Latest Posts

Cellular Automata

In the recently released “The International Encyclopedia of Geography: People, the Earth, Environment, and Technology” I was asked to write a brief entry on “Cellular Automata“. Below is the abstract to my chapter, along some of the images I used in my discussion, the full reference to the chapter.

Abstract: 

Cellular Automata (CA) are a class of models where one can explore how local actions generate global patterns through well specified rules. In such models, decisions are made locally by each cell which are often arranged on a regular lattice and the patterns that emerge, be it urban growth or deforestation are not coordinated centrally but arise from the bottom up. Such patterns emerge through the cell changing its state based on specific transition rules and the states of their surrounding cells. This entry reviews the principles of CA models, provides a background on how CA models have developed, explores a range of applications of where they have been used within the geographical sciences, prior to concluding with future directions for CA modeling. 
The figures below are a sample from the entry, for example, we outline different types of spaces within CA models such as those shown in Figures 1 and 2. We also show how simple rules can lead to the emergence of patterns such as the Game of Life as shown in Figure 3 or  Rule 30 as shown in Figure 4.

Figure 1: Two-Dimensional Cellular Automata Neighborhoods

Figure 2: Voronoi Tessellations Of Space Where Each Polygon Has A Different Number Of Neighbors Based On A Shared Edge.

Figure 3: Example of Cells Changing State from Dead (White) To Alive (Black) Over Time Depending On The States of its Neighboring Cells.

Figure 4: A One-Dimensional CA Model Implementing “Rule 30” Where Successive Iterations Are Presented Below Each Other.

Full Reference:

Crooks, A.T. (2017), Cellular Automata, in Richardson, D., Castree, N., Goodchild, M. F., Kobayashi, A. L., Liu, W. and Marston, R.  (eds.), The International Encyclopedia of Geography: People, the Earth, Environment, and Technology, Wiley Blackwell. DOI: 10.1002/9781118786352.wbieg0578. (pdf)

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Cellular Automata

In the recently released “The International Encyclopedia of Geography: People, the Earth, Environment, and Technology” I was asked to write a brief entry on “Cellular Automata“. Below is the abstract to my chapter, along some of the images I used in my discussion, the full reference to the chapter.

Abstract: 

Cellular Automata (CA) are a class of models where one can explore how local actions generate global patterns through well specified rules. In such models, decisions are made locally by each cell which are often arranged on a regular lattice and the patterns that emerge, be it urban growth or deforestation are not coordinated centrally but arise from the bottom up. Such patterns emerge through the cell changing its state based on specific transition rules and the states of their surrounding cells. This entry reviews the principles of CA models, provides a background on how CA models have developed, explores a range of applications of where they have been used within the geographical sciences, prior to concluding with future directions for CA modeling. 
The figures below are a sample from the entry, for example, we outline different types of spaces within CA models such as those shown in Figures 1 and 2. We also show how simple rules can lead to the emergence of patterns such as the Game of Life as shown in Figure 3 or  Rule 30 as shown in Figure 4.

Figure 1: Two-Dimensional Cellular Automata Neighborhoods

Figure 2: Voronoi Tessellations Of Space Where Each Polygon Has A Different Number Of Neighbors Based On A Shared Edge.

Figure 3: Example of Cells Changing State from Dead (White) To Alive (Black) Over Time Depending On The States of its Neighboring Cells.

Figure 4: A One-Dimensional CA Model Implementing “Rule 30” Where Successive Iterations Are Presented Below Each Other.

Full Reference:

Crooks, A.T. (2017), Cellular Automata, in Richardson, D., Castree, N., Goodchild, M. F., Kobayashi, A. L., Liu, W. and Marston, R.  (eds.), The International Encyclopedia of Geography: People, the Earth, Environment, and Technology, Wiley Blackwell. DOI: 10.1002/9781118786352.wbieg0578. (pdf)

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Call for Papers in a special issue of Remote Sensing in Ecology and Conservation on citizen science

Remote Sensing in Ecology and Conservation is a new open access journal, addressing the interdisciplinary field that links different aspects of remote sensing (the use of different imaging and sensing technologies) and the field of ecology and conservation. It is publishing its papers in Open Access, so the papers are free to download and share. … Continue reading Call for Papers in a special issue of Remote Sensing in Ecology and Conservation on citizen science

