Forestry, Faculty of
Non UBC
Forest Resources Management, Department of
DSpace
Sustainability 16 (22): 9698 (2024)
Cardenas-Ritzert, Orion S. E.
Vogeler, Jody C.
Shah Heydari, Shahriar
Fekety, Patrick A.
Laituri, Melinda
McHale, Melissa R.
Geospatial data play a significant role in the United Nation’s Sustainable Development Goals, particularly through assessments of monitoring indicators. Sustainable Development Goal (SDG) Indicator 11.3.1 assessments utilize land and population geospatial data to monitor urban expansion, and were implemented to enhance inclusive and sustainable urbanization, and capacity for participatory, integrated and sustainable human settlement planning and management in all countries by 2030. Data-limited countries often rely on accessible, wide-coverage geospatial datasets for SDG Indicator 11.3.1 assessments which may have characteristics (e.g., coarse spatial resolution) influential to assessment outcomes. The presented work examines the effect of land use data spatial resolution on SDG Indicator 11.3.1 assessment components including urban area delineation, SDG Indicator 11.3.1 and supporting spatial metrics, spatial patterns of urban land development, and land use change patterns for urbanizing areas in Ethiopia from 2016 to 2020. A comparison was made between a single land use map at the spatial resolution in which it was originally produced, 30 m, and at a majority-resampled spatial resolution comparable to many global coverage datasets, 90 m. Analyses revealed changes in the urban areas identified, observed boundaries of urban areas, and all quantified metrics from 30 m resolution to 90 m resolution, with the decrease in resolution resulting in smaller urban areas being missed and differences in the delineated hinterland areas connected to an urban core. Statistical testing indicated significant differences in SDG Indicator 11.3.1 values, developed land use area per capita, and spatial patterns of urban development between the two spatial resolutions. The relative order of land use conversion types remained similar across both resolutions, with agricultural land experiencing the greatest conversion to developed land, followed by rangeland and forest, although the total area of each conversion type differed. This study illustrates the possible discrepancies in SDG Indicator 11.3.1 and related outputs when utilizing differing resolution datasets and the importance of data characteristic consideration when conducting SDG Indicator 11.3.1 assessments.
Citation: Cardenas-Ritzert, O.S.E.;Vogeler, J.C.; Shah Heydari, S.; Fekety,P.A.; Laituri, M.; McHale, M.R. Effectsof Land Use Data Spatial Resolutionon SDG Indicator 11.3.1 (UrbanExpansion) Assessments: A CaseStudy Across Ethiopia. Sustainability2024, 16, 9698. https://doi.org/10.3390/su16229698Academic Editor: Jinyao LinReceived: 1 October 2024Revised: 31 October 2024Accepted: 2 November 2024Published: 7 November 2024Copyright: © 2024 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).ArticleEffects of Land Use Data Spatial Resolution on SDG Indicator11.3.1 (Urban Expansion) Assessments: A Case StudyAcross EthiopiaOrion S. E. Cardenas-Ritzert 1,* , Jody C. Vogeler 1 , Shahriar Shah Heydari 1 , Patrick A. Fekety 1,Melinda Laituri 2 and Melissa R. McHale 31 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523-1499, USA;jody.vogeler@colostate.edu (J.C.V.); shahriar.shah_heydari@colostate.edu (S.S.H.);patrick.fekety@colostate.edu (P.A.F.)2 Department of Ecosystem Science and Sustainability, Colorado State University,Fort Collins, CO 80523-1476, USA; melinda.laituri@colostate.edu3 Department of Forest Resources Management, University of British Columbia,Vancouver, BC V6T 1Z4, Canada; melissa.mchale@ubc.ca* Correspondence: orion.cardenas-ritzert@colostate.eduAbstract: Geospatial data play a significant role in the United Nation’s Sustainable DevelopmentGoals, particularly through assessments of monitoring indicators. Sustainable Development Goal(SDG) Indicator 11.3.1 assessments utilize land and population geospatial data to monitor urbanexpansion, and were implemented to enhance inclusive and sustainable urbanization, and capacityfor participatory, integrated and sustainable human settlement planning and management in allcountries by 2030. Data-limited countries often rely on accessible, wide-coverage geospatial datasetsfor SDG Indicator 11.3.1 assessments which may have characteristics (e.g., coarse spatial resolution)influential to assessment outcomes. The presented work examines the effect of land use data spatialresolution on SDG Indicator 11.3.1 assessment components including urban area delineation, SDGIndicator 11.3.1 and supporting spatial metrics, spatial patterns of urban land development, andland use change patterns for urbanizing areas in Ethiopia from 2016 to 2020. A comparison wasmade between a single land use map at the spatial resolution in which it was originally produced,30 m, and at a majority-resampled spatial resolution comparable to many global coverage datasets,90 m. Analyses revealed changes in the urban areas identified, observed boundaries of urban areas,and all quantified metrics from 30 m resolution to 90 m resolution, with the decrease in resolutionresulting in smaller urban areas being missed and differences in the delineated hinterland areasconnected to an urban core. Statistical testing indicated significant differences in SDG Indicator 11.3.1values, developed land use area per capita, and spatial patterns of urban development between thetwo spatial resolutions. The relative order of land use conversion types remained similar acrossboth resolutions, with agricultural land experiencing the greatest conversion to developed land,followed by rangeland and forest, although the total area of each conversion type differed. Thisstudy illustrates the possible discrepancies in SDG Indicator 11.3.1 and related outputs when utilizingdiffering resolution datasets and the importance of data characteristic consideration when conductingSDG Indicator 11.3.1 assessments.Keywords: urban SDG; land use change; spatial SDG1. IntroductionUrbanization is driving land use change globally [1] and projections suggest a sub-stantial loss of natural land to urban expansion over the coming decades [2]. Sustainabledevelopment initiatives have been established to combat challenges of urbanization, mostnotably under the 2030 Agenda for Sustainable Development. Drawn from the MillenniumSustainability 2024, 16, 9698. https://doi.org/10.3390/su16229698 https://www.mdpi.com/journal/sustainabilitySustainability 2024, 16, 9698 2 of 21Development Goals [3], 17 Sustainable Development Goals (SDGs) and 169 accompanyingtargets shape the 2030 Agenda for Sustainable Development to synergistically confrontthe social, economic, and environmental needs for accelerating global sustainable develop-ment [4]. The SDGs and their targets aim to achieve broad objectives, including eradicatingpoverty, increasing access to essential resources, reducing social and economic inequalities,and promoting peace [5]. The SDGs and their targets are objectives; thus, the UnitedNations Statistical Commission formed and tasked the Inter-Agency and Expert Groupon Sustainable Development Goal (IAEG-SDG) indicators with developing the GlobalIndicator Framework [6]. The indicator framework contains 231 unique indicators devel-oped to measure progress towards the achievement of the SDGs and associated targets.Many indicators have