Environmental monitoring and management systems in most cases deal with models and spatial analyt... more Environmental monitoring and management systems in most cases deal with models and spatial analytics that involve the integration of in-situ and remote sensor observations. In-situ sensor observations and those gathered by remote sensors are usually provided by different databases and services in real-time dynamic service systems like the Geo-Web Services. Thus, data have to be pulled from different databases and transferred over the web before they are fused and processed on the service middleware. This process is very massive and unnecessary communication and work load on the service, especially when retrieving massive raster coverage data. Thus in this research, we propose a database model for heterogeneous sensortypes that enables geo-scientific processing and spatial analytics involving remote and in-situ
Fault identification is one of the most significant bottlenecks faced by electricity transmission... more Fault identification is one of the most significant bottlenecks faced by electricity transmission and distribution utilities in developing countries to deliver efficient services to the customers and ensure proper asset audit and management for network optimization and load forecasting. This is due to data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and general human cost. In view of this, we exploited the use of oblique UAV imagery with a high spatial resolution and a fine-tuned and deep Convolutional Neural Networks (CNNs) to monitor four major Electric power transmission network (EPTN) components. This study explored the capability of the Single Shot Multibox Detector (SSD), a one-stage object detection model on the electric transmission power line imagery to localize, detect and classify faults. The fault considered in this study include the broken insulator plate, missing insulator plate, missing knob, and rusty clamp. Our adapted ne...
Understanding the biases in Deep Neural Networks (DNN) based algorithms is gaining paramount impo... more Understanding the biases in Deep Neural Networks (DNN) based algorithms is gaining paramount importance due to its increased applications on many real-world problems. A known problem of DNN penalizing the underrepresented population could undermine the efficacy of development projects dependent on data produced using DNN-based models. In spite of this, the problems of biases in DNN for Land Use and Land Cover Classification (LULCC) have not been a subject of many studies. In this study, we explore ways to quantify biases in DNN for land use with an example of identifying school buildings in Colombia from satellite imagery. We implement a DNN-based model by fine-tuning an existing, pre-trained model for school building identification. The model achieved overall 84% accuracy. Then, we used socioeconomic covariates to analyze possible biases in the learned representation. The retrained deep neural network was used to extract visual features (embeddings) from satellite image tiles. The ...
Computer vision for large scale building detection can be very challenging in many environments a... more Computer vision for large scale building detection can be very challenging in many environments and settings even with recent advances in deep learning technologies. Even more challenging is modeling to detect the presence of specific buildings (in this case schools) in satellite imagery at a global scale. However, despite the variation in school building structures from rural to urban areas and from country to country, many school buildings have identifiable overhead signatures that make them possible to be detected from high-resolution imagery with modern deep learning techniques. Our hypothesis is that a Deep Convolutional Neural Network (CNN) could be trained for successful mapping of school locations at a regional or global scale from high-resolution satellite imagery. One of the key objectives of this work is to explore the possibility of having a scalable model that can be used to map schools across the globe. In this work, we developed AI-assisted rapid school location mappi...
The study of the dynamic relationship between topological structure of a transit network and the ... more The study of the dynamic relationship between topological structure of a transit network and the mobility patterns of transit vehicles on this network is critical towardsdevising smart and time-aware solutions to transit management and recommendation systems. This paper proposes a time-varying graph (TVG) to model thisrelationship. The effectiveness of this proposed model has been explored by implementing the model in Neo4j graph database using transit feeds generated by bus transit network of the City of Moncton, New Brunswick, Canada. Dynamics in this relationshipalsohave been detected using network metrics such as temporal shortest paths, degree, betweenness and PageRank centralities as well as temporal network diameter and density. Keywords: Transit Networks,Mobility Pattern,Time-Varying Graph model, Graph Databaseand Graph Analytics Keywords: Transit Networks,Mobility Pattern,Time-Varying Graph model, Graph Database and Graph Analytics
The purposes of this study were to identify the living environment in rural fishing area and to s... more The purposes of this study were to identify the living environment in rural fishing area and to suggest checking methods for implementation performance. Rural service standard is the key factor of rural development in Korea. In 2012, The first Implementations of performance was announced. The results were presented to the unit by the City and the County. Because of Fishing villages exists as a unit by the haengjeongri, It is difficult to know the status of the fishing villages by the Rural service standard. In order to look for the actual conditions in rural fishing village it was investigated in the 100 Eochongye. The data used in the analysis is 577 questionnaires. Analysis showed that rural fishing areas were superior to general state of rural in the 8 items of rural service standard. Especially housing, transportation and health care sector in rural fishing area wes better than general state of rural. But Public safety and order is relatively poor. This is because Fishing village contains islands. Presenting to improve rural service standard based on the results of research, The items of rural service standard should be measured the actual residents' accessibility than opportunity of the public service. and after setting the rural service standard clearly related to the quality of life of residents in each sector, Accessibility aspects of the customer for the public services should be considered. Checking the performance for the unit by the City and the County should be replaced as a living zone in order to consider the facilities using nearby.
