita, Rosita}, month = aug, year = {2016}, note = {ISSN: 2352-5428}, keywords = {Lebanon\_event\_2021, \_C:Indonesia IDN, \_C:Kenya KEN, \_\_C:filed:1, \_\_C:scheme:1}, pages = {63--66}, } @techreport{world_bank_teacher_2016, title = {Teacher {Management} 2.0: {Improving} {Teacher} {Deployment} in {Malawi}}, url = {http://documents1.worldbank.org/curated/en/780321468194950346/pdf/104252-BRI-P155972-PUBLIC-ADD-SERIES-WB-TeacherMgmt-brief-final-web.pdf}, author = {{World Bank}}, year = {2016}, note = {KerkoCite.ItemAlsoKnownAs: 2129771:F2I5PI5L}, keywords = {\_C:Malawi MWI, \_\_C:filed:1, \_\_C:scheme:1}, pages = {4}, } @article{easton_childrens_2015, title = {Children's travel to school—the interaction of individual, neighbourhood and school factors}, volume = {44}, issn = {0967-070X}, url = {https://www.sciencedirect.com/science/article/pii/S0967070X15300196}, doi = {10.1016/j.tranpol.2015.05.023}, abstract = {The increase in average distance from home to secondary school over recent decades has been accompanied by a significant growth in the proportion of pupils travelling to school by motorized means as opposed to walking or cycling. More recently this switch in travel mode has received considerable attention as declining levels of physical activity, growing car dependence and the childhood obesity “crisis” have pushed concerns about the health of future generations up the public health agenda, particularly in the U.S., but also in the UK and Europe. This has led to a proliferation of international studies researching a variety of individual, school and spatial characteristics associated with children's active travel to school which has been targeted by some governments as a potential silver bullet to reverse the trend. However, to date national pupil census data, which comprises annual data on all English pupils, including a mode of travel to school variable, has been under-utilised in the analysis of how pupils commute to school. Furthermore, methodologically, the grouped nature of the data with pupils clustered within both schools and residential neighbourhoods has often been ignored - an omission which can have considerable consequences for the statistical estimation of the model. The research presented here seeks to address both of these points by analysing pupil census data on all 26,709 secondary pupils (aged 11–16) who attended schools in Sheffield, UK during the 2009–10 school year. Individual pupil data is grouped within school, and neighbourhood, within a cross-classified multilevel model of active versus motorised modes of commuting to school. The results support the findings of other research that distance to school is key, but suggest that sociospatial clustering within neighbourhoods and schools is also critical. A further finding is that distance to school varies significantly by ethnicity, with white British pupils travelling the shortest distance of all ethnic groups. The implications of these findings for education and transport policy are discussed.}, language = {en}, urldate = {2021-03-25}, journal = {Transport Policy}, author = {Easton, Sue and Ferrari, Ed}, month = nov, year = {2015}, keywords = {Active transport, Mode of travel, Motorised transport, Multilevel model, Pupils, Secondary, Sociospatial, Travel to school}, pages = {9--18}, } @article{ozoglu_mobility-related_2015, title = {Mobility-{Related} {Teacher} {Turnover} and the {Unequal} {Distribution} of {Experienced} {Teachers} in {Turkey}}, volume = {15}, copyright = {Copyright (c) 2015 Educational Sciences: Theory \& Practice}, issn = {2148-7561}, url = {https://jestp.com/~jestpcom/index.php/estp/article/view/650}, doi = {10.12738/estp.2015.4.2619}, abstract = {This study investigates the issue of mobility-related teacher turnover in Turkey through both quantitative and qualitative methods. The quantitative findings derived from descriptive and correlational analyses of countrywide teacher-assignment and transfer data indicate that a high rate of mobility-related turnover is observed in the less- developed, eastern provinces of Turkey. The qualitative findings derived from semi-structured, in-depth interviews with school principals suggest that the factors contributing to the issue of mobility-related teacher turnover experienced in eastern