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Mapping 5,000 Years of City Growth

I recently stumbled upon a great dataset. It’s the first to provide comprehensive data for world city sizes as far back as 3700BC. The authors (Meredith Reba, Femke Reitsma & Karen Seto) write: How were cities distributed globally in the past? How many people lived in these cities? How did cities influence their local and regional […]

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Paper: GeoKey – open infrastructure for community mapping and science

The special issue of the Human Computation Journal (see the details of the editorial here), summarises the result from the EU FP7 “Citizen Cyberlab” project. One of the outcomes of the project is the development of the GeoKey platform for participatory mapping. Therefore, a paper that was written with Oliver Roick and Claire Ellul explains … Continue reading Paper: GeoKey – open infrastructure for community mapping and science

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Evolution of London’s Rush Hour Traffic Mix

My latest London data visualisation crunches an interesting dataset from the Department of Transport. The data is available across England, although I’ve chosen London in particular because of its more interesting (i.e. not just car dominated) traffic mix. I’ve also focused on just the data for 8am to 9am, to examine the height of the … Continue reading Evolution of London’s Rush Hour Traffic Mix

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Big Data and Design: More Baboon, Less Unicorn

I recently had the pleasure of giving a Creative Mornings talk. Each month there is a new theme that the presenters need to refer to – mine was “fantasy” so I chose to open with one of my favourite fantasy creatures: the unicorn. It’s a talk about the creative process behind Oliver Uberti and I’s […]

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Competition Winner: Exceptional contribution to (GIS/statistical) software

The GIScRG (Geographic Information Science Research Group) and QMRG (Quantitative Methods Research Group) are pleased to announce the winner of our 2017 Exceptional contribution to (GIS/statistical) software competition. Zhaoya Gong has been awarded £400 for your development of the ARTMAP-based GeoComputation Toolbox. Further details: The ARTMAP-based GeoComputation Toolbox is a set of ARTMAP-based neural networks tools … Continue reading Competition Winner: Exceptional contribution to (GIS/statistical) software

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Applications of Agent-based Models

Often I get asked the question along the lines of: “how are agent-based models are being used outside academia, especially in government and private industry?” So I thought it was about time I briefly write something about this.

Let me start with a question I ask my students when I first introduce agent-based modeling: “Have you ever seen an agent-based model before?” Often the answer is NO, but then I show them the following clip from MASSIVE (Multiple Agent Simulation System in Virtual Environment) where agent-based models are used in a variety of movies and TV shows. But apart from TV shows and movies where else have agent-based models been used?
There are two specific application domains where agent-based modeling has taken off. The first being pedestrian simulation for example, LegionSteps and EXODUS simulation platforms. The second is the area of traffic modeling for example, there are several microsimulation/agent-based model platforms such as PTV Visum, TransModeler and Paramics. Based on these companies websites they have clients in industry, government and academia.
If we move away from the areas discussed above, there is a lot of writing about the potential of agent-based modeling. For example, the Bank of England had a article entitled “Agent-based models: understanding the economy from the bottom up” which to quote from the summary:
“considers the strengths of agent-based modelling, which explains the behaviour of a system by simulating the behaviour of each individual ‘agent’ in it, and the ways that it can be used to help central banks understand the economy.”

Similar articles can be seen in the New York Times and the Guardian to name but a few. But where else have agent-based models been used? A sample (and definitely not an exhaustive list) of applications and references are provided below for interested readers:
  • Southwest Airlines used an agent-based model to improve how it handled cargo (Seibel and Thomas, 2000).
  • Eli Lilly used an agent-based model for drug development (Bonabeau, 2003a).
  • Pacific Gas and Electric: Used an agent based model to see how energy flows through the power grid (Bonabeau, 2003a).
  • Procter and Gamble used an agent-based model to understand its consumer markets (North et al., 2010) while Hewlett-Packard used an agent-based model to understand how hiring strategies effect corporate culture (Bonabeau, 2003b).
  • Macy’s have used agent-based models for store design (Bonabeau, 2003b).
  • NASDAQ used and agent based model to explore changes to Stock Market’s decimalization (Bonabeau, 2003b; Darley and Outkin, 2007).
  • Using a agent-based model to explore capacity and demand in theme parks (Bonabeau, 2000).
  • Traffic and pedestrian modeling (Helbing and Balietti, 2011).
  • Disease dynamics (e.g. Eubank et al., 2004).
  • Agent-based modeling has also been used for wild fire training, incident command and community outreach (Guerin and Carrera, 2010). For example SimTable was used in the  2016 Sand Fire in California. 
  • InSTREAM: Explores how river salmon populations react to changes (Railsback and Harvey, 2002).

