Africa is experiencing rapid urbanisation, which calls for well-considered urban and regional planning efforts to cater for the current and future populations. However, as it is typically the case in the global South, African countries are characterised by a lack of quality spatial economic data required for planning and evaluation processes. Using the study area of Harare, Zimbabwe, the paper demonstrates ways that, amidst the paucity of data, geographic information system can be used to measure urban development’s congruence with spatial plans. To prepare for the analysis, the base map preparation process entailed a laborious digitisation of hardcopy material obtained from the authorities. This was followed by land-use surveys and land-use change investigations whose data were analysed in ESRI’s ArcGIS 9.3. The analysis compared urban development patterns in 2014 with the proposals of two applicable spatial plans, which were approved in 1990 and 2000 respectively. The investigations uncovered that urban development patterns and trends did not correspond with the aspirations of the plans. The paper proposes that follow-up research be conducted on factors that influence the misalignment between plans and development, particularly in African countries that are characterised by rapid urbanisation.
{"title":"Using geographic information system to analyse the divergence of urban development from spatial plans in Harare, Zimbabwe","authors":"D. Machakaire, N. Tapela, Masilonyane Mokhele","doi":"10.4314/sajg.v9i2.15","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.15","url":null,"abstract":"Africa is experiencing rapid urbanisation, which calls for well-considered urban and regional planning efforts to cater for the current and future populations. However, as it is typically the case in the global South, African countries are characterised by a lack of quality spatial economic data required for planning and evaluation processes. Using the study area of Harare, Zimbabwe, the paper demonstrates ways that, amidst the paucity of data, geographic information system can be used to measure urban development’s congruence with spatial plans. To prepare for the analysis, the base map preparation process entailed a laborious digitisation of hardcopy material obtained from the authorities. This was followed by land-use surveys and land-use change investigations whose data were analysed in ESRI’s ArcGIS 9.3. The analysis compared urban development patterns in 2014 with the proposals of two applicable spatial plans, which were approved in 1990 and 2000 respectively. The investigations uncovered that urban development patterns and trends did not correspond with the aspirations of the plans. The paper proposes that follow-up research be conducted on factors that influence the misalignment between plans and development, particularly in African countries that are characterised by rapid urbanisation.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46789442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Peerbhay, Roxanne. Munsamy, M. Gebreslasie, R. Ismail
Accurate multi-source forest inventory attributes are necessary for estimating productivity and timber stock in commercial forest plantations. This study aims to uncover the effects of terrain variation on the growth of even aged Eucalyptus forest species using Light Detection and Ranging (LiDAR) topographical variables. Using 32 generated variables at 5 different spatial resolutions (1m, 3m, 5m, 7m, 9m), the random forest (RF) regression successfully revealed variations for structural attributes such as volume (Vol/ha), dominant tree height (HtD), mean tree height (Htm), and diameter breast heights (DBH). Results indicate that smaller spatial resolutions performed better for younger stands while larger resolutions produced the best results for mature stands. Using the multi-resolution approach results improved with variable selection. Incoming solar radiation and slope variables were among the most important terrain variables for modelling forest structural variability. The findings from this study demonstrates the value of stratifying forest productivity across the commercial forest landscapes of South Africa.