Turkey are largely related to the socio-economic and geographic conditions of the region. The qualitative findings further suggest that this turnover issue may have far-reaching negative consequences across school-wide performances and processes. Participants consistently reported that the issue of teacher turnover had negative impacts on student performance, teacher motivation and commitment, instructional planning, administrative processes, and school climate. The study concludes by exploring possible policy implications for alleviating the issue of mobility-related teacher turnover as experienced in the less-developed, eastern regions of Turkey.}, language = {en}, number = {4}, urldate = {2021-03-07}, journal = {Educational Sciences: Theory \& Practice}, author = {Özoğlu, Murat}, month = aug, year = {2015}, note = {Number: 4}, keywords = {Lebanon\_event\_2021, Turkey, \_C:Georgia GEO, \_C:Italy ITA, \_C:Turkey TUR, \_C:United States USA, \_C:Uruguay URY, \_\_C:filed:1, \_\_C:scheme:1}, } @techreport{wall_quantitative_2015, title = {Quantitative {Analysis} of the {Distribution} of {Professional} {Staff} with {Advanced} {Degrees} in 2014 {Missouri} {Public} {School} {Districts} by {Student} {Ethnicity} and {Socioeconomic} {Status}}, url = {https://www.nwmissouri.edu/accreditation/NCATE/pdf/FocusVisit/Rejoinder/Exhibits/R.4.5.9%20Action%20Research%20Paper.pdf}, urldate = {2021-03-07}, author = {Wall, T J}, year = {2015}, keywords = {Lebanon\_event\_2021, \_C:Finland FIN, \_C:Norway NOR, \_C:Sweden SWE, \_C:United States USA, \_\_C:filed:1, \_\_C:scheme:1}, } @article{schultz_inequitable_2014, title = {Inequitable {Dispersion}: {Mapping} the {Distribution} of {Highly} {Qualified} {Teachers} in {St}. {Louis} {Metropolitan} {Elementary} {Schools}}, volume = {22}, issn = {EISSN-1068 2341}, shorttitle = {Inequitable {Dispersion}}, url = {https://eric.ed.gov/?id=EJ1050052}, abstract = {The No Child Left Behind (NCLB) Act of 2001 required all schools, including those located in historically disadvantaged areas, to employ highly qualified teachers. Schools in areas with higher levels of poverty and students of color have historically employed a higher percentage of less qualified teachers (Clotfelter, Ladd, \& Vidgor, 2005, 2006; Hill \& Lubienski, 2007; Lankford, Loeb, \& Wyckoff, 2002). This study examines the distribution, location, and exceptions to highly qualified teachers in St. Louis metropolitan elementary schools. Using Geographic Information Systems (GIS), this study demonstrates how the distribution of highly qualified teachers remains relevant to urban education policy discussions.}, language = {en}, number = {90}, urldate = {2021-03-07}, journal = {Education Policy Analysis Archives}, author = {Schultz, Lyndsie Marie}, month = sep, year = {2014}, note = {Publisher: Colleges of Education at Arizona State University and the University of South Florida}, keywords = {Educational Legislation, Elementary School Teachers, Elementary Schools, Federal Legislation, Lebanon\_event\_2021, Minority Group Students, Poverty, Predictor Variables, Socioeconomic Status, Teacher Certification, Teacher Effectiveness, Teacher Qualifications, Urban Education, Urban Schools, \_C:Argentina ARG, \_C:Bolivia BOL, \_C:Brazil BRA, \_C:Chad TCD, \_C:Chile CHL, \_C:China CHN, \_C:Colombia COL, \_C:India IND, \_C:Mexico MEX, \_C:Peru PER, \_C:Portugal PRT, \_C:Spain ESP, \_C:United States USA, \_C:Venezuela VEN, \_\_C:filed:1, \_\_C:scheme:1, ⛔ No DOI found}, } @techreport{terrier_matching_2014, type = {{MiP} {Country} {Profile}}, title = {Matching practices of teachers to {Schools} – {France} – {Matching} in {Practice}}, url = {https://www.matching-in-practice.eu/matching-practices-of-teachers-to-schools-france/}, language = {en-US}, number = {20}, urldate = {2021-03-07}, author = {Terrier, Camille}, month = jun, year = {2014}, keywords = {Lebanon\_event\_2021, \_C:Belgium BEL, \_C:Estonia EST, \_C:Finland FIN, \_C:France FRA, \_C:Germany DEU, \_C:Hungary HUN, \_C:Ireland IRL, \_C:Italy ITA, \_C:Spain ESP, \_C:Ukraine UKR, \_C:United States USA, \_\_C:filed:1, \_\_C:scheme:1}, } @article{mulaku_gis_2013, title = {{GIS} in {Education} {Planning}: {The} {Kenyan} {School} {Mapping} {Project}}, volume = {43}, copyright = {© 2011 Maney Publishing}, shorttitle = {{GIS} in {Education} {Planning}}, url = {https://www.tandfonline.com/doi/pdf/10.1179/003962611X13117748892155}, doi = {10.1179/003962611X13117748892155}, abstract = {School mapping consists of the building of geospatial databases of educational, demographic and socioeconomic data for educational institutions in order to support educational planning and decision...