While not a comprehensive list, it is hoped that these examples and links will be useful if someone asks the question I started this post with. If anyone else knows of any other real world applications of agent-based modeling please let me know (preferably with a link to a paper or website).
 
References

  • Bonabeau, E. (2000), ‘Business Applications of Social Agent-Based Simulation’, Advances in Complex Systems, 3(1-4): 451-461.
  • Bonabeau, E. (2003a), ‘Don’t Trust Your Gut’, Harvard Business Review, 81(5): 116-123.
  • Bonabeau, E. (2003b), ‘Predicting the Unpredictable’, Harvard Business Review, 80(3): 109-116.
  • Darley, V. and Outkin, A.V. (2007), NASDAQ Market Simulation: Insights on a Major Market from the Science of Complex Adaptive Systems, World Scientific Publishing, River Edge, NJ.
  • Eubank, S., Guclu, H., Kumar, A.V.S., Marathe, M.V., Srinivasan, A., Toroczkai, Z. and Wang, N. (2004), ‘Modelling Disease Outbreaks in Realistic Urban Social Networks’, Nature, 429: 180-184.
  • Guerin, S. and Carrera, F. (2010), ‘Sand on Fire: An Interactive Tangible 3D Platform for the Modeling and Management of Wildfires.’ WIT Transactions on Ecology and the Environment, 137: 57-68.
  • Helbing, D. and Balietti, S. (2011), How to do Agent-based Simulations in the Future: From Modeling Social Mechanisms to Emergent Phenomena and Interactive Systems Design, Santa Fe Institute, Working Paper 11-06-024, Santa Fe, NM.
  • North, M.J., Macal, C.M., Aubin, J.S., Thimmapuram, P., Bragen, M., Hahn, J., J., K., Brigham, N., Lacy, M.E. and Hampton, D. (2010), ‘Multiscale Agent-based Consumer Market Modeling’, Complexity, 15(5): 37-47.
  • Railsback, S.F. and Harvey, B.C. (2002), ‘Analysis of Habitat Selection Rules using an Individual-based Model’, Ecology, 83(7): 1817-1830.
  • Seibel, F. and Thomas, C. (2000), ‘Manifest Destiny: Adaptive Cargo Routing at Southwest Airlines’, Perspectives on Business Innovation, 4: 27-33.

    Continue reading »

    Applications of Agent-based Models

    Often I get asked the question along the lines of: “how are agent-based models are being used outside academia, especially in government and private industry?” So I thought it was about time I briefly write something about this.

    Let me start with a question I ask my students when I first introduce agent-based modeling: “Have you ever seen an agent-based model before?” Often the answer is NO, but then I show them the following clip from MASSIVE (Multiple Agent Simulation System in Virtual Environment) where agent-based models are used in a variety of movies and TV shows. But apart from TV shows and movies where else have agent-based models been used?
    There are two specific application domains where agent-based modeling has taken off. The first being pedestrian simulation for example, LegionSteps and EXODUS simulation platforms. The second is the area of traffic modeling for example, there are several microsimulation/agent-based model platforms such as PTV Visum, TransModeler and Paramics. Based on these companies websites they have clients in industry, government and academia.
    If we move away from the areas discussed above, there is a lot of writing about the potential of agent-based modeling. For example, the Bank of England had a article entitled “Agent-based models: understanding the economy from the bottom up” which to quote from the summary:
    “considers the strengths of agent-based modelling, which explains the behaviour of a system by simulating the behaviour of each individual ‘agent’ in it, and the ways that it can be used to help central banks understand the economy.”

