scholarly journals Spatial Database Designing for Environmental Monitoring and Decision Making in Mitrovica Region, The Republic of Kosovo

2021 ◽  
Vol 6 (2) ◽  
pp. 189
Author(s):  
Bashkim Idrizi ◽  
Edon Maliqi ◽  
Lyubka Pashova
Keyword(s):  
Environmental Monitoring ◽  
Open Source ◽  
Spatial Data ◽  
Negative Impact ◽  
Spatial Database ◽  
Spatial Data Analysis ◽  
Anthropogenic Pressure ◽  
Creative Commons ◽  
Mining Areas ◽  
The Republic

The integration of spatial data analysis methods and thematic map models is an approach to reduce the negative impact of anthropogenic pressure on the environment due to mining and waste generation. The large amounts of industrial waste from mining in the Mitrovica region in northern Kosovo lead to serious environmental problems with organic and inorganic water and soil pollution. This study aims to design and establish a geospatial database for long-term environmental monitoring, provide analytical tools, and support appropriate management decisions by local authorities and agencies. The database contains topographical elements and ecological parameters collected from different national and open access international sources. All collected data have been analyzed, standardized and harmonized within the open-source QGIS ver.3 software. The results showed that in developed datasets were organized in different GIS layers and compiled several thematic maps. The designed database is unique by its architecture, providing an opportunity for periodical monitoring of the environment near the mining areas. Keywords: Environmental monitoring; Spatial database; Open source software; QGIS; Kosovo.   Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

scholarly journals Adaptive Choropleth Mapper: An Open-Source Web-Based Tool for Synchronous Exploration of Multiple Variables at Multiple Spatial Extents

2019 ◽  
Vol 8 (11) ◽  
pp. 509 ◽  
Author(s):  
Han ◽  
Rey ◽  
Knaap ◽  
Kang ◽  
Wolf
Keyword(s):  
Open Source ◽  
Spatial Data ◽  
Visual Analytics ◽  
Spatial Data Analysis ◽  
Dynamic Visualization ◽  
Web Based ◽  
Choropleth Mapping ◽  
Choropleth Maps ◽  
Multiple Variables ◽  
Choropleth Map

Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the geographical distributions of multiple variables. Critical features for effective exploration of multiple choropleth maps are (1) automated computation of the same class intervals for shading different choropleth maps, (2) dynamic visualization of local variation in a variable, and (3) linking for synchronous exploration of multiple choropleth maps. Since the 1990s, these features have been developed and are now included in many commercial geographic information system (GIS) software packages. However, many choropleth mapping tools include only one or two of the three features described above. On the other hand, freely available mapping tools that support side-by-side multiple choropleth map visualizations are usually desktop software only. As a result, most existing tools supporting multiple choropleth-map visualizations cannot be easily integrated with Web-based and open-source data visualization libraries, which have become mainstream in visual analytics and geovisualization. To fill this gap, we introduce an open-source Web-based choropleth mapping tool called the Adaptive Choropleth Mapper (ACM), which combines the three critical features for flexible choropleth mapping.

scholarly journals Progress in the R ecosystem for representing and handling spatial data

2020 ◽  
Author(s):  
Roger S. Bivand
Keyword(s):  
New York ◽  
Data Analysis ◽  
Open Source ◽  
Spatial Data ◽  
Spatial Data Analysis ◽  
Data Handling ◽  
Good Match ◽  
R Programming Language ◽  
R Packages ◽  
R Programming

Abstract Twenty years have passed since Bivand and Gebhardt (J Geogr Syst 2(3):307–317, 2000. 10.1007/PL00011460) indicated that there was a good match between the then nascent open-source R programming language and environment and the needs of researchers analysing spatial data. Recalling the development of classes for spatial data presented in book form in Bivand et al. (Applied spatial data analysis with R. Springer, New York, 2008, Applied spatial data analysis with R, 2nd edn. Springer, New York, 2013), it is important to present the progress now occurring in representation of spatial data, and possible consequences for spatial data handling and the statistical analysis of spatial data. Beyond this, it is imperative to discuss the relationships between R-spatial software and the larger open-source geospatial software community on whose work R packages crucially depend.

scholarly journals Smart and IoT-Based Coupling and Coordination Development for Financial and Logistics Industries

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ji-you Li ◽  
Qi-qing Zhou ◽  
Pan-pan Yin
Keyword(s):  
Spatial Data ◽  
Driving Forces ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Spatial Data Analysis ◽  
Theil Index ◽  
Eastern Region ◽  
Coordinated Development ◽  
The Republic ◽  
Total Difference

Based on the panel data, collected through various Internet of Things (IoT) devices, of 31 various provinces and cities in the Republic of China from 2004 to 2019, due to the analysis of mechanism and the significance of coupled and coordinated development, methods like fuzzy comprehensive evaluation, entropy, coupling, and coordination degree model, exploratory spatial data analysis, and Theil index are widely used to analytically evaluate the dynamic coupling development of China’s financial and logistics industries. The analysis of the collected data shows that demand promotion, technological progress, corporate decision-making, and policy stimulus are the driving forces for the coordinated development. In addition, the coordinated development of both industries can achieve a win-win situation. Moreover, during the sample period, the level of coupled and coordinated development has made considerable progress, achieving a transition from moderate to slightly unbalanced level, but overall, it is still at a low level. The level of coupled and coordinated development is showing east-central-west, that is, a three-level declining trend. Guangdong is the province with the highest level, and Qinghai and Ningxia are the provinces with the lowest levels of coupled and coordinated development. The general evolution trend of the total difference in the levels of coupled and coordinated development is declining in fluctuation, and the differences in the eastern region and within the zones are the main reasons for the total difference.