}, language = {EN}, number = {323}, urldate = {2020-11-08}, journal = {Survey Review}, author = {Mulaku, G. C. and Nyadimo, E.}, month = jul, year = {2013}, note = {Publisher: Taylor \& Francis KerkoCite.ItemAlsoKnownAs: 10.1179/003962611X13117748892155 2129771:D7LGKELW 2129771:EG8QF9R9}, keywords = {\_C:Ethiopia ETH, \_C:France FRA, \_C:India IND, \_C:Ireland IRL, \_C:Kenya KEN, \_C:State of Palestine PSE, \_C:Thailand THA, \_C:Uganda UGA, \_\_C:filed:1, \_\_C:scheme:1}, } @book{kennedy_introducing_2013, title = {Introducing {Geographic} {Information} {Systems} with {ArcGIS}: {A} {Workbook} {Approach} to {Learning} {GIS}}, isbn = {978-1-118-33034-0}, shorttitle = {Introducing {Geographic} {Information} {Systems} with {ArcGIS}}, abstract = {An integrated approach that combines essential GIS background with a practical workbook on applying the principles in ArcGIS 10.0 and 10.1 Introducing Geographic Information Systems with ArcGISintegrates a broad introduction to GIS with a software-specific workbook for Esri's ArcGIS. Where most courses make do using two separate texts, one covering GIS and another the software, this book enables students and instructors to use a single text with an integrated approach covering both in one volume with a common vocabulary and instructional style. This revised edition focuses on the latest software updates—ArcGIS 10.0 and 10.1. In addition to its already successful coverage, the book allows students to experience publishing maps on the Internet through new exercises, and introduces the idea of programming in the language Esri has chosen for applications (i.e., Python). A DVD is packaged with the book, as in prior editions, containing data for working out all of the exercises. This complete, user-friendly coursebook: Is updated for the latest ArcGIS releases—ArcGIS 10.0 and 10.1 Introduces the central concepts of GIS and topics needed to understand spatial information analysis Provides a considerable ability to operate important tools in ArcGIS Demonstrates new capabilities of ArcGIS 10.0 and 10.1 Provides a basis for the advanced study of GIS and the study of the newly emerging field of GIScience Introducing Geographic Information Systems with ArcGIS, Third Edition is the ideal guide for undergraduate students taking courses such as Introduction to GIS, Fundamentals of GIS, and Introduction to ArcGIS Desktop. It is also an important guide for professionals looking to update their skills for ArcGIS 10.0 and 10.1.}, language = {en}, publisher = {John Wiley \& Sons}, author = {Kennedy, Michael D.}, month = mar, year = {2013}, note = {Google-Books-ID: v6WcvrT8jRsC}, keywords = {Science / Earth Sciences / Geography}, } @article{roulston_gis_2013, title = {{GIS} in {Northern} {Ireland} secondary schools: mapping where we are now}, volume = {22}, issn = {1038-2046}, shorttitle = {{GIS} in {Northern} {Ireland} secondary schools}, url = {https://doi.org/10.1080/10382046.2012.759437}, doi = {10.1080/10382046.2012.759437}, abstract = {A number of studies suggest that integrating Geographical Information Systems (GIS) into Geography teaching in schools has been and is challenging, and it seems that much of the early promise for the technology supporting learning in Geography has not been realised. This paper examines the progress made in Northern Ireland in implementing GIS in secondary schools. The deployment of a centrally procured entry-level GIS, in the context of a programme of centralised provision of information and communication technology (ICT) services to all schools, is examined and the results of an online survey of 85 Geography teachers provides an insight into how effective that provision has been. This combination of a regional strategy on GIS, curriculum changes and increased access to computers seems to have ensured that GIS is being used in many Geography classrooms. There is evidence that a range of GI systems are being used in schools and in a number of different ways, but mostly for teacher rather than for pupil use at present. Teachers expressed a need for coordinated training in order to make full use of the hardware and software available.