{"title":"Modelling the effect of terrain variability in even-aged Eucalyptus species using LiDAR-derived DTM variables","authors":"K. Peerbhay, Roxanne. Munsamy, M. Gebreslasie, R. Ismail","doi":"10.4314/sajg.v9i2.9","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.9","url":null,"abstract":"Accurate multi-source forest inventory attributes are necessary for estimating productivity and timber stock in commercial forest plantations. This study aims to uncover the effects of terrain variation on the growth of even aged Eucalyptus forest species using Light Detection and Ranging (LiDAR) topographical variables. Using 32 generated variables at 5 different spatial resolutions (1m, 3m, 5m, 7m, 9m), the random forest (RF) regression successfully revealed variations for structural attributes such as volume (Vol/ha), dominant tree height (HtD), mean tree height (Htm), and diameter breast heights (DBH). Results indicate that smaller spatial resolutions performed better for younger stands while larger resolutions produced the best results for mature stands. Using the multi-resolution approach results improved with variable selection. Incoming solar radiation and slope variables were among the most important terrain variables for modelling forest structural variability. The findings from this study demonstrates the value of stratifying forest productivity across the commercial forest landscapes of South Africa.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43264011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Popoola, Peters Durojaye, T. Bayode, A. Popoola, J. Olanibi, Olamide Aladetuyi
The threat of the increasing global temperature is now of global concern than ever before. This prompted the authors to gain insights on the Urban Heat Island (UHI) phenomenon in a medium-sized city of Akure, Nigeria. A random sampling of three hundred and twenty-five (325) structured questionnaires was administered and analyzed with the aid of the Statistical Package for Social Sciences (SPSS). Landsat satellite imagery for the years 2000; 2007; 2013 and 2018 were acquired and used for the computation of land use-land cover (LULC) and the Land Surface Temperature (LST) of the study area using ArcGIS 10.5. Between the years 2000 and 2018, built-up area increased by 8.78% at the expense of the non-built up land use. The residents were aware of UHI and climate change but characterized by superficiality. The study recommends a community awareness program on the menace of climate change and the integration of climate education into the curriculum of schools and other institutions of higher learning.
{"title":"Spatio-temporal variance and urban heat island in Akure, Nigeria: A time-spaced analysis Using GIS Techniqu","authors":"O. Popoola, Peters Durojaye, T. Bayode, A. Popoola, J. Olanibi, Olamide Aladetuyi","doi":"10.4314/sajg.v9i2.24","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.24","url":null,"abstract":"The threat of the increasing global temperature is now of global concern than ever before. This prompted the authors to gain insights on the Urban Heat Island (UHI) phenomenon in a medium-sized city of Akure, Nigeria. A random sampling of three hundred and twenty-five (325) structured questionnaires was administered and analyzed with the aid of the Statistical Package for Social Sciences (SPSS). Landsat satellite imagery for the years 2000; 2007; 2013 and 2018 were acquired and used for the computation of land use-land cover (LULC) and the Land Surface Temperature (LST) of the study area using ArcGIS 10.5. Between the years 2000 and 2018, built-up area increased by 8.78% at the expense of the non-built up land use. The residents were aware of UHI and climate change but characterized by superficiality. The study recommends a community awareness program on the menace of climate change and the integration of climate education into the curriculum of schools and other institutions of higher learning.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43405224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Feature selection techniques are often employed for reducing data dimensionality, improving computational efficiency, and most importantly for selecting a subset of the most important features for model building. The present study explored the utility of a Filter-Wrapper (FW) approach for feature selection using terrestrial hyperspectral remote sensing imagery. The efficacy of the FW approach was evaluated in conjunction with the Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers, to discriminate between water-stressed and non-stressed Shiraz vines. The proposed FW approach yielded a test accuracy of 80.0% (KHAT = 0.6) for both RF and XGBoost, outperforming the more traditional Kruskal-Wallis (KW) filter by more than 20%. The FW approach was also less computationally expensive when compared with the more commonly used Sequential Floating Forward Selection (SFFS) wrapper. Additionally, we examined the effect of hyperparameter optimisation on classification accuracy and computational expense. The results showed that RF marginally outperformed XGBoost when using all wavebands (p = 176) and optimised hyperparameter values. RF yielded a test accuracy of 83.3% (KHAT = 0.67), whereas XGBoost yielded a test accuracy of 81.7% (KHAT = 0.63). Our results further show that optimising hyperparameter values yielded an overall increase in test accuracy, ranging from 0.8% to 5.0%, for both RF and XGBoost. Overall, the results highlight the effect of feature selection and optimisation on the performance of machine learning ensembles for modelling vineyard water stress.