Evolutionary graph analytics have attracted attention from many research communities with the mai... more Evolutionary graph analytics have attracted attention from many research communities with the main purpose of understanding the changing pattern of real-world networks through evolutionary analysis of graph metrics and dynamic interactions between entities. Graphs of real-world networks evolve as new nodes and edges continually appear and disappear in the structure but, more importantly, their metrics such as density, average path length and network diameter also evolve. Uncovering and understanding hidden patterns in an evolving network requires evolutionary analysis of the network over different temporal resolutions. Evolutionary graph analytics have been explored for use in different types of networks including web citation and co-authorship networks [1-4], online social networks [5-10], biology and disease networks [11-14], as well as in communication networks [15-20]. All networks do not evolve at the same rate; some
International Journal of Geographical Information Science, 2019
Modelling topological relationships between places and events is challenging especially because t... more Modelling topological relationships between places and events is challenging especially because these relationships are dynamic, and their evolutionary analysis relies on the explanatory power of representing their interactions across different temporal resolutions. In this paper, we introduce the Space-Time Varying Graph (STVG) based on the whole graph approach that combines directed and bipartite subgraphs with a time-tree for representing the complex interaction between places and events across time. We demonstrate how the proposed STVG can be exploited to identify and extract evolutionary patterns of traffic accidents using graph metrics, ad-hoc graph queries and clustering algorithms. The results reveal evolutionary patterns that uncover the places with high incidence of accidents over different time resolutions, reveal the main reasons why the traffic accidents have occurred, and disclose evolving communities of densely connected traffic accidents over time.
GPS-equipped public transit vehicles generate a massive amount of location information, yet analy... more GPS-equipped public transit vehicles generate a massive amount of location information, yet analytical methods based on Geographic Information System and Relational Database Management Systems are limited in their ability to handle these data for transit performance assessment. Graph analytics approach appears well suited for addressing these limitations; however, existing graph data models that have been used to represent the transit network do not provide the flexibility to incorporate mobility context from Automatic Vehicle Location feeds with the geographic context of the network. This research work presents a new graph model that accounts for the mobility and geographical contexts of transit networks yet capable of processing a large volume of Automatic Vehicle Location data feeds for transit performance assessment. The efficacy of the proposed graph model and analytics method has been evaluated using Automatic Vehicle Location feeds at 5 second intervals over a period of 2 weeks from the bus transit network serving the communities of Greater Moncton, New Brunswick, Canada. The results demonstrate the effectiveness of using simple graph queries to retrieve operational-level performance indicators such as schedule adherence, bus stops and routes activity levels.
Environmental monitoring and management systems in most cases deal with models and spatial analyt... more Environmental monitoring and management systems in most cases deal with models and spatial analytics that involve the integration of in-situ and remote sensor observations. In-situ sensor observations and those gathered by remote sensors are usually provided by different databases and services in real-time dynamic service systems like the Geo-Web Services. Thus, data have to be pulled from different databases and transferred over the web before they are fused and processed on the service middleware. This process is very massive and unnecessary communication and work load on the service, especially when retrieving massive raster coverage data. Thus in this research, we propose a database model for heterogeneous sensortypes that enables geo-scientific processing and spatial analytics involving remote and in-situ
Environmental monitoring and management systems in most cases deal with models and spatial analyt... more Environmental monitoring and management systems in most cases deal with models and spatial analytics that involve the integration of in-situ and remote sensor observations. In-situ sensor observations and those gathered by remote sensors are usually provided by different databases and services in real-time dynamic services such as the Geo-Web Services. Thus, data have to be pulled from different databases and transferred over the network before they are fused and processed on the service middleware. This process is very massive and unnecessary communication and work load on the service. Massive work load in large raster downloads from flat-file raster data sources each time a request is made and huge integration and geo-processing work load on the service middleware which could actually be better leveraged at the database level. In this paper, we propose and present a heterogeneous sensor database framework or model for integration, geo-processing and spatial analysis of remote and ...