received criticism, demanding more exploration and development toimprove their utility [7–9].The SDG indicators were envisioned to use official data from national statistical sys-tems, but additional data sources are necessary to advance the 2030 Agenda for SustainableDevelopment. Earth observation and geospatial data have been used for the practicableimplementation of numerous SDG Indicators [10–12], transcending many limitations ofground-based data to support consistent, widespread, and novel SDG Indicator assess-ments. A multitude of studies have demonstrated the viability of using spatial approachesto examine SDG indicators, for instance, monitoring SDG 11.7.1 (share of open urbanspace) in Greece [13], SDG 6.4.1 (change in water use efficiency) in South and South-EastAsia [14], and SDG 15.3.1 (identification of degraded lands) in Ukraine [15]. More recently,the IAEG-SDG’s Working Group on Geospatial Information was formed to emphasize theimportance of geospatial data for measuring relevant SDG Indicators, particularly to bridgethe gap in data needs for developing countries [16]. Global organizations, principally theUnited Nations, continue to work to facilitate improved spatial data infrastructures, anddata production and access [17,18], but further work is needed to drive improvements inSDG Indicator assessments.SDG Indicator 11.3.1 assessments regularly exploit geospatial data to evaluate urbanland expansion and progress towards Goal 11 and Target 11.3 (Table 1). The indicator is aratio of the land consumption rate to population growth rate and is measured by spatialanalysis of developed land use or built-up land cover data and population data within anurban area of interest [12]. SDG Indicator 11.3.1 evaluates whether undeveloped land isbeing consumed for urban use at a lesser, greater, or similar rate to which the population inthe same area is growing, and the calculation of additional metrics, such as the built-uparea per capita, are suggested to support the understanding of urban expansion in anarea (see Section 2.4). SDG Indicator 11.3.1 is classified as a Tier 2 indicator, meaning itis a conceptually clear indicator and methods exist for its computation, but data are notregularly produced for some regions. Global geospatial datasets may be used in place ofabsent local data, but the coarser spatial resolution common to many global products mayimpact assessment results [12].Table 1. Description of Sustainable Development Goal 11, Target 11.3 and SDG Indicator 11.3.1 [12].Sustainable Development Goal 11Make cities and human settlements inclusive, safe, resilient, and sustainable.Target 11.3Enhance inclusive and sustainable urbanization and capacities for participatory, integrated, andsustainable human settlement planning and management in all countries by 2030.SDG Indicator 11.3.1Ratio of land consumption rate to population growth rate.Regardless of potential data availability challenges, SDG Indicator 11.3.1 has beenutilized to monitor urban land expansion across a range of urbanizing environments atvarying scales and expanses [19–21], and many studies have explored new approaches tobuild on the performance of the indicator. Nicolau et al. [22] compared the performanceSustainability 2024, 16, 9698 3 of 21of two formulations for examining SDG Indicator 11.3.1 in mainland Portugal, the UN-Habitat’s proposed land consumption rate to population growth rate and the change rateof the built-up area per capita. They found that the change rate of the built-up area percapita was more straightforward to interpret than SDG Indicator 11.3.1 and, arguably, bettersuited for monitoring urban development. Schiavina et al. [9] assessed SDG Indicator 11.3.1at the global, regional and settlement level, revealing the benefit of multi-scale compar-isons for capturing dynamics of urban change. Guo et al. [23] proposed an additionalindicator, the ratio of economic growth rate to land consumption rate, to be applied along-side SDG Indicator 11.3.1 to evaluate the relationship between urban economic growthand urban expansion. Cardenas-Ritzert et al. [24] developed an automated geospatialapproach to facilitate the delineation of urban agglomerations and calculation of SDGIndicator 11.3.1 at the urban agglomeration scale across national extents. The automatedapproach captured urban agglomerations composed of multiple, diverse settlements andexamined urban changes within them over a five-year period in Ethiopia, Nigeria, andSouth Africa, summarizing urban changes at multiple levels and identifying hotspots ofurban land expansion.Despite the advancements provided by existing studies, examinations of the effectsof geospatial data characteristics on SDG indicators and related outputs are lacking andshould be further investigated. In particular, urban area delineations and subsequent SDGIndicator 11.3.1 calculations are largely determined by land cover or land use map products,indicating potential limitations and impacts to analyses related to the spatial resolution ofthat data [25]. Provided that spatial resolution of land cover and land use products oftendiffer across global and local datasets [26–28], the presented study evaluates the influence ofland use data at coarse and moderate spatial resolutions on SDG Indicator 11.3.1 and relatedoutputs. A higher spatial resolution captures finer features and increases the mappingaccuracy of land cover or land use data [29], so we theorize that details of urban land coverand urban boundary edges are changed when spatial resolution decreases. We postulatethat a changing urban boundary would greatly influence the delineation of physicallysmaller urban units, change urban metrics and patterns of urban land development withinurban units, and influence land use change assessments.This study aims to assess the influence of land use data at two different spatial resolu-tions, 30 m and 90 m, on characterizations of urban areas and measures of urban changeacross Ethiopia. The main objectives for this study are to (1) examine the influence of landuse data spatial resolution on urban agglomeration boundaries when using the automatedmethodology defined in Cardenas-Ritzert et al. [24]; (2) compare SDG Indicator 11.3.1values, supporting urban spatial metrics, and spatial patterns of urban land developmentcalculated from a land use product at the two spatial resolutions; and (3) compare land usechange assessments conducted within and around urban agglomerations between a landuse product at the two spatial resolutions.2. Materials and Methods2.1. Study RegionAlthough predominantly rural, Ethiopia is one of Africa’s fastest urbanizing countries.Ethiopia’s urban population is anticipated to increase from 108 million people in 2018 to191 million by 2050, accounting for 39% of the Ethiopia’s total population [30]. Recent esti-mates show Ethiopia’s urban population comprising 22% of the total Ethiopian populationin 2022 and the country is urbanizing at an annual rate of around 4–5% [31,32]. Naturalpopulation growth, rural to urban migration, and the reclassification of rural settlementsare driving rapid urbanization in Ethiopia. Although there are conflicting theories, ruralto urban migration is expected to account for a significant proportion of Ethiopia’s futureurban population growth, as rural dwellers migrate to cities in search of greater livelihoodsecurity and opportunity [33–35]. Ethiopia’s burgeoning urban regions have the potentialto generate significant economic