    Similar articles can be seen in the New York Times and the Guardian to name but a few. But where else have agent-based models been used? A sample (and definitely not an exhaustive list) of applications and references are provided below for interested readers:
    • Southwest Airlines used an agent-based model to improve how it handled cargo (Seibel and Thomas, 2000).
    • Eli Lilly used an agent-based model for drug development (Bonabeau, 2003a).
    • Pacific Gas and Electric: Used an agent based model to see how energy flows through the power grid (Bonabeau, 2003a).
    • Procter and Gamble used an agent-based model to understand its consumer markets (North et al., 2010) while Hewlett-Packard used an agent-based model to understand how hiring strategies effect corporate culture (Bonabeau, 2003b).
    • Macy’s have used agent-based models for store design (Bonabeau, 2003b).
    • NASDAQ used and agent based model to explore changes to Stock Market’s decimalization (Bonabeau, 2003b; Darley and Outkin, 2007).
    • Using a agent-based model to explore capacity and demand in theme parks (Bonabeau, 2000).
    • Traffic and pedestrian modeling (Helbing and Balietti, 2011).
    • Disease dynamics (e.g. Eubank et al., 2004).
    • Agent-based modeling has also been used for wild fire training, incident command and community outreach (Guerin and Carrera, 2010). For example SimTable was used in the  2016 Sand Fire in California. 
    • InSTREAM: Explores how river salmon populations react to changes (Railsback and Harvey, 2002).

    While not a comprehensive list, it is hoped that these examples and links will be useful if someone asks the question I started this post with. If anyone else knows of any other real world applications of agent-based modeling please let me know (preferably with a link to a paper or website).
     
    References

    • Bonabeau, E. (2000), ‘Business Applications of Social Agent-Based Simulation’, Advances in Complex Systems, 3(1-4): 451-461.
    • Bonabeau, E. (2003a), ‘Don’t Trust Your Gut’, Harvard Business Review, 81(5): 116-123.
    • Bonabeau, E. (2003b), ‘Predicting the Unpredictable’, Harvard Business Review, 80(3): 109-116.
    • Darley, V. and Outkin, A.V. (2007), NASDAQ Market Simulation: Insights on a Major Market from the Science of Complex Adaptive Systems, World Scientific Publishing, River Edge, NJ.
    • Eubank, S., Guclu, H., Kumar, A.V.S., Marathe, M.V., Srinivasan, A., Toroczkai, Z. and Wang, N. (2004), ‘Modelling Disease Outbreaks in Realistic Urban Social Networks’, Nature, 429: 180-184.
    • Guerin, S. and Carrera, F. (2010), ‘Sand on Fire: An Interactive Tangible 3D Platform for the Modeling and Management of Wildfires.’ WIT Transactions on Ecology and the Environment, 137: 57-68.
    • Helbing, D. and Balietti, S. (2011), How to do Agent-based Simulations in the Future: From Modeling Social Mechanisms to Emergent Phenomena and Interactive Systems Design, Santa Fe Institute, Working Paper 11-06-024, Santa Fe, NM.
    • North, M.J., Macal, C.M., Aubin, J.S., Thimmapuram, P., Bragen, M., Hahn, J., J., K., Brigham, N., Lacy, M.E. and Hampton, D. (2010), ‘Multiscale Agent-based Consumer Market Modeling’, Complexity, 15(5): 37-47.
    • Railsback, S.F. and Harvey, B.C. (2002), ‘Analysis of Habitat Selection Rules using an Individual-based Model’, Ecology, 83(7): 1817-1830.
    • Seibel, F. and Thomas, C. (2000), ‘Manifest Destiny: Adaptive Cargo Routing at Southwest Airlines’, Perspectives on Business Innovation, 4: 27-33.