The interplay between Geographical Information System (GIS) and Computer Science has continued to... more The interplay between Geographical Information System (GIS) and Computer Science has continued to yield improved methods of carrying out many surveying-related activities. In the past, survey control points were stored in file systems and at the best in Database Management applications thereby leading to the limited usage of the survey control points since they are difficult to locate in the field. This study however, suggests another approach for the storage of these survey control points which makes them to be easily accessible and gives room for faster update and geo-visualization of the survey control points. This was achieved by means of web programming applications such as Node-JS, Leaflet Javascript Mapping API, MONGODB, HTML and CSS, integrating GIS into web technologies. The end product is an interactive web application that can be accessed using any smart device with the control points rendered on the user interface. The Survey Control Finder application (E-Beacon) is a We...
Journal of the Indian Society of Remote Sensing, 2016
Soil moisture estimation from satellite earth observation has emerged effectively advantageous du... more Soil moisture estimation from satellite earth observation has emerged effectively advantageous due to the high temporal resolution, spatial resolution, coverage, and processing convenience it affords. In this paper, we present a study carried out to estimate soil moisture level at every location within Enugu State Nigeria from satellite earth observation. Comparative analysis of multiple indices for soil moisture estimation was carried out with a view to evaluating the robustness, correlation, appropriateness and accuracy of the indices in estimating the spatial distribution of soil moisture level in Enugu State. Results were correlated and validated with In-Situ soil moisture observations from multi-sample points. To achieve this, the Topographic Wetness Index (TWI), based on digital elevation data, the Temperature Vegetation Dryness Index (TVDI) and an improved TVDI (iTVDI) incorporating air temperature and a Digital Elevation Model (DEM) were calculated from ASTER global DEM and Landsat images. Possible dependencies of the indices on land cover type, topography, and precipitation were explored. In-Situ soil moisture data were used to validate the derived indices. The results showed that there was a positive significant relationship between iTVDI versus TVDI (R = 0.53, P value \ 0.05), while in iTVDI versus TWI (R = 0.00, P value [ 0.05) and TVDI versus TWI (R =-0.01, P value [ 0.05) no significant relationship existed. There was a strong relationship between iTVDI and topography, land cover type, and precipitation than other indices (TVDI, TWI). In situ measured soil moisture values showed negative significant relationship with TVDI (R =-0.52, P value \ 0.05) and iTVDI (R =-0.63, P value \ 0.05) but not with TWI (R =-0.10, P value [ 0.05). The iTVDI outperformed the other two index; having a stronger relationship with topography, precipitation, land cover classes and soil moisture. It concludes that although iTVDI outperformed other indices (TVDI, TWI) in soil moisture estimation, the decision of which index to apply is dependent on available data, the intent of usage and spatial scale.
Analysis of the dynamic relationship between traffic accident events and road network topology ba... more Analysis of the dynamic relationship between traffic accident events and road network topology based on connectivity and graph analytics offers a new approach to identifying, ranking and profiling traffic accident high risk-locations at different levels of space and time granularities. Previous studies on traffic accident hot spots have mostly adopted spatial statistics and Geographic Information Systems (GIS) where spatial point patterns are discovered based only on spatial dependence with no recognition of the temporal dependence of the events. A limitation arises from the fact that the results are either under or overestimated because of the temporal aggregation of the events to an absolute time point. Furthermore, the existing methods apart from the Network Kernel Density Estimation (NETKDE), consider traffic accident events as events randomly on a 2-D geographic space. However, traffic accident events are network constrained events that happens majorly on the road network space. Therefore, in this paper, we adopt the connectivity of graph on a network space approach that identifies accident high risk-locations based on spacetime-varying connectivity between traffic accident events and the road network geometry. A simple but extensible traffic accident space time-varying graph (STVG) model is developed and implemented for this study. Traffic accident high risk-locations are identified and ranked in space and time using time-dependent degree centrality and PageRank centrality graph metrics respectively through time-incremental graph queries. This study offers urban traffic accident analysts with a new and efficient approach to identify, rank and profile accident-prone areas in space and time at different scales.
