This open access book includes methods for retrieval, semantic representation, and analysis of Volunteered Geographic Information (VGI), geovisualization and user interactions related to VGI, and ...discusses selected topics in active participation, social context, and privacy awareness. It presents the results of the DFG-funded priority program "VGI: Interpretation, Visualization, and Social Computing" (2016-2023). The book includes three parts representing the principal research pillars within the program. Part I "Representation and Analysis of VGI" discusses recent approaches to enhance the representation and analysis of VGI. It includes semantic representation of VGI data in knowledge graphs; machine-learning approaches to VGI mining, completion, and enrichment as well as to the improvement of data quality and fitness for purpose. Part II "Geovisualization and User Interactions related to VGI" book explores geovisualizations and user interactions supporting the analysis and presentation of VGI data. When designing these visualizations and user interactions, the specific properties of VGI data, the knowledge and abilities of different target users, and technical viability of solutions need to be considered. Part III "Active Participation, Social Context and Privacy Awareness" of the book addresses the human impact associated with VGI. It includes chapters on the use of wearable sensors worn by volunteers to record their exposure to environmental stressors on their daily journeys, on the collective behavior of people using location-based social media and movement data from football matches, and on the motivation of volunteers who provide important support in information gathering, filtering and analysis of social media in disaster situations. The book is of interest to researchers and advanced professionals in geoinformation, cartography, visual analytics, data science and machine learning.
Geographical Information System (GIS) and remote sensing have become necessary tools in finding out land use land cover (LULC) change integrated with their associated driving factors. The utilization ...of satellite imagery made it easy to interpret the highly urbanized Warangal City that has experienced a lot of change in LULC during the last few decades. This paper discusses the ability of the integration of cellular automata (CA) and Markov chain–based 2D land use simulation module conjunction with GIS techniques. Markov chain algorithm is used for calibration and optimization by considering LULC of appropriate set of images for the years 2004, 2006, and 2018. Transitional change in LULC from one class to another is simulated using an artificial neural network (ANN) while cellular automata simulation is carried out to predict the plausible future LULC for the year 2052 after validating the model using the LULC of the year 2018. Analysis of the multi-temporal LULC maps indicated that the biophysical and socio-economic factors have greatly influenced the rise in built up while a decrease in agriculture in the year 2052. In conclusion, this technique is a powerful tool for monitoring and modeling change in land cover. Further suggestions for government officials are provided for an effective policymaking and to protect the land resource.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This book thoroughly covers the remote sensing visualization and analysis techniques based on computational imaging and vision in Earth science. The book addresses several advanced improvement models ...to guide the engineers in developing different remote sensing visualization and analysis schemes.
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FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Urban expansion on agricultural land is driven by rapid population growth and is seen as a critical problem in Sub-Saharan African countries. This study was attempted to analyze the dynamics of urban ...expansion and land use land cover (LULC) changes using geospatial techniques in Addis Ababa City. In the present study, rate and extent of built-up area as well as changes of LULC types were derived from multispectral band of Landsat 5 TM (1990), Landsat 7 ETM + (2003), and Landsat 8 OLI/TIRS (2020), respectively. The expansion of built-up area in the periphery of the city was generated by using supervised classification method with maximum likelihood algorithm from 1990 to2020. The result of the study shows that built-up area was the most dominated LULC types in the study area from 1990 to 2020. Consequently, built-up area was increased by the rate of 2.77km
2
/year in the past three decades whereas agriculture and grassland were decreased by the 2.68 km
2
/year and 1.78 km
2
/year, respectively, over the study period. The results show that the built-up area was increased by an area 83.2km
2
while agriculture and grassland were decreased by 80.4km
2
and 53.4km
2
from 1990 to 2020, respectively. Agriculture and grassland were converted to built-up area by an area of 41.1km
2
and 17.3km
2
, from 1990 to 2020, respectively. This is mainly because of rapid expansion of built-up area and declined of agricultural land and grassland in the periphery of the city. To minimize the rapid expansions of built-up area on agricultural and grassland, the city planners should look other land use types like bare land. Moreover, urban agriculture should be promoted in the periphery of the city. This study can help to improve land use policy and enhance public understanding on the dynamics of LULC change and its implications on sustainable land management around big cities. The use of Geographic Information System (GIS) in urban planning and land use management is essential not optional to minimize the anticipated impacts of urban expansions on agricultural and other LULC classes.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This editorial presents a special collection of papers addressing the concept of place and its use in geographical information science (GIScience). The concept of place is a topic of increasing ...interest among GIScience scholars. First attempts to formalise platial information have been made and it is increasingly held that user‐generated data sets in particular are often more platial than spatial in nature. At the same time, and especially when compared to geometric spatial concepts, the concept of place is ambiguous, complex and difficult to capture in formal and analytical terms, suggesting the need for interdisciplinary approaches. This collection presents articles covering a wide range of place‐related aspects, including both conceptual and more applied contributions. In the present editorial we summarise these and comment on their individual contributions, and hope that the readership of Transactions in GIS will find the special collection inspiring and informative.
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The prediction of diseases caused by viral infections is a complex medical task where many real data that consists of different variables must be employed. As known, COVID-19 is the most dangerous ...disease worldwide; nowhere, an effective drug has been found yet. To limit its spread, it is essential to find a rational method that shows the spread of this virus by relying on many infected people’s data. A model consisting of three artificial neural networks’ (ANN) functions was developed to predict COVID-19 separation in Iraq based on real infection data supplied by the public health department at the Iraqi Ministry of Health. The performance efficiency of this model was evaluated, where its performance efficiency reached 81.6% when employed four statistical error criteria as mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (
R
2
), and Nash-Sutcliffe coefficient (NC). The severity of the virus’s spread across Iraq was assessed in a short term (in the next 6 months), where the results show that the spread severity will intensify in this short term by 17.1%, and the average death cases will increase by 8.3%. These results clarified by creating spatial distribution maps for virus spread are simulated by employing a Geographic Information System (GIS) environment to be used as a useful database for developing plans for combating viruses in Iraq.
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