Jamal Jokar Arsanjani
Biographic Data
| ID | 5246007 |
|---|---|
| NAME | Jamal Jokar Arsanjani |
| GIVEN NAMES | Jamal Jokar |
| FAMILY NAME | Arsanjani |
| SIGNATURE | ARSANJANI J J |
| AFFILIATIONS | Aalborg University |
| ORCID | 0000-0001-6347-2935 |
| VERIFIED | Yes |
| TOTAL WORKS | 18 |
| TOTAL CITATIONS | 53 |
| AUTHOR COUNT | 18 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2013 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 4 |
A Scenario-Based Framework to Optimising Eco-Wellness Tourism Development and Creating Niche Markets
Decision-making and planning in eco-wellness tourism can vary depending on time, resources, and the perspectives of stakeholders, as it is often challenging to generalize the results of decision-making models across different scenarios. Hence, the primary objective of this study was to propose a scenario-based framework for optimising eco-wellness tourism development. For this purpose, maps of 26 factors affecting the evaluation of nature-based e…
Assessing the Effect of Urban Growth on Surface Ecological Status Using Multi-Temporal Satellite Imagery
Quantification of Surface Ecological Status (SES) changes is of great importance for understanding human exposure and adaptability to the environment. This study aims to assess the effect of urban growth on spatial and temporal changes of SES over a set of neighboring Iranian cities, Amol, Babol, Qaemshahr, and Sari, which are located in moderate and humid climate conditions. Firstly, the built-up footprint was prepared using Landsat images based…
Landslide Susceptibility Mapping Using Machine Learning
Mapping of landslides, conducted in 2021 by the Geological Survey of Denmark and Greenland (GEUS), revealed 3202 landslides in Denmark, indicating that they might pose a bigger problem than previously acknowledged. Moreover, the changing climate is assumed to have an impact on landslide occurrences in the future. The aim of this study is to conduct the first landslide susceptibility mapping (LSM) in Denmark, reducing the geographical bias existin…
A Spatial Decision Support Approach for Flood Vulnerability Analysis in Urban Areas
Preparedness against floods in a hazard management perspective plays a major role in the pre-event phase. Hence, assessing urban vulnerability and resilience towards floods for different risk scenarios is a prerequisite for urban planners and decision makers. Therefore, the main objective of this study is to propose the design and implementation of a spatial decision support tool for mapping flood vulnerability in the metropolis of Tehran under d…
Perspectives on “Earth Observation and GIScience for Agricultural Applications”
Current and future scenarios for global agricultural systems under a changing climate require innovative approaches, novel datasets, and methods for improving environmental resource management and better data-driven decision-making [...]
Prediction of Groundwater Level Variations in a Changing Climate
Shallow groundwater is a key resource for human activities and ecosystems, and is susceptible to alterations caused by climate change, causing negative socio-economic and environmental impacts, and increasing the need to predict the evolution of the water table. The main objective of this study is to gain insights about future water level changes based on different climate change scenarios using machine learning algorithms, while addressing the f…
The Geography of the Covid-19 Pandemic
The Covid-19 pandemic emerged and evolved so quickly that societies were not able to respond quickly enough, mainly due to the nature of the Covid-19 virus' rate of spread and also the largely open societies that we live in. While we have been willingly moving towards open societies and reducing movement barriers, there is a need to be prepared for minimizing the openness of society on occasions such as large pandemics, which are low probability …
Deep Learning for Detecting and Classifying Ocean Objects
Synthetic aperture radar (SAR) plays a remarkable role in ocean surveillance, with capabilities of detecting oil spills, icebergs, and marine traffic both at daytime and at night, regardless of clouds and extreme weather conditions. The detection of ocean objects using SAR relies on well-established methods, mostly adaptive thresholding algorithms. In most waters, the dominant ocean objects are ships, whereas in arctic waters the vast majority of…
A geographical direction-based approach for capturing the local variation of urban expansion in the application of CA-Markov model
Development of a cellular automata model using open source technologies for monitoring urbanisation in the global south
Spatial data for slum upgrading
Urban change in Goa, India
Analyzing crop change scenario with the SmartScapeTM spatial decision support system
GlobeLand30 as an alternative fine-scale global land cover map
Crowdsourced mapping of land use in urban dense environments
Geo‐located information is increasingly important for regional decision making and spatial assessment. Toronto has witnessed rapid demographic and economic change over the last decades, making the Toronto region the fourth largest economic centre in North America. From a policymaker's perspective, understanding land use for planning purposes is critical for better urban planning. Such information is, however, conditioned by classical surveying an…
Spatial eigenvector filtering for spatiotemporal crime mapping and spatial crime analysis
Spatial and spatiotemporal analyses are exceedingly relevant to determine criminogenic factors. The estimation of Poisson and negative binomial models (NBM) is complicated by spatial autocorrelation. Therefore, first, eigenvector spatial filtering (ESF) is introduced as a method for spatiotemporal mapping to uncover time-invariant crime patterns. Second, it is demonstrated how ESF is effectively used in criminology to invalidate model misspecific…
Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion
Spatiotemporal simulation of urban growth patterns using agent-based modeling
Spatiotemporal simulation of urban growth patterns using agent-based modeling
Spatial eigenvector filtering for spatiotemporal crime mapping and spatial crime analysis
Spatial and spatiotemporal analyses are exceedingly relevant to determine criminogenic factors. The estimation of Poisson and negative binomial models (NBM) is complicated by spatial autocorrelation. Therefore, first, eigenvector spatial filtering (ESF) is introduced as a method for spatiotemporal mapping to uncover time-invariant crime patterns. Second, it is demonstrated how ESF is effectively used in criminology to invalidate model misspecific…
A geographical direction-based approach for capturing the local variation of urban expansion in the application of CA-Markov model