Proceedings of the Second ACM/IEEE Symposium on Edge Computing, 2017
Mobility analytics using data generated from the Internet of Mobile Things (IoMT) is facing many ... more Mobility analytics using data generated from the Internet of Mobile Things (IoMT) is facing many challenges which range from the ingestion of data streams coming from a vast number of fog nodes and IoMT devices to avoiding overflowing the cloud with useless massive data streams that can trigger bottlenecks [1]. Managing data flow is becoming an important part of the IoMT because it will dictate in which platform analytical tasks should run in the future. Data flows are usually a sequence of out-of-order tuples with a high data input rate, and mobility analytics requires a real-time flow of data in both directions, from the edge to the cloud, and vice-versa. Before pulling the data streams to the cloud, edge data stream processing is needed for detecting missing, broken, and duplicated tuples in addition to recognize tuples whose arrival time is out of order. Analytical tasks such as data filtering, data cleaning and low-level data contextualization can be executed at the edge of a n...
2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), 2016
The growth of Internet of Things (IoT) brings the promise of a wide range of new recommender syst... more The growth of Internet of Things (IoT) brings the promise of a wide range of new recommender systems due to the expected 57 billion smart connected devices by 2025. In this paper, we propose a new IoT platform for supporting a real-time recommender system. To illustrate the effectiveness of our proposed IoT platform, we present a prototype implementation and a tourism application to demonstrate the entire process from user event data collection to notification/recommendations provision. We conducted several experiments including notification and system performance tests to illustrate the use and performance of our real-time recommender system.
In this article, the correct details of Altunel et al. 2022 should be "Altunel AO, Okolie CJ, Kur... more In this article, the correct details of Altunel et al. 2022 should be "Altunel AO, Okolie CJ, Kurtipek A (2022) Capturing the level of progress in vertical accuracy achieved by ASTER GDEM since the beginning: Turkish and Nigerian examples. Geocarto International, 23 p.,
Environmental monitoring and management systems in most cases deal with models and spatial analyt... more Environmental monitoring and management systems in most cases deal with models and spatial analytics that involve the integration of in-situ and remote sensor observations. In-situ sensor observations and those gathered by remote sensors are usually provided by different databases and services in real-time dynamic service systems like the Geo-Web Services. Thus, data have to be pulled from different databases and transferred over the web before they are fused and processed on the service middleware. This process is very massive and unnecessary communication and work load on the service, especially when retrieving massive raster coverage data. Thus in this research, we propose a database model for heterogeneous sensortypes that enables geo-scientific processing and spatial analytics involving remote and in-situ
Fault identification is one of the most significant bottlenecks faced by electricity transmission... more Fault identification is one of the most significant bottlenecks faced by electricity transmission and distribution utilities in developing countries to deliver efficient services to the customers and ensure proper asset audit and management for network optimization and load forecasting. This is due to data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and general human cost. In view of this, we exploited the use of oblique UAV imagery with a high spatial resolution and a fine-tuned and deep Convolutional Neural Networks (CNNs) to monitor four major Electric power transmission network (EPTN) components. This study explored the capability of the Single Shot Multibox Detector (SSD), a one-stage object detection model on the electric transmission power line imagery to localize, detect and classify faults. The fault considered in this study include the broken insulator plate, missing insulator plate, missing knob, and rusty clamp. Our adapted ne...
Understanding the biases in Deep Neural Networks (DNN) based algorithms is gaining paramount impo... more Understanding the biases in Deep Neural Networks (DNN) based algorithms is gaining paramount importance due to its increased applications on many real-world problems. A known problem of DNN penalizing the underrepresented population could undermine the efficacy of development projects dependent on data produced using DNN-based models. In spite of this, the problems of biases in DNN for Land Use and Land Cover Classification (LULCC) have not been a subject of many studies. In this study, we explore ways to quantify biases in DNN for land use with an example of identifying school buildings in Colombia from satellite imagery. We implement a DNN-based model by fine-tuning an existing, pre-trained model for school building identification. The model achieved overall 84% accuracy. Then, we used socioeconomic covariates to analyze possible biases in the learned representation. The retrained deep neural network was used to extract visual features (embeddings) from satellite image tiles. The ...