growth and improve standards of living but face numerousfinancial and administrative constraints. Challenges stemming from rapid urbanization inSustainability 2024, 16, 9698 4 of 21Ethiopia include inadequate urban infrastructure (e.g., roads), poor provision of essentialservices (e.g., water and sewage), increased environmental health risks, and a lack of afford-able housing [32,36–38] along with a general increase in congestion diseconomies [39,40].Furthermore, rapid urbanization is driving land use changes within and surroundingEthiopian urban areas [41,42], which is further exacerbating existing challenges. Our studysites encompass urban areas (i.e., urban agglomerations) in Ethiopia identified using adelineation approach developed by Cardenas-Ritzert et al. [24], and largely informed byland use maps produced by Shah Heydari et al. [43] (Figure 1).Sustainability 2024, 16, x FOR PEER REVIEW 4 of 21 theories, rural to urban migration is expected to account for a significant proportion of Ethiopia’s future urban population growth, as rural dwellers migrate to cities in search of greater livelihood security and opportunity [33–35]. Ethiopia’s burgeoning urban regions have the potential to generate significant economic growth and improve standards of liv-ing but face numerous financial and administrative constraints. Challenges stemming from rapid urbanization in Ethiopia include inadequate urban infrastructure (e.g., roads), poor provision of essential services (e.g., water and sewage), increased environmental health risks, and a lack of affordable housing [32,36–38] along with a general increase in congestion diseconomies [39,40]. Furthermore, rapid urbanization is driving land use changes within and surrounding Ethiopian urban areas [41,42], which is further exacer-bating existing challenges. Our study sites encompass urban areas (i.e., urban agglomer-ations) in Ethiopia identified using a delineation approach developed by Cardenas-Ritzert et al. [24], and largely informed by land use maps produced by Shah Heydari et al. [43] (Figure 1). Figure 1. Map showing study area country (Ethiopia) and urban agglomerations within it for the year 2020 at the native mapping resolution of 30 m. 2.2. Spatial Data For this study, two main data inputs were required to delineate urban agglomera-tions, calculate urban expansion metrics, and compare outputs between two spatial reso-lutions of land use data: a land use map and spatial population dataset. Specifically, the data inputs included WorldPop’s top-down unconstrained 100 m spatial resolution grid-ded population data, and a base 30 m gridded land use product developed by Shah Hey-dari and Vogeler [43], which was also resampled and summarized at 90 m spatial resolu-tion (Table 2). Fi r . try ( thiopia) and urban a glomerations within it for theyear 2020 at the native apping resolution of 30 .2.2. Spatial DataFor this study, two main data inputs were required to delineate urban agglomerations,calculate urban expansion metrics, and compare outputs between two spatial resolutionsof land use data: a land use map and spatial population dataset. Specifically, the datainputs included WorldPop’s top-down unconstrained 100 m spatial resolution griddedpopulation data, and a base 30 m gridded land use product developed by Shah Heydariand Vogeler [43], which was also resampled and summarized at 90 m spatial resolution(Table 2).WorldPop’s top-down unconstrained population count data were utilized to extractpopulation counts within urban areas for both urban delineation and metric calculations.The dataset is developed by extracting the count of people per administrative unit fromrelevant census data, combining it with ancillary data (e.g., land cover), and employingthem in a random forest model to estimate the total number of people in 100 m × 100 mgrid cells for a given year [44]. The top-down unconstrained gridded population countdatasets are available for numerous developing countries from 2000 to 2020 as of early2023 and new count datasets will become available as new census or geospatial datasetsarise, permitting comprehensive and ongoing assessments of SDG Indicator 11.3.1. TheWorldPop 100 m resolution population count dataset for Ethiopia (2016 and 2020) wasdownloaded from the WorldPop website: www.worldpop.org/ (accessed on 18 July 2024).The 30 m gridded land use map product developed by Shah Heydari and Vogeler [43]is central to the delineation of urban agglomerations and urban expansion calculationsand is the focus of the work in this study. Using random forest and a variety of remoteSustainability 2024, 16, 9698 5 of 21sensing data inputs (e.g., Landsat, Sentinel-2, Sentinel-1, nightlight imagery), Shah Heydariand Vogeler [43] produced 30 m spatial resolution land use maps for Ethiopia, Nigeria,and South Africa for each year in the 2016 to 2020 period. The product comprises sevendiscrete classes describing land use modeled at the pixel level which includes Agriculture,Developed, Forest, Rangeland, Bare, Wetland, and Water (Figure 2). The 30 m land usemaps were obtained for Ethiopia (2016 and 2020) from Shah Heydari and Vogeler [43]on ORNL DAAC, and classification procedures are detailed in an associated publicationby Shah Heydari et al. [45]. For the delineation of urban agglomeration boundaries andcalculation of SDG Indicator 11.3.1 and related metrics, only the Developed land use classwas used (see Cardenas-Ritzert et al. [24]).Sustainability 2024, 16, x FOR PEER REVIEW 6 of 21 Figure 2. Map showing study area country (Ethiopia) and land use within it for the year 2020 at the native mapping resolution of 30 m. To examine the influence of spatial resolution on SDG Indicator 11.3.1 related out-puts, the 30 m spatial resolution developed land use raster was resampled to 90 m spatial resolution using a majority rule method. Under the majority resampling method, the 90 m resolution cell values were determined by the most popular land use value within a four-by-four cell neighborhood. The goal was to assess the influence of spatial resolution alone, and resampling the developed land use raster preserved uniformity in map pro-duction and classification methods, although arguably this may have limitations (see Sec-tion 4). 2.3. Automated Approach for Identifying Urban Agglomerations Cardenas-Ritzert et al. [24] developed and presented a methodology to automatically delineate urban agglomerations across national extents to facilitate the assessment of SDG Indicator 11.3.1 and supporting metrics. The methodology incorporates various spatial datasets, tools, and techniques to detect functionally connected urban areas across na-tional extents. The approach employs various spatial analysis techniques, thresholds of population characteristics, geographic data from OpenStreetMap [46], and transportation analyses from the openrouteservice API [47] to identify clusters of urban character, differ-entiate urban clusters into classes, assess connectivity between these cluster classes, and produce functionally connected urban agglomerations [24]. The 30 m spatial resolution and 90 m spatial resolution versions of the land use prod-uct were incorporated into the automated urban delineation approach using the associ-ated Python 3.9.16 scripts provided by Cardenas-Ritzert et al. [24] on GitHub: https://github.com/VogelerLab/SDG-11.3.1-Automated-Urban-Delineation-Code (ac-cessed on 1 October 2024). Following the delineation of urban boundaries across