      Continue reading »

      Tomorrow’s cities – nightmare vision of the future? – BBC News

      Tomorrow’s cities – nightmare vision of the future?
      BBC News
      The trend towards both cities and citizens being plugged into the network has only one logical conclusion, says Prof Andrew Hudson-Smith, from University College London’s Centre for Advanced Spatial Analysis. “Bees exist on Earth to pollinate flowers

      and more »

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      Tomorrow’s cities – nightmare vision of the future? – BBC News

      Tomorrow’s cities – nightmare vision of the future?
      BBC News
      The trend towards both cities and citizens being plugged into the network has only one logical conclusion, says Prof Andrew Hudson-Smith, from University College London’s Centre for Advanced Spatial Analysis. “Bees exist on Earth to pollinate flowers

      and more »

      Continue reading »

      Edinburgh to host global experts at first Data Summit – The Scotsman

      Edinburgh to host global experts at first Data Summit
      The Scotsman
      Natalia Adler, the charity’s New York-based data, research and policy planning specialist, will also be among the industry speakers at the summit, as will mathematician Dr Hannah Fry, a lecturer at the centre for advanced spatial analysis at University

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      Into the night – training day on citizen science slides

      Last December, the Natural Environment Research Council (NERC) awarded funding to UCL Extreme Citizen Science group and Earthwatch as part of their investment in public engagement. The projects are all short – they start from January to March and included public engagement and training to early career researchers. “Into the Night” highlights the importance of light … Continue reading Into the night – training day on citizen science slides

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      Podcast – discussion with Liz Killen and Alice Sheppard on citizen science

      Several weeks ago, Liz Killen, who is studying for an MSc in Science Communication at Imperial College organised an interview with me and Alice Sheppard about aspects of citizen science, for the I, Science the science magazine of Imperial College. This is the second time ExCiteS is covered in the magazine, after a report in 2013 by … Continue reading Podcast – discussion with Liz Killen and Alice Sheppard on citizen science

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      Editorial in Human Computation Journal – Creativity and Learning in Citizen Cyberscience

      As part of a special issue of the open access Human Computation Journal, I am the co-author of the editorial Creativity and Learning in Citizen Cyberscience – Lessons from the Citizen Cyberlab Summit. Following the summit (see blog post here), Egle Ramanauskaite took the blog posts and edited them with her notes, which led to a … Continue reading Editorial in Human Computation Journal – Creativity and Learning in Citizen Cyberscience

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      Public Participation GIS and Participatory GIS in the Era of GeoWeb – editorial for a special issue

      As part of the AAG 2015 conference, Bandana Kar, Rina Ghose, Renee Sieber and I organised a set of sessions on Public Participation GIS – you can read the summary here. After the conference, we’ve organised a special issue of the Cartographic Journal (thanks to Alex Kent, the journal editor) dedicated to current perspectives of public participation … Continue reading Public Participation GIS and Participatory GIS in the Era of GeoWeb – editorial for a special issue

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      Competition: Exceptional contribution to (GIS/statistical) software

      The GIScRG (Geographic Information Science Research Group of the RGS) and QMRG (Quantitative Methods Research Group) are looking to use some of their fund to support a contribution to some GIS / statistical open source software. We have £500 to offer as a grant (or series of grants) to one or more projects that will … Continue reading Competition: Exceptional contribution to (GIS/statistical) software

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      A Shared Perspective for PGIS and VGI – new paper

      Part of the special issue on Public Participation GIS that was published in The Cartographic Journal, was a paper that was led by Jeroen Verplanke (ITC). This paper goes back to the workshop on participatory GIS in 2013, that was the leaving event for Dr Mike McCall in ITC, after which he continue to work in UNAM, Mexico. … Continue reading A Shared Perspective for PGIS and VGI – new paper

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      Authoritative and VGI in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi

      The motivation behind the paper was that while there are numerous studies comparing VGI to authoritative data in the developed world, there are very few that do so in developing world. In order to address this issue in the paper we compare the quality of authoritative road data (i.e. from the Regional Center for Mapping of Resources for Development – RCMRD) and non-authoritative crowdsourced road data (i.e. from OpenStreetMap (OSM) and Google’s Map Maker) in conjunction with population data in and around Nairobi, Kenya.

      Results from our analysis show variability in coverage between all these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards more rural areas. Further information including the abstract to our paper, some figures and full reference is given below.