Computer vision for large scale building detection can be very challenging in many environments a... more Computer vision for large scale building detection can be very challenging in many environments and settings even with recent advances in deep learning technologies. Even more challenging is modeling to detect the presence of specific buildings (in this case schools) in satellite imagery at a global scale. However, despite the variation in school building structures from rural to urban areas and from country to country, many school buildings have identifiable overhead signatures that make them possible to be detected from high-resolution imagery with modern deep learning techniques. Our hypothesis is that a Deep Convolutional Neural Network (CNN) could be trained for successful mapping of school locations at a regional or global scale from high-resolution satellite imagery. One of the key objectives of this work is to explore the possibility of having a scalable model that can be used to map schools across the globe. In this work, we developed AI-assisted rapid school location mappi...
The study of the dynamic relationship between topological structure of a transit network and the ... more The study of the dynamic relationship between topological structure of a transit network and the mobility patterns of transit vehicles on this network is critical towardsdevising smart and time-aware solutions to transit management and recommendation systems. This paper proposes a time-varying graph (TVG) to model thisrelationship. The effectiveness of this proposed model has been explored by implementing the model in Neo4j graph database using transit feeds generated by bus transit network of the City of Moncton, New Brunswick, Canada. Dynamics in this relationshipalsohave been detected using network metrics such as temporal shortest paths, degree, betweenness and PageRank centralities as well as temporal network diameter and density. Keywords: Transit Networks,Mobility Pattern,Time-Varying Graph model, Graph Databaseand Graph Analytics Keywords: Transit Networks,Mobility Pattern,Time-Varying Graph model, Graph Database and Graph Analytics
The purposes of this study were to identify the living environment in rural fishing area and to s... more The purposes of this study were to identify the living environment in rural fishing area and to suggest checking methods for implementation performance. Rural service standard is the key factor of rural development in Korea. In 2012, The first Implementations of performance was announced. The results were presented to the unit by the City and the County. Because of Fishing villages exists as a unit by the haengjeongri, It is difficult to know the status of the fishing villages by the Rural service standard. In order to look for the actual conditions in rural fishing village it was investigated in the 100 Eochongye. The data used in the analysis is 577 questionnaires. Analysis showed that rural fishing areas were superior to general state of rural in the 8 items of rural service standard. Especially housing, transportation and health care sector in rural fishing area wes better than general state of rural. But Public safety and order is relatively poor. This is because Fishing village contains islands. Presenting to improve rural service standard based on the results of research, The items of rural service standard should be measured the actual residents' accessibility than opportunity of the public service. and after setting the rural service standard clearly related to the quality of life of residents in each sector, Accessibility aspects of the customer for the public services should be considered. Checking the performance for the unit by the City and the County should be replaced as a living zone in order to consider the facilities using nearby.
Evolutionary graph analytics have attracted attention from many research communities with the mai... more Evolutionary graph analytics have attracted attention from many research communities with the main purpose of understanding the changing pattern of real-world networks through evolutionary analysis of graph metrics and dynamic interactions between entities. Graphs of real-world networks evolve as new nodes and edges continually appear and disappear in the structure but, more importantly, their metrics such as density, average path length and network diameter also evolve. Uncovering and understanding hidden patterns in an evolving network requires evolutionary analysis of the network over different temporal resolutions. Evolutionary graph analytics have been explored for use in different types of networks including web citation and co-authorship networks [1-4], online social networks [5-10], biology and disease networks [11-14], as well as in communication networks [15-20]. All networks do not evolve at the same rate; some
International Journal of Geographical Information Science, 2019
Modelling topological relationships between places and events is challenging especially because t... more Modelling topological relationships between places and events is challenging especially because these relationships are dynamic, and their evolutionary analysis relies on the explanatory power of representing their interactions across different temporal resolutions. In this paper, we introduce the Space-Time Varying Graph (STVG) based on the whole graph approach that combines directed and bipartite subgraphs with a time-tree for representing the complex interaction between places and events across time. We demonstrate how the proposed STVG can be exploited to identify and extract evolutionary patterns of traffic accidents using graph metrics, ad-hoc graph queries and clustering algorithms. The results reveal evolutionary patterns that uncover the places with high incidence of accidents over different time resolutions, reveal the main reasons why the traffic accidents have occurred, and disclose evolving communities of densely connected traffic accidents over time.