the 2016 and 2020 Ethiopia land use maps, overlap analyses were carried out manually in QGIS to ensure the occurrence of corresponding urban agglomerations for both resolutions of the Figure 2. Map showing study area country (Ethiopia) and land use within it for the year 2020 at thenative mapping resolution of 30 m.To examine the influence of spatial resolution on SDG Indicator 11.3.1 related outputs,the 30 m spatial resolution developed land use raster was resampled to 90 m spatialresolution using a majority rule method. Under the majority resampling method, the 90 mresolution cell values were determined by the most popular land use value within a four-by-four cell neighborhood. The goal was to assess the influence of spatial resolution alone,and resampling the developed land use raster preserved uniformity in map productionand classification methods, although arguably this may have limitations (see Section 4).Sustainability 2024, 16, 9698 6 of 21Table 2. Summary of datasets utilized.Data SpatialResolutionDataFormat Function SourceGriddedPopulation Data 100 m Count dataIdentifypopulationcharacteristics todelineate urbanagglomerationsandcalculate metricsWorldPop (www.worldpop.org/)(accessed on 18July 2024)Base Land UseMap 30 mDiscrete land useclassesIdentify landfeatures todelineate urbanagglomerationsand calculatemetricsShah. Heydariet al. [43]Resampled LandUse Map 90 mDiscrete land useclassesIdentify landfeatures todelineate urbanagglomerationsand calculatemetricsShah. Heydariet al. [43]2.3. Automated Approach for Identifying Urban AgglomerationsCardenas-Ritzert et al. [24] developed and presented a methodology to automaticallydelineate urban agglomerations across national extents to facilitate the assessment of SDGIndicator 11.3.1 and supporting metrics. The methodology incorporates various spatialdatasets, tools, and techniques to detect functionally connected urban areas across nationalextents. The approach employs various spatial analysis techniques, thresholds of popula-tion characteristics, geographic data from OpenStreetMap [46], and transportation analysesfrom the openrouteservice API [47] to identify clusters of urban character, differentiateurban clusters into classes, assess connectivity between these cluster classes, and producefunctionally connected urban agglomerations [24].The 30 m spatial resolution and 90 m spatial resolution versions of the land useproduct were incorporated into the automated urban delineation approach using theassociated Python 3.9.16 scripts provided by Cardenas-Ritzert et al. [24] on GitHub: https://github.com/VogelerLab/SDG-11.3.1-Automated-Urban-Delineation-Code (accessed on1 October 2024). Following the delineation of urban boundaries across the 2016 and 2020Ethiopia land use maps, overlap analyses were carried out manually in QGIS to ensurethe occurrence of corresponding urban agglomerations for both resolutions of the landuse data inputs. Urban agglomerations present under one resolution input and not theother were documented and removed from additional analyses. Discrepancies in urbanagglomeration boundaries, urban change metrics, spatial patterns of urban development,and land use changes were examined and described (see Section 3.1).2.4. SDG Indicator 11.3.1 and Supporting MetricsSDG Indicator 11.3.1 and supporting metrics were calculated for all urban agglomera-tions identified in Ethiopia under the 30 m and 90 m land use maps for the 2016 to 2020analysis period. These metrics were calculated within the boundaries of each urban ag-glomeration including land consumption rate and population growth rate, which togetherare necessary components for calculating SDG Indicator 11.3.1. Moreover, the UN suggestsevaluating additional metrics to support the interpretation of SDG Indicator 11.3.1 [12].The supporting metrics calculated in this work include percent change in developed area(DA), DA per capita in 2016, DA per capita in 2020, new development by infill, and newdevelopment by outward growth.Sustainability 2024, 16, 9698 7 of 21The land consumption rate examines the change in the developed land use area fromthe initial year within a defined time period to the final year. The land consumption rateformula is as follows:Land Consumption Rate (LCR) =(Vpresent − Vpast)(Vpast) ∗ 1(t)where Vpresent is the total developed land use area in the final year, Vpast is the totaldeveloped land use area in the initial year, and t is the number of years between Vpresentand Vpast [12].The population growth rate evaluates the change in population from the initial year tothe final year. The population growth rate formula is as follows:Population Growth Rate (PGR) =LN (Popt+n / Popt)(y)where LN is the natural log, Popt+n is the total population in the urban agglomeration in thefinal year of a given analysis period, Popt is the total population in the urban agglomerationin the initial year of the given analysis period, and y is the difference between the initialyear and final year of the analysis period [12].SDG Indicator 11.3.1 is the ratio between the land consumption rate and the populationgrowth rate. SDG Indicator 11.3.1 may also be referenced to as LCRPGR and the formula isas follows:SDG Indicator 11.3.1 (LCRPGR) = LCR / PGR (1)where LCR is the land consumption rate, and PGR is the population growth rate [12].Two additional metrics are strongly suggested for supporting the interpretation of SDGIndicator 11.3.1 and are the total change in built-up area and built-up area per capita [12].The first metric inspects the percent change in built-up land within an urban area over aperiod. We used developed area (DA) from our land use product in place of built-up areain the total change in built-up area formula. The formula is as follows:Total change in built-up area (%) =(UrBUt+n− UrBUt)UrBUt(2)where UrBUt+n is the urban built-up area or DA in the final year of the period, and UrBUtis the urban built-up area or DA in the initial year of the period [12].We used developed land use area in place of built-up area in calculating built-up areaper capita. The formula for this metric is as follows:Built-up area per capita(m2/person)= (UrBUt/Popt) (3)where UrBUt is the urban built-up area or DA in a given year (time t), and Popt is thepopulation size within the urban area in the same year (time t) [12].Lastly, urban development by infill, and urban development by outward growth,which included extension and leapfrog forms of development [12], were calculated foreach urban agglomeration under both resolution inputs. These metrics provide informa-tion about the spatial patterns new development is taking form in and also support theinterpretation of SDG Indicator 11.3.1. Infill was defined as the newly developed landthat occurred after the initial year and existed only within the urban boundaries of theinitial year. Outward growth was defined as newly developed land that occurred after theinitial year and only existed in the boundaries of the final year and not the initial year. Theformulas for the percent development by infill and percent development by outward wereas follows:Development by infill (%) =(IFNDL)∗ 100 (4)Sustainability 2024, 16, 9698 8 of 21where IF is the newly developed area that is in the form of infill development and NDL isthe total newly developed land area.Development by outward growth (%) =(OUTNDL)∗ 100 (5)where OUT is the newly developed area that follows outward forms of growth, includingextension and leapfrog, and NDL is the total newly