{"title":"A feature selection approach for terrestrial hyperspectral image analysis","authors":"Kyle Loggenberg, Nitesh K. Poona","doi":"10.4314/sajg.v9i2.20","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.20","url":null,"abstract":"Feature selection techniques are often employed for reducing data dimensionality, improving computational efficiency, and most importantly for selecting a subset of the most important features for model building. The present study explored the utility of a Filter-Wrapper (FW) approach for feature selection using terrestrial hyperspectral remote sensing imagery. The efficacy of the FW approach was evaluated in conjunction with the Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers, to discriminate between water-stressed and non-stressed Shiraz vines. The proposed FW approach yielded a test accuracy of 80.0% (KHAT = 0.6) for both RF and XGBoost, outperforming the more traditional Kruskal-Wallis (KW) filter by more than 20%. The FW approach was also less computationally expensive when compared with the more commonly used Sequential Floating Forward Selection (SFFS) wrapper. Additionally, we examined the effect of hyperparameter optimisation on classification accuracy and computational expense. The results showed that RF marginally outperformed XGBoost when using all wavebands (p = 176) and optimised hyperparameter values. RF yielded a test accuracy of 83.3% (KHAT = 0.67), whereas XGBoost yielded a test accuracy of 81.7% (KHAT = 0.63). Our results further show that optimising hyperparameter values yielded an overall increase in test accuracy, ranging from 0.8% to 5.0%, for both RF and XGBoost. Overall, the results highlight the effect of feature selection and optimisation on the performance of machine learning ensembles for modelling vineyard water stress.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43361580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bathymetry is the science to study and measure the depths of the ocean floor. The differences in water depth, underwater slope and ocean floor structure were investigated using a geographic information system (GIS). This article investigates changes to the hydrodynamic sedimentation processes in Saldanha Bay as a result of the harbour constructions that took place in the early 1970s. The construction of the harbour included the massive dredging operations and resultant relocation of 30 million m3 of soil. Bathymetric data from Saldanha Bay before (1957) and after (1977) the construction of the harbour in the early seventies were analysed and compared in ArcGIS. It was found that the overall depth of the inner part of Saldanha Bay increased with about 1.4 m and that the bottom and slopes were smoothed. The physical structure that was constructed for the harbour had a serious impact on the hydrodynamic sedimentation processes. It was found that these interventions altered the depth profile of the Bay and the article finally draws conclusions on coastal and beach formation, based on the change in the hydrodynamic sedimentation processes of Saldanha Bay.
{"title":"Determining the change in the bathymetry of Saldanha Bay due to the harbour construction in the seventies","authors":"Ivan Henrico, J. Bezuidenhout","doi":"10.4314/sajg.v9i2.16","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.16","url":null,"abstract":"Bathymetry is the science to study and measure the depths of the ocean floor. The differences in water depth, underwater slope and ocean floor structure were investigated using a geographic information system (GIS). This article investigates changes to the hydrodynamic sedimentation processes in Saldanha Bay as a result of the harbour constructions that took place in the early 1970s. The construction of the harbour included the massive dredging operations and resultant relocation of 30 million m3 of soil. Bathymetric data from Saldanha Bay before (1957) and after (1977) the construction of the harbour in the early seventies were analysed and compared in ArcGIS. It was found that the overall depth of the inner part of Saldanha Bay increased with about 1.4 m and that the bottom and slopes were smoothed. The physical structure that was constructed for the harbour had a serious impact on the hydrodynamic sedimentation processes. It was found that these interventions altered the depth profile of the Bay and the article finally draws conclusions on coastal and beach formation, based on the change in the hydrodynamic sedimentation processes of Saldanha Bay.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46129192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Urbanisation is accelerating urban land use dynamics and this has a significant impact on land surface temperature (LST). Impervious surfaces and increase in air pollution has led to the increase in land surface temperature. This study reports on the use of geospatial technologies to monitor and quantify changes in LST using remotely sensed data in the City of Tshwane. Land surface temperature was retrieved using the winter and summer Landsat datasets for 1997 and 2015 and the MODIS data from 2000 to 2015. Land surface temperature was extracted using emissivity and satellite temperature as input parameters. The spatial and temporal variations in the LST were retrieved to show the effects of land cover change on LST. Change in LST was also analysed on different land cover types using transects across the study area. The study revealed an increase in land surface temperature between the years. It also showed that impervious surfaces had a higher LST compared to the non-impervious surfaces. The results revealed variations in LST between non-cropped and cropped agricultural areas, where the former had higher LST than the latter. Temporal trends revealed a notable increase in LST in the urban areas and there were some seasonal variations in LST with high LST values in summer and low values in winter. Cross-section analysis along transects revealed spatio-temporal thermal variations across different land cover types.