GPS-equipped public transit vehicles generate a massive amount of location information, yet analy... more GPS-equipped public transit vehicles generate a massive amount of location information, yet analytical methods based on Geographic Information System and Relational Database Management Systems are limited in their ability to handle these data for transit performance assessment. Graph analytics approach appears well suited for addressing these limitations; however, existing graph data models that have been used to represent the transit network do not provide the flexibility to incorporate mobility context from Automatic Vehicle Location feeds with the geographic context of the network. This research work presents a new graph model that accounts for the mobility and geographical contexts of transit networks yet capable of processing a large volume of Automatic Vehicle Location data feeds for transit performance assessment. The efficacy of the proposed graph model and analytics method has been evaluated using Automatic Vehicle Location feeds at 5 second intervals over a period of 2 weeks from the bus transit network serving the communities of Greater Moncton, New Brunswick, Canada. The results demonstrate the effectiveness of using simple graph queries to retrieve operational-level performance indicators such as schedule adherence, bus stops and routes activity levels.
Environmental monitoring and management systems in most cases deal with models and spatial analyt... more Environmental monitoring and management systems in most cases deal with models and spatial analytics that involve the integration of in-situ and remote sensor observations. In-situ sensor observations and those gathered by remote sensors are usually provided by different databases and services in real-time dynamic service systems like the Geo-Web Services. Thus, data have to be pulled from different databases and transferred over the web before they are fused and processed on the service middleware. This process is very massive and unnecessary communication and work load on the service, especially when retrieving massive raster coverage data. Thus in this research, we propose a database model for heterogeneous sensortypes that enables geo-scientific processing and spatial analytics involving remote and in-situ
Environmental monitoring and management systems in most cases deal with models and spatial analyt... more Environmental monitoring and management systems in most cases deal with models and spatial analytics that involve the integration of in-situ and remote sensor observations. In-situ sensor observations and those gathered by remote sensors are usually provided by different databases and services in real-time dynamic services such as the Geo-Web Services. Thus, data have to be pulled from different databases and transferred over the network before they are fused and processed on the service middleware. This process is very massive and unnecessary communication and work load on the service. Massive work load in large raster downloads from flat-file raster data sources each time a request is made and huge integration and geo-processing work load on the service middleware which could actually be better leveraged at the database level. In this paper, we propose and present a heterogeneous sensor database framework or model for integration, geo-processing and spatial analysis of remote and ...
The interplay between Geographical Information System (GIS) and Computer Science has continued to... more The interplay between Geographical Information System (GIS) and Computer Science has continued to yield improved methods of carrying out many surveying-related activities. In the past, survey control points were stored in file systems and at the best in Database Management applications thereby leading to the limited usage of the survey control points since they are difficult to locate in the field. This study however, suggests another approach for the storage of these survey control points which makes them to be easily accessible and gives room for faster update and geo-visualization of the survey control points. This was achieved by means of web programming applications such as Node-JS, Leaflet Javascript Mapping API, MONGODB, HTML and CSS, integrating GIS into web technologies. The end product is an interactive web application that can be accessed using any smart device with the control points rendered on the user interface. The Survey Control Finder application (E-Beacon) is a We...