developed land area.2.5. Statistical and Summary AnalysesTo best evaluate the differences in metrics derived under the differing land use spatialresolution inputs, we relied not only on descriptive statistics to illuminate general changesbetween outputs, but also verified statistically significant changes. We began by convertingthe attribute table containing metrics for the 30 m spatial resolution outputs and 90 m spatialresolution outputs into a CSV file. The data were imported into R Version 4.2.1, wheresummary and statistical analyses were performed. The main packages we used includedthe tidyverse library within R to manipulate and prepare the data for summarizationand use in statistical tests, as well as the stats library to conduct summary and statisticalanalyses [48]. Using the Wilcoxon signed rank test, differences in urban change metricswere examined between the two resolutions [49]. The Wilcoxon signed rank test is a non-parametric statistical test that can be used to examine paired samples [50]. In the case ofthis work, the metrics calculated under each land use data resolution for a specific urbanagglomeration are the paired samples being compared.We tested for statistically significant differences between the outputs of the 30 m spatialresolution land use data and the 90 m spatial resolution land use data for the completeagglomeration sample and for the agglomerations when categorized by population sizeclass. We divided the agglomerations into three population size classes: population smallerthan 50,000, population between 50,000 and 100,000, and population greater than 100,000.The population size classes were intended to separate relatively small urban areas, suchas less dense towns with fewer than 50,000 people, from larger urban areas with morethan 100,000 people, such as metropolitans or secondary cities [51,52]. The intermediatepopulation size class between 50,000 and 100,000 people was created to capture emergingurbanizing areas and potential future secondary cities.2.6. Land Use Change Under Different Spatial ResolutionsUrbanization often drives radical landscape changes and results in the loss of im-portant, provisioning natural land uses [53,54], making it an important consequence ofurbanization to understand. Therefore, we summarized and compared urbanization drivenland use changes between 2016 and 2020 within and around urban agglomerations at 30 mand 90 m spatial resolution using the land cover change tool from the Semi-AutomaticClassification Plugin in QGIS to examine land use change [55]. The tool required two rasterinputs to measure change. The inputs included a 2016 seven-class land use map and araster containing newly developed land that occurred by 2020. The output of the tool was achange map with a file explaining the land use type that existed in 2016 that was convertedto developed land by 2020.To assess the urbanization driven land use changes around agglomerations identifiedat both resolutions, a shared focal boundary was defined to maintain consistency in thespatial analyses’ areas. A convex hull, the smallest polygon that enveloped around each ofthe overlaid urban agglomerations, was created to produce a boundary geometry aroundeach unique urban area for each resolution. We combined the boundaries by dissolving tocreate one shared analyses area for each focal agglomeration. Each boundary was manuallyvalidated to ensure the bounding geometries contained the correct agglomerations for eachresolution. Zonal statistics were carried out to summarize the land use change area underboth resolutions within the shared boundaries.Sustainability 2024, 16, 9698 9 of 213. Results3.1. Differences in Urban BoundariesAgglomerations smaller in area and, correspondingly, smaller in population size werebetter identified by the 30 m spatial resolution land use input data. Eight instances occurredwhere an agglomeration was identified under the 30 m spatial resolution input and notunder the 90 m input. These agglomerations were exceptionally small with populationsranging from approximately 5000 people (the lower population limit within the delineationapproach) to 10,000 people in 2020 and urban extents ranging from an approximated 1 km2to 5 km2 in area in 2020 (Table 3). Although all missed areas were comparable in populationcharacteristics and urban boundary area, the spatial form of their urban extents did differ(Figure 3). Regardless, the 90 m spatial resolution input still failed to capture these areas.Additionally, the 30 m spatial resolution land use data captured marginal hinterlandareas differently than the 90 m resolution land use data. Several instances were observedwhere a peripheral urban area was included in an agglomeration under the 30 m resolutioninput but did not exist under the 90 m resolution input. These clusters or extensions repre-senting peripheral urban areas typically comprised sparse development, only contained afew developed land use pixels under the 90 m spatial resolution version or were linked tothe core urban areas by linear developments, such as roads, which were scarcely capturedunder the 90 m resolution land use data. Clusters with these characteristics typically dif-fered in size or contiguity, and were therefore likely not dense enough in developed land orpopulation to be retained based on the delineation approach used [24]. Arguably, the abilityto capture hinterland areas could have also been affected by inaccuracies in the underlyingland use datasets and/or variations in the characteristics of development captured underthe land use data (Figure 4).Sustainability 2024, 16, x FOR PEER REVIEW 10 of 21 Figure 3. Examples of areas missed under the 90 m resolution input and captured under the 30 m resolution input. Figure shows satellite imagery of the towns Kumbabe and Elias overlaid with their respective 2020 urban boundary polygons extracted from 30 m resolution input land use data. Figure 3. Examples of areas missed under the 90 m resolution input and captured under the 30 mresolution input. Figure shows satellite imagery of the towns Kumbabe and Elias overlaid with theirrespective 2020 urban boundary polygons extracted from 30 m resolution input land use data.Sustainability 2024, 16, 9698 10 of 21Sustainability 2024, 16, x FOR PEER REVIEW 10 of 21 Figure 3. Examples of areas missed under the 90 m resolution input and captured under the 30 m resolution input. Figure shows satellite imagery of the towns Kumbabe and Elias overlaid with their respective 2020 urban boundary polygons extracted from 30 m resolution input land use data. Figure 4. Display of differences in urban boundaries for the Durame agglomeration in 2020 under30 m and 90 m spatial resolution land use inputs. (A) Urban agglomeration boundaries for Durameand connected peripheral areas at 30 m resolution and 90 m resolution in 2020. (B) Urban boundaryin southern portion of Durame under 30 m resolution in 2020. (C) Urban boundary in southernportion of Durame under 90 m resolution in 2020. (D) Developed land use pixels captured under30 m resolution input with corresponding urban boundary in 2020. (E) Developed land use pixelscaptured under 90 m resolution input with corresponding urban boundary in 2020. (F) Transparentdeveloped land use pixels from 30 m resolution map input overlaid on satellite imagery in 2020.