{"title":"Spatio-temporal variations of land surface temperature using Landsat and MODIS: case study of the City of Tshwane, South Africa","authors":"J. Magidi, F. Ahmed","doi":"10.4314/sajg.v9i2.25","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.25","url":null,"abstract":"Urbanisation is accelerating urban land use dynamics and this has a significant impact on land surface temperature (LST). Impervious surfaces and increase in air pollution has led to the increase in land surface temperature. This study reports on the use of geospatial technologies to monitor and quantify changes in LST using remotely sensed data in the City of Tshwane. Land surface temperature was retrieved using the winter and summer Landsat datasets for 1997 and 2015 and the MODIS data from 2000 to 2015. Land surface temperature was extracted using emissivity and satellite temperature as input parameters. The spatial and temporal variations in the LST were retrieved to show the effects of land cover change on LST. Change in LST was also analysed on different land cover types using transects across the study area. The study revealed an increase in land surface temperature between the years. It also showed that impervious surfaces had a higher LST compared to the non-impervious surfaces. The results revealed variations in LST between non-cropped and cropped agricultural areas, where the former had higher LST than the latter. Temporal trends revealed a notable increase in LST in the urban areas and there were some seasonal variations in LST with high LST values in summer and low values in winter. Cross-section analysis along transects revealed spatio-temporal thermal variations across different land cover types.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43822635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. D. Villiers, C. Munghemezulu, G. Chirima, Philemon Tsele, Zinhle Mashaba
Invasive alien plants (IAPs) are responsible for loss in biodiversity and the depletion of water resources in natural ecosystems. Prosopis species are IAPs previously introduced by farmers to provide shade and fodder for livestock. In the Northern Cape, Prosopis spp. invasions are associated with the loss of native species resulting in overgrazing and degrading rangelands. Mapping Prosopis glandulosa is essential for management initiatives to assist the government in minimising the spread and impact of IAPs. This study aims to evaluate the performance of two machine learning algorithms i.e., Support Vector Machine (SVM) and Random Forest (RF) to map the spatial dynamics of P. glandulosa in Prieska. The spatial invasion extent of P. glandulosa was mapped using multitemporal Landsat data spanning the period from 1990 to 2018. Validation of the results was done through an estimated error matrix with the use of the proportion of area and the estimates of overall accuracy, user’s accuracy and producer’s accuracy with a 95% confidence interval. The performance of the SVM and RF classifiers showed similar results in the overall accuracy and Kappa statistics throughout the years. These methods showed an overall increase of at least 3.3% of the area invaded by P. glandulosa from 1990 to 2018. The study indicates the importance of Landsat imagery for mapping historical and current land cover change of IAPs. The spread of P. glandulosa was confirmed by an increase in the total area of invasion, which enables decision-makers to improve monitoring and eradication initiatives.