}, number = {1}, urldate = {2021-03-07}, journal = {International Research in Geographical and Environmental Education}, author = {Roulston, Stephen}, month = feb, year = {2013}, note = {Publisher: Routledge \_eprint: https://doi.org/10.1080/10382046.2012.759437}, keywords = {C2k, Geographic Information Systems, Northern Ireland, \_C:Australia AUS, \_C:Germany DEU, \_C:Ireland IRL, \_C:Singapore SGP, \_C:Turkey TUR, \_C:United Kingdom GBR, \_C:United States USA, \_\_C:filed:1, \_\_C:scheme:1, geography education}, pages = {41--56}, } @incollection{roosaare_using_2012, title = {Using {GIS} and spatial modelling to support school network planning in {Estonia}}, isbn = {978-1-78100-712-9}, abstract = {A spatial decision support system based on geo-informatics and spatial modelling tools has been used to provide a reorganization plan of school networks, The study was undertaken in collaboration with the Ministry of Education and Research of Estonia}, author = {Roosaare, Jüri and Sepp, Edgar}, month = jan, year = {2012}, doi = {10.4337/9781781007129.00011}, keywords = {\_C:Austria AUT, \_C:Estonia EST, \_C:France FRA, \_C:Germany DEU, \_C:Portugal PRT, \_C:Russian Federation RUS, \_C:State of Palestine PSE, \_C:United States USA, \_\_C:filed:1, \_\_C:scheme:1}, pages = {95--108}, } @book{crampton_mapping_2011, title = {Mapping: {A} {Critical} {Introduction} to {Cartography} and {GIS}}, isbn = {978-1-4443-5673-1}, shorttitle = {Mapping}, abstract = {Mapping: A Critical Introduction to Cartography and GIS is an introduction to the critical issues surrounding mapping and Geographic Information Systems (GIS) across a wide range of disciplines for the non-specialist reader. Examines the key influences Geographic Information Systems (GIS) and cartography have on the study of geography and other related disciplines Represents the first in-depth summary of the “new cartography” that has appeared since the early 1990s Provides an explanation of what this new critical cartography is, why it is important, and how it is relevant to a broad, interdisciplinary set of readers Presents theoretical discussion supplemented with real-world case studies Brings together both a technical understanding of GIS and mapping as well as sensitivity to the importance of theory}, language = {en}, publisher = {John Wiley \& Sons}, author = {Crampton, Jeremy W.}, month = sep, year = {2011}, note = {Google-Books-ID: Lw08zMsCTeEC}, keywords = {Science / Earth Sciences / Geography}, } @techreport{vuri_effect_2007, title = {The effect of availability and distance from school on children’s time allocation in {Ghana} and {Guatemala}}, institution = {https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.909.7963\&rep=rep1\&type=pdf}, author = {Vuri, Daniela}, year = {2007}, } @techreport{international_development_center_of_japan_school_2005, title = {School {Mapping} and {Micro}-planning in {Primary} {Education} ({Tanzania})}, url = {https://openjicareport.jica.go.jp/pdf/11798204_01.pdf}, urldate = {2021-01-30}, author = {{International Development Center of Japan}}, year = {2005}, note = {KerkoCite.ItemAlsoKnownAs: 2129771:THDCVZTZ}, keywords = {\_\_C:filed:1, \_\_C:scheme:1}, } @article{galabawa_impact_2002, title = {The impact of school mapping in the development of education in {Tanzania}: an assessment of the experiences of six districts}, volume = {25}, issn = {0149-7189}, shorttitle = {The impact of school mapping in the development of education in {Tanzania}}, url = {http://www.sciencedirect.com/science/article/pii/S0149718901000465}, doi = {10.1016/S0149-7189(01)00046-5}, abstract = {In this study the authors have looked at the impact of school mapping in the development of education in Tanzania. The study examined the experiences of six districts where school mapping exercises were carried out. The key question that guided the study is what happened after school mapping. Through a combination of instruments and/or techniques—interviews, questionnaires, focus group discussion, and document analysis, the study found that school mapping impacted in varying degrees positively on the development of education in the districts in terms of increased enrollment and attendance, decreased incidents of dropping out, improved information for decision making, and enhanced capacities of field actors to plan and take action. The authors conclusively argued that for the benefits of school mapping to be maximized and sustained, it should not be a one shot activity for data collection purposes only. Rather, it should be an on-going process of assessment, analysis, and action.