(G) Transparent developed land use pixels under 90 m resolution map input overlaid on satelliteimagery in 2020.Table 3. Agglomerations present under 30 m spatial resolution inputs but not under 90 m spatialresolution inputs. The table includes the associated name of the agglomeration, the population of theagglomeration in 2020, and the area of the urban boundaries in 2020.UrbanAgglomeration 2020 Population2020 Urban BoundariesArea (km2)Kobo 7014 1.17Chwahit 9196 2.51Kumbabe 9339 4.81Tis Abay 7428 1.36Wukro Maray 6373 1.76Elias 8201 5.32Deksis 7841 3.25Karamile 5468 1.60Sustainability 2024, 16, 9698 11 of 213.2. Differences in Urban Change MetricsUrban change metrics calculated for all agglomerations varied between the inputresolutions. Summary statistics of urban change metrics highlighted LCR, SDG Indicator11.3.1, 2020 DA per capita, 2020 Change in DA, infill, and outward growth values asconsiderably different between the 30 m and 90 m input (Figures A1–A7). For example,SDG Indicator 11.3.1 exhibited extreme change between the 30 m and 90 m resolution input,with a difference range of 70.7. The 2020 DA per capita and 2020 Change in DA values alsovaried between resolutions, with a difference range of 110 and 215, respectively.The examination of the agglomerations when divided by population size class elicitedmore details on the differences observed (Table 4). The agglomerations with fewer than50,000 people showed the greatest difference in all urban change metrics when compared tothe changes observed across the other population sizes classes. Differences in SDG Indicator11.3.1 values were quite considerable for the 50 k size class, displaying a substantial rangein differences from −69.9 to 0.69. For agglomerations with 50 k to 100 k, the largestdifferences were detected for 2020 DA per capita and 2020 Change in DA, while theremaining metrics appeared to exhibit negligible changes. Larger agglomerations withmore than 100,000 people saw a lesser change in SDG Indicator 11.3.1 with a differencerange of 1.04, while 2020 DA per capita and 2020 Change in DA were greater.Table 4. Summary statistics for differences in outputs between the 30 m spatial resolution land usedata input and 90 m spatial resolution land use data input. Min is the minimum difference observed,and Max is the maximum difference observed from the 30 m input to the 90 m input. LCR is landconsumption rate, PGR is population growth rate, and DA is developed area.Differences Between 30 m and 90 m Resolution Data OutputsAll Fewer than 50 k 50 k to 100 k More than 100 kMetrics Min Max Min Max Min Max Min MaxLCR −0.13 0.30 −0.13 0.07 −0.02 0.01 −0.01 0.30PGR −0.03 0.06 −0.03 0.02 <−0.01 <0.01 <−0.01 0.06SDG Indicator11.3.1 −69.9 0.80 −69.9 0.69 −0.14 0.46 −0.24 0.802020 DA per capita −58.0 52.0 −58.0 52.0 −10.0 8.0 −10.0 13.02020 Change inDA (%) −65.0 150.0 −65.0 34.0 −11.0 8.0 −7.0 150.0Infill (%) −0.17 0.24 −0.17 0.24 <0.00 0.04 <−0.01 0.06Outward Growth(%) −0.24 0.17 −0.24 0.17 −0.04 <0.00 −0.06 <0.01After obtaining summary statistics, the Wilcoxon Rank Test was conducted to under-line the metrics with statistically significant differences. We have only reported on thosethat showed statistically significant differences which included SDG Indicator 11.3.1, DAper capita in 2020, infill, and outward growth (Table 5). Statistically significant differenceswere detected for 2020 DA per capita, infill, and outward growth values for smaller ur-ban agglomerations with fewer than 50,000 people and moderate sized agglomerationswith 50,000 to 100,000 people. The largest size class of agglomerations with more than100,000 people displayed statistically significant differences for SDG Indicator 11.3.1, 2020DA per capita, infill, and outward growth values. While there was statistical evidence of adifference, the estimated median differences across all significant results were minuscule.Sustainability 2024, 16, 9698 12 of 21Table 5. Results of the Wilcoxon Signed Rank Test for the differences between the 30 m outputs and90 m outputs.Wilcoxon Signed Rank Test of the DifferencesSDG Indicator 11.3.1 2020 DA Per Capita Infill Outward GrowthMedian p-Value Median p-Value Median p-Value Median p-ValueAll 0.02 * 0.02 −5.00 * <0.01 0.03 * <0.01 −0.03 * <0.01Fewer Than50 k 0.02 0.14 −7.00 * <0.01 0.03 * <0.01 −0.03 * <0.0150 k to 100 k <0.01 0.27 −4.50 * 0.01 0.03 * <0.01 −0.03 * <0.01More than 100 k 0.05 * 0.03 −2.00 * 0.01 0.03 * <0.01 −0.03 * <0.01* p ≤ 0.05.3.3. Land Use Change Analysis ResultsIn addition to testing differences in urban boundaries and change metrics, land useconversion patterns were characterized within and surrounding urban agglomerationsunder both input resolutions. First, land use conversions observed under the 30 m reso-lution data were summarized to then compare with outputs of the 90 m resolution data.Under the 30 m resolution input, agricultural land experienced the greatest conversion todeveloped land use at approximately 1044 km2 (Table 6). Rangeland was the second mostconverted land use, with 160 km2 of land transformed to developed land from 2016 to 2020.Forest was the next most converted land at 17 km2, followed by wetland at 2 km2. Lessthan 2 km2 of barren land and water was lost to developed land within the focal areas from2016 to 2020 according to the 30 m resolution data.Table 6. Summary of land use conversions within identified urban boundaries and their immediatesurroundings under 30 m spatial resolution land use data and 90 m resolution land use data.Land Use Change 30 m Spatial Resolution(m2)90 m Spatial Resolution(m2)Agriculture to Developed 1,044,825,779 1,029,453,382Barren to Developed 708,924 554,452Forest to Developed 17,039,378 15,493,416Rangeland to Developed 160,122,435 145,383,560Water to Developed 311,280 429,506Wetland to Developed 2,752,031 1,967,914The main conversions under the 90 m resolution input remained the same as the 30 mresolution input with agricultural land, rangeland and forest experiencing the greatestloss to developed land in that order over the 2016 to 2020 period. Although the maintypes of conversions remained the same, the resulting areas for each conversion type didchange between the input resolutions. From the 30 m input to the 90 m input, agriculturalconversion area decreased by 15 km2, rangeland conversion area decreased by 15 km2,and the forest conversion area decreased by 2 km2. There was less than a 1 km2 differencebetween both resolution inputs for barren, water, and wetland conversions. Although thedifference was small, water conversions were the only conversion type to increase in totalarea from the 30 m resolution input to the 90 m resolution input.When divided by population size classes, urban areas with fewer than 50 k people andurban areas with a population from 50 k to 100 k showed a large decrease in the area of landconverted from agricultural to developed when comparing the 30 m input to the 90 m input.Conversely, urban areas with more than 100,000 people displayed a substantial increasein the area of land converted from agricultural to developed when moving from 30 mresolution to 90 m resolution (Table 7). The area of rangeland converted to developed landincreased across all population size classes as the resolution of the input data decreased.Sustainability 2024, 16, 9698 13 of 21For this conversion type, the magnitude of the difference did differ, with the largest classexperiencing the greatest change, followed by the smallest class and, next, the intermediateclass. Additionally, the area of forest converted to developed land also increased for allpopulation size classes, with the change in area being