{"title":"Machine learning algorithms for mapping Prosopis glandulosa and land cover change using multi-temporal Landsat products: a case study of Prieska in the Northern Cape Province, South Africa","authors":"C. D. Villiers, C. Munghemezulu, G. Chirima, Philemon Tsele, Zinhle Mashaba","doi":"10.4314/SAJG.V9I2.13","DOIUrl":"https://doi.org/10.4314/SAJG.V9I2.13","url":null,"abstract":"Invasive alien plants (IAPs) are responsible for loss in biodiversity and the depletion of water resources in natural ecosystems. Prosopis species are IAPs previously introduced by farmers to provide shade and fodder for livestock. In the Northern Cape, Prosopis spp. invasions are associated with the loss of native species resulting in overgrazing and degrading rangelands. Mapping Prosopis glandulosa is essential for management initiatives to assist the government in minimising the spread and impact of IAPs. This study aims to evaluate the performance of two machine learning algorithms i.e., Support Vector Machine (SVM) and Random Forest (RF) to map the spatial dynamics of P. glandulosa in Prieska. The spatial invasion extent of P. glandulosa was mapped using multitemporal Landsat data spanning the period from 1990 to 2018. Validation of the results was done through an estimated error matrix with the use of the proportion of area and the estimates of overall accuracy, user’s accuracy and producer’s accuracy with a 95% confidence interval. The performance of the SVM and RF classifiers showed similar results in the overall accuracy and Kappa statistics throughout the years. These methods showed an overall increase of at least 3.3% of the area invaded by P. glandulosa from 1990 to 2018. The study indicates the importance of Landsat imagery for mapping historical and current land cover change of IAPs. The spread of P. glandulosa was confirmed by an increase in the total area of invasion, which enables decision-makers to improve monitoring and eradication initiatives.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43790294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Idowu, R. Waswa, K. Lasisi, M. Nyadawa, V. Okumu
In the wake of the burgeoning population, socio-economic and environmental issues facing coastal areas, LULC change detection has become an essential tool for environmental monitoring towards achieving sustainable development. In this study, an object-based image analysis approach using post-classification comparison technique was applied for assessing the LULC of the coastal city of Lagos from 1986 to 2016. The study describes how satellite imagery from different sources (Landsat and SENTINEL 2A) can be successfully integrated for Land use Land cover change detection. The results show that between 1986 and 2016, there were net increases in bare areas, built-up areas, and shrublands and a general decline in forestlands, waterbodies and wetlands. Over 60,000ha cover (approx. 190% increase) was converted into built-up areas while 83,541ha (835.4km2) of forestland were lost, suggesting high rates of urbanization and corresponding deforestation. About 60% loss of wetlands was also observed in the same time period. The decrease in water bodies and a steady increase in bare and built-up areas are possibly due to the prevalent land reclamation activities in the study area. Higher rates of deforestation and increase in bare areas were observed from 2001 to 2016 in comparison to 1986 to 2001. The observed trends are likely to continue, and for future management actions, predictive studies are suggested to provide more empirical evidence.
{"title":"Object-based land use/land cover change detection of a coastal city using Multi-Source Imagery: a case study of Lagos, Nigeria","authors":"T. Idowu, R. Waswa, K. Lasisi, M. Nyadawa, V. Okumu","doi":"10.4314/sajg.v9i2.10","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.10","url":null,"abstract":"In the wake of the burgeoning population, socio-economic and environmental issues facing coastal areas, LULC change detection has become an essential tool for environmental monitoring towards achieving sustainable development. In this study, an object-based image analysis approach using post-classification comparison technique was applied for assessing the LULC of the coastal city of Lagos from 1986 to 2016. The study describes how satellite imagery from different sources (Landsat and SENTINEL 2A) can be successfully integrated for Land use Land cover change detection. The results show that between 1986 and 2016, there were net increases in bare areas, built-up areas, and shrublands and a general decline in forestlands, waterbodies and wetlands. Over 60,000ha cover (approx. 190% increase) was converted into built-up areas while 83,541ha (835.4km2) of forestland were lost, suggesting high rates of urbanization and corresponding deforestation. About 60% loss of wetlands was also observed in the same time period. The decrease in water bodies and a steady increase in bare and built-up areas are possibly due to the prevalent land reclamation activities in the study area. Higher rates of deforestation and increase in bare areas were observed from 2001 to 2016 in comparison to 1986 to 2001. The observed trends are likely to continue, and for future management actions, predictive studies are suggested to provide more empirical evidence.