GlobeLand30 as an alternative fine-scale global land cover map
Spatial data for slum upgrading
Analyzing crop change scenario with the SmartScapeTM spatial decision support system
Development of a cellular automata model using open source technologies for monitoring urbanisation in the global south
Crowdsourced mapping of land use in urban dense environments
Geo‐located information is increasingly important for regional decision making and spatial assessment. Toronto has witnessed rapid demographic and economic change over the last decades, making the Toronto region the fourth largest economic centre in North America. From a policymaker's perspective, understanding land use for planning purposes is critical for better urban planning. Such information is, however, conditioned by classical surveying an…
Urban change in Goa, India
Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion
Spatiotemporal simulation of urban growth patterns using agent-based modeling
Spatial eigenvector filtering for spatiotemporal crime mapping and spatial crime analysis
Spatial and spatiotemporal analyses are exceedingly relevant to determine criminogenic factors. The estimation of Poisson and negative binomial models (NBM) is complicated by spatial autocorrelation. Therefore, first, eigenvector spatial filtering (ESF) is introduced as a method for spatiotemporal mapping to uncover time-invariant crime patterns. Second, it is demonstrated how ESF is effectively used in criminology to invalidate model misspecific…
Crowdsourced mapping of land use in urban dense environments
Geo‐located information is increasingly important for regional decision making and spatial assessment. Toronto has witnessed rapid demographic and economic change over the last decades, making the Toronto region the fourth largest economic centre in North America. From a policymaker's perspective, understanding land use for planning purposes is critical for better urban planning. Such information is, however, conditioned by classical surveying an…
Analyzing crop change scenario with the SmartScapeTM spatial decision support system
GlobeLand30 as an alternative fine-scale global land cover map
Urban change in Goa, India
Development of a cellular automata model using open source technologies for monitoring urbanisation in the global south
Spatial data for slum upgrading
A geographical direction-based approach for capturing the local variation of urban expansion in the application of CA-Markov model
Deep Learning for Detecting and Classifying Ocean Objects
Synthetic aperture radar (SAR) plays a remarkable role in ocean surveillance, with capabilities of detecting oil spills, icebergs, and marine traffic both at daytime and at night, regardless of clouds and extreme weather conditions. The detection of ocean objects using SAR relies on well-established methods, mostly adaptive thresholding algorithms. In most waters, the dominant ocean objects are ships, whereas in arctic waters the vast majority of…
Prediction of Groundwater Level Variations in a Changing Climate
Shallow groundwater is a key resource for human activities and ecosystems, and is susceptible to alterations caused by climate change, causing negative socio-economic and environmental impacts, and increasing the need to predict the evolution of the water table. The main objective of this study is to gain insights about future water level changes based on different climate change scenarios using machine learning algorithms, while addressing the f…
The Geography of the Covid-19 Pandemic
The Covid-19 pandemic emerged and evolved so quickly that societies were not able to respond quickly enough, mainly due to the nature of the Covid-19 virus' rate of spread and also the largely open societies that we live in. While we have been willingly moving towards open societies and reducing movement barriers, there is a need to be prepared for minimizing the openness of society on occasions such as large pandemics, which are low probability …
Landslide Susceptibility Mapping Using Machine Learning
Mapping of landslides, conducted in 2021 by the Geological Survey of Denmark and Greenland (GEUS), revealed 3202 landslides in Denmark, indicating that they might pose a bigger problem than previously acknowledged. Moreover, the changing climate is assumed to have an impact on landslide occurrences in the future. The aim of this study is to conduct the first landslide susceptibility mapping (LSM) in Denmark, reducing the geographical bias existin…
A Spatial Decision Support Approach for Flood Vulnerability Analysis in Urban Areas
Preparedness against floods in a hazard management perspective plays a major role in the pre-event phase. Hence, assessing urban vulnerability and resilience towards floods for different risk scenarios is a prerequisite for urban planners and decision makers. Therefore, the main objective of this study is to propose the design and implementation of a spatial decision support tool for mapping flood vulnerability in the metropolis of Tehran under d…
Perspectives on “Earth Observation and GIScience for Agricultural Applications”
Current and future scenarios for global agricultural systems under a changing climate require innovative approaches, novel datasets, and methods for improving environmental resource management and better data-driven decision-making [...]
Assessing the Effect of Urban Growth on Surface Ecological Status Using Multi-Temporal Satellite Imagery
Quantification of Surface Ecological Status (SES) changes is of great importance for understanding human exposure and adaptability to the environment. This study aims to assess the effect of urban growth on spatial and temporal changes of SES over a set of neighboring Iranian cities, Amol, Babol, Qaemshahr, and Sari, which are located in moderate and humid climate conditions. Firstly, the built-up footprint was prepared using Landsat images based…
A Scenario-Based Framework to Optimising Eco-Wellness Tourism Development and Creating Niche Markets
Decision-making and planning in eco-wellness tourism can vary depending on time, resources, and the perspectives of stakeholders, as it is often challenging to generalize the results of decision-making models across different scenarios. Hence, the primary objective of this study was to propose a scenario-based framework for optimising eco-wellness tourism development. For this purpose, maps of 26 factors affecting the evaluation of nature-based e…
Geography (15 works) · Computer Science (12 works) · Land Use and Ecosystem Services (11 works) · Environmental Science (9 works) · Environmental planning (8 works) · Engineering (7 works) · Cartography (6 works) · Environmental resource management (6 works) · Regional science (6 works) · Ecology (5 works)