remarkably similar for the smallestpopulation size class and largest population size class.Table 7. Table showing the differences in land change conversion area between 30 m spatial resolutionland use data and 90 m resolution land use data for each conversion type across each populationsize class.Differences Between 30 m and 90 m Spatial Resolution LandUsed Data(m2)Fewer than 50 k 50 k to 100 k Greater than 100 kAgriculture toDeveloped −3,873,626 −2,150,645 21,396,668Barren to Developed 39,255 1475 113,742Forest to Developed 696,325 155,762 693,875Rangeland toDeveloped 2,796,725 950,391 10,991,759Water to Developed −73 9285 −127,438Wetland toDeveloped 218,245 89,242 476,6304. DiscussionThe spatial resolution of land use data was found to be influential on the delineation ofurban agglomerations, urban change metrics, spatial patterns of urban development, andurban land use change patterns. The 30 m spatial resolution land use map identified smallerurban areas, including individual urban agglomerations and smaller peripheral urban areasassociated with a denser urban core, differently to the 90 m spatial resolution land use map.Urbanization metrics derived from the 30 m and 90 m land use maps displayed a rangeof differences (Figures A1–A7), and statistical testing highlighted significant differences,particularly in SDG Indicator 11.3.1, 2020 Developed Land Use Area per Capita, and spatialpatterns of urban development between resolutions. We assume the change in land useresolution and interaction between the population and land use dataset in specific metriccalculations resulted in significantly different outputs, as highlighted by statistical tests.Additionally, the connectivity between developed land use pixels was likely altered, both asa result of the resampling process and resolution decrease, creating a change in the spatialpatterns of development observed. The land use change analysis conducted within urbanagglomerations and their surroundings revealed differences in the areas of conversiontypes under each resolution. Differences were anticipated given the principles of spatialresolution, but a quantification of the differences further emphasized the consequencesof data resolution when conducting these analyses for multiple urban areas diverse insize and across large extents, as well as impacts on urbanization-driven land use changepatterns. Although differences between resolutions and across urban areas of differentsizes followed no direct pattern or relationship, these findings still have implications forcompleted and future SDG Indicator 11.3.1 assessments.Under the automated delineation approach, the 90 m spatial resolution data inef-fectively captured numerous standalone, smaller urban areas. For example, the town ofKumbabe agglomeration was not captured as a standalone urban area under the 90 mresolution but was under the 30 m resolution (Figure 2; Table 2). In 2016, the developedland use summarized under the 90 m land use product for this area was very small. Due tothis, we assume it was not big enough to be captured in the first year of the 90 m resolutionSustainability 2024, 16, 9698 14 of 21input. The automated approach does not retain agglomerations that do not have a pair inboth years, so it was likely removed at this step. Smaller urban areas play an important rolein the progression and development of urban Africa [56,57], but global land products thatare often of coarser resolutions (e.g., 90 m or 100 m) [58] may not be sufficient for accuratelyidentifying these areas or monitoring their change.Current global products at coarser resolutions may also produce biased estimates ofSDG Indicator 11.3.1 for very small urban areas, as we highlighted major differences inLCR and SDG Indicator 11.3.1 between the 30 m and 90 spatial resolution land use inputs.For example, the town of Gore agglomeration’s SDG Indicator 11.3.1 value jumped from6 under the 30 m resolution input to 38 under the 90 m input. Under both resolutions,SDG Indicator 11.3.1 indicated substantial expansion of urban land, but the magnitudeof that change varied. In our examination of high-resolution imagery, the extent of thistown comprised a small area and did not contain a large amount of developed land usepixels, and it did not change significantly over the 5-year period. For this small urbanagglomeration, the addition of only a few developed land use pixels created a ratherlarge LCR value under both resolutions and thereby inflated the SDG Indicator 11.3.1value. Furthermore, we observed the inflation of SDG Indicator 11.3.1 values for largeragglomerations that incorporated peripheral areas under one resolution and not the other.Based on these results, we support other works suggesting a stronger reliance on metricsoutside of SDG Indicator 11.3.1, such as change in built-up area per capita or change intotal built-up area, for evaluating urban change [22], as those metrics appeared more robustto the effects of spatial resolution and still illuminate similar urbanization patterns.Land use change analyses provide important information for sustainable urban de-velopment and the conservation of priority land use types [59], and our findings showthat summaries of land use change vary under different spatial resolutions and for urbanareas of different population sizes. These changes may be negligible across broad scalesummaries, but finer scale assessments may exhibit differences in spatial patterns of landuse conversions or shifts in the proportion of land use conversions. Our findings are notnovel [60,61], but they further corroborate the importance of considering the influenceof spatial resolution on land use change assessments, including calculations of land useefficiency under SDG Indicator 11.3.1, and future work should further test these finerscale effects.Studies examining SDG Indicator 11.3.1 and urban characteristics have been conductedon small and large urban areas across diverse extents, using spatial products of varyingresolutions [19–21,57,62]. Discrepancies may exist in the results of studies like these asthe ability to capture specific changes under coarse or moderate resolution data varies.Additionally, utilizing land products of differing resolutions may make it difficult tocompare results consistently for given areas over time, and a concern for monitoring effortsconducted across large areas using inconsistent data products.Our findings underline an important aspect of geospatial work that may be over-looked in SDG Indicator applications: the significance of input spatial data characteristicsand its implications on outputs and interpretation of results. Assessments such as theone presented in this study are important for identifying and better understanding thelimitations of available data and methods for monitoring urban change, especially fordata poor areas reliant on geospatial evaluations (e.g., developing countries, particularlysmaller urban areas within them) [63]. Our findings indicate that spatial assessments usingvaried resolutions can lead to discrepancies in outputs. Generally, the magnitude of thedifferences we observed between outputs was minor, meaning that even coarser resolutiondatasets, such as 90 m or 100 m, may still be effective for conducting urbanization-relatedgeospatial assessments across urban areas of all sizes, but users should be cautious wheninterpreting results, especially SDG Indicator 11.3.1 and particularly for smaller urbanareas. For example, underlying