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43469870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spatial Data Infrastructure (SDI) provides a platform for spatial data sharing and is a key for sustainable development. Developing countries, including Tanzania, are at different stages of implementing SDIs. The importance and advantage of implementation lie in the fact that considerable funds can be saved by avoiding duplication of data, and improving quality of decisions making as well as public services. However, SDI is very complex in nature, including many influencing factors and different stakeholders. This paper investigates the possibilities of using Agent-Based Modelling (ABM) for simulating an SDI development process in Tanzania, for better understanding and making better planning. The roles and actions of organizations were identified through interviews, and the results were analysed. The behaviour of individual organizations (stakeholders) while interacting with the system were observed and analysed. The growth results in terms of data availability, standards, and data sharing for each organization were plotted and priority tables were generated. The model was evaluated for consistency and the results were judged to be within a reasonable range. The ABM simulation depicted the main attributes of agents, their roles and their interactions while pursuing SDI development in Tanzania. The results will help SDI planners and stakeholders to understand the roles of partners and prioritize activities and actions for successful SDI implementation.
{"title":"Investigating an agent based modelling approach for SDI planning: A case study of Tanzania NSDI development","authors":"Alex Lubida, M. Rajabi, P. Pilesjö, A. Mansourian","doi":"10.4314/SAJG.V9I2.14","DOIUrl":"https://doi.org/10.4314/SAJG.V9I2.14","url":null,"abstract":"Spatial Data Infrastructure (SDI) provides a platform for spatial data sharing and is a key for sustainable development. Developing countries, including Tanzania, are at different stages of implementing SDIs. The importance and advantage of implementation lie in the fact that considerable funds can be saved by avoiding duplication of data, and improving quality of decisions making as well as public services. However, SDI is very complex in nature, including many influencing factors and different stakeholders. This paper investigates the possibilities of using Agent-Based Modelling (ABM) for simulating an SDI development process in Tanzania, for better understanding and making better planning. The roles and actions of organizations were identified through interviews, and the results were analysed. The behaviour of individual organizations (stakeholders) while interacting with the system were observed and analysed. The growth results in terms of data availability, standards, and data sharing for each organization were plotted and priority tables were generated. The model was evaluated for consistency and the results were judged to be within a reasonable range. The ABM simulation depicted the main attributes of agents, their roles and their interactions while pursuing SDI development in Tanzania. The results will help SDI planners and stakeholders to understand the roles of partners and prioritize activities and actions for successful SDI implementation.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48142849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-23DOI: 10.20944/preprints202007.0560.v1
T. Idowu, R. Waswa, K. Lasisi, Kenneth Mubea, M. Nyadawa, J. Kiema
The most extensive urban growths in the next 30 years are expected to occur in developing countries. Lagos, Nigeria - Africa’s second most populous megacity- is a prime example. To achieve more sustainable and resilient cities, there is a need for modeling the urban growth patterns of major cities and analyzing their implications. In this study, the urban growth of Lagos state was modeled using the Multi-Layer Perceptron (MLP) neural network for the transition modeling and the Markov Chain analysis for the change prediction, achieving a model accuracy of 81.8%. An innovative visual validation of the model results using the ArcGIS was combined with kappa correlation statistics. The results show that by 2031, built-up areas will be the most spatially extensive LULC class in the study area with percentage coverage of 34.1% as opposed to 9% in 1986. The coverage of bare areas is also expected to increase by 53% between 2016 and 2031. Conversely, 24.9% and 68.3% loss of forestlands and wetlands respectively, are expected between 2016 and 2031. In view of the 11th goal of SDGs which focuses on achieving sustainable cities and communities, the objectives of African Union’s Agenda 2063, and based on the urban growth trends observed, the study recommends a prioritization of vertical expansion as opposed to the current horizontal urban growth trends in the study area.
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