Journal of the Indian Society of Remote Sensing, 2016
Soil moisture estimation from satellite earth observation has emerged effectively advantageous du... more Soil moisture estimation from satellite earth observation has emerged effectively advantageous due to the high temporal resolution, spatial resolution, coverage, and processing convenience it affords. In this paper, we present a study carried out to estimate soil moisture level at every location within Enugu State Nigeria from satellite earth observation. Comparative analysis of multiple indices for soil moisture estimation was carried out with a view to evaluating the robustness, correlation, appropriateness and accuracy of the indices in estimating the spatial distribution of soil moisture level in Enugu State. Results were correlated and validated with In-Situ soil moisture observations from multi-sample points. To achieve this, the Topographic Wetness Index (TWI), based on digital elevation data, the Temperature Vegetation Dryness Index (TVDI) and an improved TVDI (iTVDI) incorporating air temperature and a Digital Elevation Model (DEM) were calculated from ASTER global DEM and Landsat images. Possible dependencies of the indices on land cover type, topography, and precipitation were explored. In-Situ soil moisture data were used to validate the derived indices. The results showed that there was a positive significant relationship between iTVDI versus TVDI (R = 0.53, P value \ 0.05), while in iTVDI versus TWI (R = 0.00, P value [ 0.05) and TVDI versus TWI (R =-0.01, P value [ 0.05) no significant relationship existed. There was a strong relationship between iTVDI and topography, land cover type, and precipitation than other indices (TVDI, TWI). In situ measured soil moisture values showed negative significant relationship with TVDI (R =-0.52, P value \ 0.05) and iTVDI (R =-0.63, P value \ 0.05) but not with TWI (R =-0.10, P value [ 0.05). The iTVDI outperformed the other two index; having a stronger relationship with topography, precipitation, land cover classes and soil moisture. It concludes that although iTVDI outperformed other indices (TVDI, TWI) in soil moisture estimation, the decision of which index to apply is dependent on available data, the intent of usage and spatial scale.
Analysis of the dynamic relationship between traffic accident events and road network topology ba... more Analysis of the dynamic relationship between traffic accident events and road network topology based on connectivity and graph analytics offers a new approach to identifying, ranking and profiling traffic accident high risk-locations at different levels of space and time granularities. Previous studies on traffic accident hot spots have mostly adopted spatial statistics and Geographic Information Systems (GIS) where spatial point patterns are discovered based only on spatial dependence with no recognition of the temporal dependence of the events. A limitation arises from the fact that the results are either under or overestimated because of the temporal aggregation of the events to an absolute time point. Furthermore, the existing methods apart from the Network Kernel Density Estimation (NETKDE), consider traffic accident events as events randomly on a 2-D geographic space. However, traffic accident events are network constrained events that happens majorly on the road network space. Therefore, in this paper, we adopt the connectivity of graph on a network space approach that identifies accident high risk-locations based on spacetime-varying connectivity between traffic accident events and the road network geometry. A simple but extensible traffic accident space time-varying graph (STVG) model is developed and implemented for this study. Traffic accident high risk-locations are identified and ranked in space and time using time-dependent degree centrality and PageRank centrality graph metrics respectively through time-incremental graph queries. This study offers urban traffic accident analysts with a new and efficient approach to identify, rank and profile accident-prone areas in space and time at different scales.
Proceedings of the Second ACM/IEEE Symposium on Edge Computing, 2017
Mobility analytics using data generated from the Internet of Mobile Things (IoMT) is facing many ... more Mobility analytics using data generated from the Internet of Mobile Things (IoMT) is facing many challenges which range from the ingestion of data streams coming from a vast number of fog nodes and IoMT devices to avoiding overflowing the cloud with useless massive data streams that can trigger bottlenecks [1]. Managing data flow is becoming an important part of the IoMT because it will dictate in which platform analytical tasks should run in the future. Data flows are usually a sequence of out-of-order tuples with a high data input rate, and mobility analytics requires a real-time flow of data in both directions, from the edge to the cloud, and vice-versa. Before pulling the data streams to the cloud, edge data stream processing is needed for detecting missing, broken, and duplicated tuples in addition to recognize tuples whose arrival time is out of order. Analytical tasks such as data filtering, data cleaning and low-level data contextualization can be executed at the edge of a n...
2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), 2016
The growth of Internet of Things (IoT) brings the promise of a wide range of new recommender syst... more The growth of Internet of Things (IoT) brings the promise of a wide range of new recommender systems due to the expected 57 billion smart connected devices by 2025. In this paper, we propose a new IoT platform for supporting a real-time recommender system. To illustrate the effectiveness of our proposed IoT platform, we present a prototype implementation and a tourism application to demonstrate the entire process from user event data collection to notification/recommendations provision. We conducted several experiments including notification and system performance tests to illustrate the use and performance of our real-time recommender system.
In this article, the correct details of Altunel et al. 2022 should be "Altunel AO, Okolie CJ, Kur... more In this article, the correct details of Altunel et al. 2022 should be "Altunel AO, Okolie CJ, Kurtipek A (2022) Capturing the level of progress in vertical accuracy achieved by ASTER GDEM since the beginning: Turkish and Nigerian examples. Geocarto International, 23 p.,
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Papers by Ikechukwu Maduako