land use products may have constraints related to accuracy,and when combined with differences in spatial resolution, can potentially lead to differentoutcomes in urban delineation as observed in Figure 3.Sustainability 2024, 16, 9698 15 of 21Our study was limited to the examination of the influence of moderate and coarseresolutions on SDG 11.3.1 and related outputs. It should be noted that the work presentedin this study only compared the 30 m land use data to a resampled 90 m dataset createdfrom interpolated values. A land use dataset initially classified at the same coarse resolutionwould likely differ from the resampled land use dataset we used, and arguably, couldresult in a more accurate depiction of land use at the given coarse resolution, which wouldalter SDG Indicator 11.3.1 assessment outcomes. Additionally, land use and land coverdatasets at higher resolutions, such as 10 m, would likely be more effective at capturing thetrue spatial form of an urban area and identify developed or built-up land with a higherdegree of accuracy, leading to more detailed SDG 11 and 11.3.1 related assessments. Thelimitations of spatial data we highlight in our work emphasize the importance of continuedground-based efforts and incorporation of higher resolution spatial datasets when possible.Future work should evaluate the performance of higher-spatial-resolution datasets (e.g.,10 m) and the influence of other characteristics of data inputs in the assessments of urbanchange to continue improving urban monitoring efforts for SDG 11. The presented workhas touched on the limitations of a coarser resolution land use dataset (i.e., 90 m), butmore work is needed to understand the finer spatial resolution at which the differenceswe observed become insignificant. Nevertheless, ground based work and other forms ofresearch investigating drivers and consequences of urban expansion may capture detailsmissed by geospatial assessments such as this, and are vital for informing effective urbandevelopment policy and planning [64,65].5. ConclusionsThe findings of this study revealed that the spatial resolution of land use data wasinfluential on urban delineations, calculated urban change metrics including SDG Indicator11.3.1, spatial patterns of urban land development, and land use change. This workhighlights the importance of spatial data characteristics when assessing SDG Indicator11.3.1 and quantifying appropriate spatial urban change metrics, as well as when comparingurban change patterns across studies or locations using differing base land use resolutions.Consideration of spatial data characteristics when conducting geospatial assessments isapplicable to SDG Indicator 11.3.1 and other geospatially compatible indicators. Furtherwork is needed to identify the influences of other spatial resolutions and characteristics ofdata inputs on SDG Indicator 11.3.1 to support assessments across urban areas of all sizesand to progress SDG 11.Author Contributions: Conceptualization, J.C.V., M.R.M., M.L. and O.S.E.C.-R.; methodology,O.S.E.C.-R. and J.C.V.; software, O.S.E.C.-R. and P.A.F.; validation, O.S.E.C.-R. and S.S.H.; formalanalysis, O.S.E.C.-R.; investigation, O.S.E.C.-R.; resources, J.C.V.; data curation, O.S.E.C.-R. and J.C.V.;writing—original draft preparation, O.S.E.C.-R.; writing—review and editing, O.S.E.C.-R., J.C.V.,M.R.M., M.L., S.S.H. and P.A.F.; visualization, O.S.E.C.-R.; supervision, J.C.V.; project administration,J.C.V.; funding acquisition, J.C.V. All authors have read and agreed to the published version ofthe manuscript.Funding: This research was funded by the NASA Land Cover and Land Use Change Program, grantnumber 80NSSC21K0313.Institutional Review Board Statement: Not applicable.Data Availability Statement: The data presented in this study are available on request from thecorresponding author.Acknowledgments: We would like to acknowledge Steven Filippelli and other members of theVogeler Research Lab at Colorado State University for constructive discussions supporting this work.Conflicts of Interest: The authors declare no conflicts of interest.Sustainability 2024, 16, 9698 16 of 21Appendix ASustainability 2024, 16, x FOR PEER REVIEW 16 of 21 Appendix A Figure A1. Distribution of LCR differences between 30 m and 90 m resolution for all urban agglom-erations in Ethiopia. Figure A2. Distribution of PGR differences between 30 m and 90 m resolution for all urban agglom-erations in Ethiopia. Figure A1. Distribution of LCR differences between 30 m and 90 m resolution for all urban agglomer-ations in Ethiopia.Sustainability 2024, 16, x FOR PEER REVIEW 16 of 21 Appendix A Figure A1. Distribution of LCR differences between 30 m and 90 m resolution for all urban agglom-er i i t iopia. Figure A2. Distribution of PGR differences between 30 m and 90 m resolution for all urban agglom-erations in Ethiopia. Figure A2. Distribution of PGR differences between 30 m and 90 m resolution for all urban agglomer-ations in Ethiopia.Sustainability 2024, 16, 9698 17 of 21Sustainability 2024, 16, x FOR PEER REVIEW 17 of 21 Figure A3. Distribution of SDG Indicator 11.3.1 differences between 30 m and 90 m resolution for all urban agglomerations in Ethiopia. Figure A4. Distribution of DA per capita differences between 30 m and 90 m resolution for all urban agglomerations in Ethiopia. Figure A3. Distribution of SDG Indicator 11.3.1 differences between 30 m and 90 m resolution for allurban agglomerations in Ethiopia.Sustainability 2024, 16, x FOR PEER REVIEW 17 of 21 Figure A3. Distribution of SDG Indicator 11.3.1 diff rences between 30 m and 90 m resolution for all urban agglomerations in Ethiopia. Figure A4. Distribution of DA per capita differences between 30 m and 90 m resolution for all urban agglomerations in Ethiopia. Fi ure A4. Distribution of DA per capita differences between 30 m and 90 m resolution for all urbanagglomerations in Ethiopia.Sustainability 2024, 16, 9698 18 of 21Sustainability 024, 16, x FOR PEER REVIEW 18 of 21 Figure A5. Distribution of total change in DA (developed area) differences between 30 m and 90 m resolution for all urban agglomerations in Ethiopia. Figure A6. Distribution of development by infill differences between 30 m and 90 m resolution for all urban agglomerations in Ethiopia. Figure A5. Distribution of total change in DA (developed area) differences between 30 m and 90 mresolution for all urban agglomerations in Ethiopia.Sustainability 2024, 16, x FOR PEER REVIEW 18 of 21 Figure A5. Distribution of total change i ( e el e area) iffere ces et ee 30 and 90 m resolution for all urban a glomerations in Ethiopia. Figure A6. Distribution of development by infill differences between 30 m and 90 m resolution for all urban agglomerations in Ethiopia. Figure A6. Distribution of development by infill differences between 30 m and 90 m resolution for allurban agglomerations in Ethiopia.Sustainability 2024, 16, 9698 19 of 21Sustainability 2024, 16, x FOR PEER REVIEW 19 of 21 Figure A7. Distribution of development by outward growth (extension and leapfrog) differences between 30 m and 90 m resolution for all urban agglomerations in Ethiopia. References 1. Nuissl, H.; Siedentop, S. Urbanisation and Land Use Change. 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urban SDG
land use change
spatial SDG
Effects of Land Use Data Spatial Resolution on SDG Indicator 11.3.1 (Urban Expansion) Assessments: A Case Study Across Ethiopia
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