      Abstract:

      With volunteered geographic information (VGI) platforms such as OpenStreetMap (OSM) becoming increasingly popular, we are faced with the challenge of assessing the quality of their content, in order to better understand its place relative to the authoritative content of more traditional sources. Until now, studies have focused primarily on developed countries, showing that VGI content can match or even surpass the quality of authoritative sources, with very few studies in developing countries. In this paper we compare the quality of authoritative (data from the Regional Center for Mapping of Resources for Development – RCMRD) and non-authoritative (data from OSM and Google’s Map Maker) road data in conjunction with population data in and around Nairobi, Kenya. Results show variability in coverage between all these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards rural areas. Furthermore, OSM had higher content density in large slums, surpassing the authoritative datasets at these locations, while Map Maker showed better coverage in rural housing areas. These results suggest a greater need for a more inclusive approach using VGI to supplement gaps in authoritative data in developing nations.

      Keywords: Volunteered Geographic Information; Crowdsourcing; Road Networks; Population Data; Kenya  
      Road Coverage per km2
      Pairwise difference in road coverage. Clockwise from top left: i) RCMRD 2011 versus Map Maker 2014; ii) RCMRD 2011 versus OSM 2011; iii) RCMRD 2011 versus OSM 2014; iv) OSM 2014 versus Map Maker 2014 (Red cells: first layer has higher coverage; Green cells: second layer has higher coverage).

      Full Reference:

      Mahabir, R., Stefanidis, A., Croitoru, A., Crooks, A.T. and Agouris, P. (2017), “Authoritative and Volunteered Geographical Information in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi, Kenya”, ISPRS International Journal of Geo-Information, 6(1): 24, doi:10.3390/ijgi6010024.

      As always any thoughts or comments about this work are welcome.

      Continue reading »

      Authoritative and VGI in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi

      The motivation behind the paper was that while there are numerous studies comparing VGI to authoritative data in the developed world, there are very few that do so in developing world. In order to address this issue in the paper we compare the quality of authoritative road data (i.e. from the Regional Center for Mapping of Resources for Development – RCMRD) and non-authoritative crowdsourced road data (i.e. from OpenStreetMap (OSM) and Google’s Map Maker) in conjunction with population data in and around Nairobi, Kenya.

      Results from our analysis show variability in coverage between all these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards more rural areas. Further information including the abstract to our paper, some figures and full reference is given below.

      Abstract:

      With volunteered geographic information (VGI) platforms such as OpenStreetMap (OSM) becoming increasingly popular, we are faced with the challenge of assessing the quality of their content, in order to better understand its place relative to the authoritative content of more traditional sources. Until now, studies have focused primarily on developed countries, showing that VGI content can match or even surpass the quality of authoritative sources, with very few studies in developing countries. In this paper we compare the quality of authoritative (data from the Regional Center for Mapping of Resources for Development – RCMRD) and non-authoritative (data from OSM and Google’s Map Maker) road data in conjunction with population data in and around Nairobi, Kenya. Results show variability in coverage between all these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards rural areas. Furthermore, OSM had higher content density in large slums, surpassing the authoritative datasets at these locations, while Map Maker showed better coverage in rural housing areas. These results suggest a greater need for a more inclusive approach using VGI to supplement gaps in authoritative data in developing nations.

      Keywords: Volunteered Geographic Information; Crowdsourcing; Road Networks; Population Data; Kenya  
      Road Coverage per km2
      Pairwise difference in road coverage. Clockwise from top left: i) RCMRD 2011 versus Map Maker 2014; ii) RCMRD 2011 versus OSM 2011; iii) RCMRD 2011 versus OSM 2014; iv) OSM 2014 versus Map Maker 2014 (Red cells: first layer has higher coverage; Green cells: second layer has higher coverage).

      Full Reference:

      Mahabir, R., Stefanidis, A., Croitoru, A., Crooks, A.T. and Agouris, P. (2017), “Authoritative and Volunteered Geographical Information in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi, Kenya”, ISPRS International Journal of Geo-Information, 6(1): 24, doi:10.3390/ijgi6010024.

      As always any thoughts or comments about this work are welcome.

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