}, language = {en}, number = {1}, urldate = {2021-01-30}, journal = {Evaluation and Program Planning}, author = {Galabawa, Justinian C. J. and Agu, Augustine Obeleagu and Miyazawa, Ichiro}, month = feb, year = {2002}, note = {KerkoCite.ItemAlsoKnownAs: 10.1016/S0149-7189(01)00046-5 2129771:5DICP3CA}, keywords = {\_C:Central African Republic CAF, \_C:France FRA, \_C:Ghana GHA, \_C:India IND, \_C:Japan JPN, \_C:Kenya KEN, \_C:Tanzania TZA, \_C:Uganda UGA, \_\_C:filed:1, \_\_C:scheme:1}, pages = {23--33}, } @book{attfield_improving_2002, address = {International Institute for Educational Planning}, title = {Improving micro-planningin education througha {Geographical} {Information} {SystemStudies} on {Ethiopia} and {Palestine}}, author = {Attfield, Ian and Tamiru, Mathewos and Parolin, Bruno and De Grauwe, Anton}, year = {2002}, note = {KerkoCite.ItemAlsoKnownAs: 2129771:ALELB33F}, keywords = {\_C:Argentina ARG, \_C:Australia AUS, \_C:Cambodia KHM, \_C:Chile CHL, \_C:Denmark DNK, \_C:Egypt EGY, \_C:Eritrea ERI, \_C:Ethiopia ETH, \_C:Finland FIN, \_C:France FRA, \_C:Germany DEU, \_C:Iceland ISL, \_C:India IND, \_C:Ireland IRL, \_C:Israel ISR, \_C:Italy ITA, \_C:Japan JPN, \_C:Jordan JOR, \_C:Malaysia MYS, \_C:Namibia NAM, \_C:Nepal NPL, \_C:Norway NOR, \_C:South Africa ZAF, \_C:State of Palestine PSE, \_C:Sweden SWE, \_C:Switzerland CHE, \_C:Tunisia TUN, \_C:United Kingdom GBR, \_C:United States USA, \_\_C:filed:1, \_\_C:scheme:1}, } @book{hallak_planning_1977, address = {Paris}, title = {Planning the {Location} of {Schools}: {An} {Instrument} of {Educational} {Policy}}, publisher = {International Institute for Educational Planning}, author = {Hallak, Jacques}, year = {1977}, note = {KerkoCite.ItemAlsoKnownAs: 2129771:YZPYBX6L}, keywords = {\_C:Afghanistan AFG, \_C:Albania ALB, \_C:Algeria DZA, \_C:Australia AUS, \_C:Austria AUT, \_C:Bangladesh BGD, \_C:Belgium BEL, \_C:Bolivia BOL, \_C:Brazil BRA, \_C:Brunei Darussalam BRN, \_C:Bulgaria BGR, \_C:Cameroon CMR, \_C:Canada CAN, \_C:Chile CHL, \_C:China CHN, \_C:Colombia COL, \_C:Costa Rica CRI, \_C:Denmark DNK, \_C:Ecuador ECU, \_C:Estonia EST, \_C:Ethiopia ETH, \_C:Finland FIN, \_C:France FRA, \_C:Georgia GEO, \_C:Germany DEU, \_C:Greece GRC, \_C:Honduras HND, \_C:Hungary HUN, \_C:India IND, \_C:Iran IRN, \_C:Iraq IRQ, \_C:Ireland IRL, \_C:Italy ITA, \_C:Ivory Coast CIV, \_C:Japan JPN, \_C:Jordan JOR, \_C:Latvia LVA, \_C:Lebanon LBN, \_C:Lithuania LTU, \_C:Luxembourg LUX, \_C:Mexico MEX, \_C:Morocco MAR, \_C:Nepal NPL, \_C:Netherlands NLD, \_C:New Zealand NZL, \_C:Nicaragua NIC, \_C:Nigeria NGA, \_C:Norway NOR, \_C:Pakistan PAK, \_C:Panama PAN, \_C:Peru PER, \_C:Philippines PHL, \_C:Poland POL, \_C:Russian Federation RUS, \_C:Singapore SGP, \_C:South Africa ZAF, \_C:Spain ESP, \_C:Sri Lanka LKA, \_C:Sudan SDN, \_C:Sweden SWE, \_C:Tanzania TZA, \_C:Thailand THA, \_C:Tunisia TUN, \_C:Turkey TUR, \_C:Uganda UGA, \_C:Ukraine UKR, \_C:United Kingdom GBR, \_C:United States USA, \_C:Uruguay URY, \_C:Venezuela VEN, \_C:Viet Nam VNM, \_\_C:filed:1, \_\_C:scheme:1}, } @techreport{noauthor_final_grid3boundariespaperpdf_nodate, title = {{FINAL}\_GRID3BoundariesPaper.pdf}, } @techreport{noauthor_grid3sleeducationreport_final1pdf_nodate, title = {{GRID3SLEEducationReport}\_Final1.pdf}, } @techreport{noauthor_sle_africell_report_v1-2_dec20pdf_nodate, title = {sle\_africell\_report\_v1-2\_dec20.pdf}, url = {https://www.flowminder.org/media/vg5ov5s5/sle_africell_report_v1-2_dec20.pdf}, urldate = {2021-03-18}, } @article{schaefer_gis_nodate, title = {{GIS} in {Schools}: {Experiences} and {Progress} in {Germany}}, language = {en}, author = {Schaefer, Dirk}, keywords = {\_C:Germany DEU, \_\_C:filed:1, \_\_C:scheme:1, ⛔ No DOI found}, pages = {10}, }