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Land use and land cover change (LULCC) is among the most apparent natural landscape processes impacted by anthropogenic activities, particularly in fast-growing regions. In India, at present, due to the impacts of anthropogenic climate change, supplemented by the fast pace of developmental activities, the areas providing the highest agricultural yields are facing the threat of either extinction or change in land use. This study assesses the LULCC in the fastest-changing landscape region of the Indian state of Bihar, District Muzaffarpur. This district is known for its litchi cultivation, which, over the last few years, has been observed to be increasing in acreage at the behest of a decrease in natural vegetation. In this study, we aim to assess the past, present and future changes in LULC of the Muzaffarpur district using support vector classification and CA-ANN (cellular automata-artificial neural network) algorithms. For assessing the present and past LULC of the study area, we used Landsat Satellite data for 1990, 2000, 2010, and 2020. It was observed that between 1990 and 2020, the area under vegetation, wetlands, water body, and fallow land decreased by 44.28%, 34.82%, 25.56%, and 5.63%, respectively. At the same time, the area under built-up, litchi plantation, and cropland increased by 1451.30%, 181.91%, and 5.66%, respectively. Extensive ground truthing was carried out to assess the accuracy of the LULC for 2020, whereas historical google earth images were used for 1990, 2000, and 2010, through the use of overall accuracy and kappa coefficient indices. The kappa coefficients for the final LULC for the years 1990, 2000, 2010, and 2020 were 0.79, 0.75, 0.87, and 0.85, respectively. For forecasting the future LULC, first, the LULC of 1990 and 2010 were used to predict the landscape for 2020 using the CA-ANN model. After calibrating and validating the CA-ANN outputs, LULC for 2030 and 2050 were generated. The generated future LULC scenarios were validated using kappa index statistics by comparing the forecast outcomes with the original LULC data for 2020. It was observed that in both 2030 and 2050, built-up and vegetation would be the major transitioning LULC. In 2030 and 2050, built-up will increase by 13.15% and 108.69%, respectively, compared to its area in 2020; whereas vegetation is expected to decrease by 14.30% in 2030 and 32.84% in 2050 compared to its area in 2020. Overall, this study depicted a decline in the natural landscape and a sudden increase in the built-up and cash-crop area. If such trends continue, the future scenario of LULC will also demonstrate the same pattern. This study will help formulate better land use management policy in the study area, and the overall state of Bihar, which is considered to be the poorest state of India and the most vulnerable to natural calamities. It also demonstrates the ability of the CA-ANN model to forecast future events and comprehend spatiotemporal LULC dynamics. [ABSTRACT FROM AUTHOR]

Urban open space is defined as any urban ground space, which is not covered by any concrete structures or that is not roofed by an architectural structure. These include food production areas, parks, gardens, recreational spaces, road/street side plantings, private farms etc. Some like governmental or municipal parks etc., within the city are permanent and cannot be converted to other land categories. While others like agricultural farms on the outskirts of city as well as within the city are easily converted to other land categories due to indiscriminate urban growth. The city of Patna (in Bihar, India) seems no exception. One of the obvious indicators of sustainable urban development is the quality and quantity of urban green spaces. In a recent report released by the World Health Organisation (WHO), Patna has been ranked at sixth place out of 795 cities in 67 countries across the globe in terms of air pollution. For this study, an outline map of Patna Municipal Corporation area was obtained from the Municipal department and georeferenced in QGIS with the help of reference points from Open Street Map (OSM) of the study area. Besides this, Landsat imageries of the region of three different years (1988, 2009 and 2017) were downloaded from GLCF (Global Land Cover Facility) site. Area of Interest was demarcated for all the imageries based on the geo-referenced map of Patna Municipal Corporation. Spatio-temporal change in land use was found out with the help of unsupervised classification using Erdas Imagine 9. The results were compiled to find the unsustainable growth of the city leading to the decline of urban open spaces especially the green space. Lastly some suggestive measures were given to ensure sustainable growth of the city in times to come. [ABSTRACT FROM AUTHOR]

Introduction: Development in technologies has changed the types of crime committed so does the investigation of crime. Almost in every forensic investigation digital evidence plays an important role and most of the digital evidence contains data related to location which is also called spatial data. The technology stores & displays geospatial data in the GIS. GIS is an emerging field, and it has vast applications in mapping, environmental management, urban planning, and forensics. Though forensic GIS is still in its infancy, it has the potential to act as an important tool in forensic science. Material and Methods: The objective of this study was Integration of spatial data and non-spatial data & creating a crime map of district Gopalganj, Bihar, India using the QGIS tool and to collect & analyze crime data. Crime data of district Gopalganj from 22 police stations for the year 2020 was collected from the website of the state crime record bureau, Bihar. Results and Conclusions: Kuchaikote block had highest number of cases as it shares its boundary with Uttar Pradesh, criminals take advantage of it and commit the crime on one side and take shelter on another side. The Crime Map presented in this paper will help law enforcement agencies to analyze the pattern and take appropriate action to prevent the occurrence of crime. Some of the areas which are needed to be explored in the field of crime mapping are geographic profiling and the integration of GPS with GIS. More GIS tools should be created with a forensic perspective in mind. A database can be developed at the state level which would contain a crime map of districts. [ABSTRACT FROM AUTHOR]