An Introduction to Spatial Data Analysis

2020 ◽  
Author(s):  
Martin Wegmann ◽  
Jakob Schwalb-Willmann ◽  
Stefan Dech
Keyword(s):  
Remote Sensing ◽  
Data Analysis ◽  
Open Source ◽  
Spatial Data ◽  
Remote Sensing Data ◽  
Spatial Data Analysis ◽  
Data Sets ◽  
Data Handling ◽  
Book Covers

This is a book about how ecologists can integrate remote sensing and GIS in their research. It will allow readers to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. An Introduction to Spatial Data Analysis introduces spatial data handling using the open source software Quantum GIS (QGIS). In addition, readers will be guided through their first steps in the R programming language. The authors explain the fundamentals of spatial data handling and analysis, empowering the reader to turn data acquired in the field into actual spatial data. Readers will learn to process and analyse spatial data of different types and interpret the data and results. After finishing this book, readers will be able to address questions such as “What is the distance to the border of the protected area?”, “Which points are located close to a road?”, “Which fraction of land cover types exist in my study area?” using different software and techniques. This book is for novice spatial data users and does not assume any prior knowledge of spatial data itself or practical experience working with such data sets. Readers will likely include student and professional ecologists, geographers and any environmental scientists or practitioners who need to collect, visualize and analyse spatial data. The software used is the widely applied open source scientific programs QGIS and R. All scripts and data sets used in the book will be provided online at book.ecosens.org. This book covers specific methods including: what to consider before collecting in situ data how to work with spatial data collected in situ the difference between raster and vector data how to acquire further vector and raster data how to create relevant environmental information how to combine and analyse in situ and remote sensing data how to create useful maps for field work and presentations how to use QGIS and R for spatial analysis how to develop analysis scripts

scholarly journals Geostatistical Methods as a Tool Supporting Revitalization of Industrially Degraded and Post-Mining Areas

2020 ◽  
Vol 3 (1) ◽  
pp. 30-40
Author(s):  
Jarosław Zawadzki ◽  
Piotr Fabijańczyk ◽  
Karol Przeździecki
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Data Sets ◽  
Anthropogenic Pressure ◽  
Initial State ◽  
Entire Area ◽  
Mining Areas ◽  
Geostatistical Methods ◽  
Spatial Data Sets ◽  
Post Industrial

AbstractPost-industrial and post-mining areas have often been under strong anthropogenic pressure for a long time. As a result, such areas, after the ending of industrial activity require taking steps to revitalize them. It may cover many elements of the natural or urban environment, such as water, soil, vegetated areas, urban development etc. To carry out revitalization, it is necessary to determine the initial state of such areas, often using selected chemical, geophysical or ecological. After that it is also important to properly monitor the state of such areas to assess the progress of the revitalization process. For this purpose a variety of change detection technics were developed. Post-industrial areas are very often characterized by a large extent, are difficult to access, have complicated land cover. For this reason, it is particularly important to choose appropriate methods to assess the degree of pollution of such areas. Such methods should be as economical as possible and time-effective. A very desirable feature of such methods is that they should allow a quick assessment of the entire area. Geostatistics supplemented by modern remote sensing can be effective for this purpose. Nowadays, using remote sensing, it is possible to gather information simultaneously from the entire, even vast area, with high spatial, spectral and temporal resolution. Geostatistics in turn provides many tools that are able to enable rapid analysis and inference based on even very complicated often scarce spatial data sets obtained from ground measurement and satellite observations. The goal of the article was to present selected results obtained using geostatistical methods also related to remote sensing, which may be helpful for decision makers in revitalizing post-industrial and post-mining areas. The results described in this paper were based mostly on the previous studies, carried out by authors.

scholarly journals Analysis of the Spatial Distribution Characteristics of Urban Resilience and Its Influencing Factors: A Case Study of 56 Cities in China

2019 ◽  
Vol 16 (22) ◽  
pp. 4442 ◽  
Author(s):  
Zhang ◽  
Chen ◽  
Cai ◽  
Gao ◽  
Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Influencing Factors ◽  
Spatial Data ◽  
Evaluation System ◽  
Negative Impact ◽  
Positive Impact ◽  
Measurement Model ◽  
Spatial Data Analysis ◽  
Urban Resilience ◽  
Pension Insurance

The healthy development of the city has received widespread attention in the world, and urban resilience is an important issue in the study of urban development. In order to better provide a useful reference for urban resilience and urban health development, this paper takes 56 cities in China as the research object, and selects 29 indicators from urban infrastructure, economy, ecology and society. The combination weight method, exploratory spatial data analysis (ESDA) and spatial measurement model are used to explore the spatial distribution of urban resilience and its influencing factors. From 2006 to 2017, the urban resilience of prefecture-level cities in the four provinces showed a wave-like rise. During the study period, the urban resilience values, measured as Moran’s Is, were greater than 0.3300, showing a significantly positive correlation in regard to their spatial distribution. Regarding the local spatial correlation, the urban resilience of the study area had spatial agglomeration characteristics within the province, with a significant distribution of "cold hot spots" in the spatial distribution. From the perspective of the factors that affected urban resilience, the proportion of the actual use of foreign capital in GDP and carbon emissions per 10,000 CNY of GDP had a negative impact and GDP per square kilometer, the proportion of urban pension insurance coverage, the proportion of the population with higher education, and expenditure to maintain and build cities had a positive impact. The development strategy of urban resilience must be combined with the actual situation of the region, and the rational resilience performance evaluation system and the top-level design of urban resilience improvement should be formulated to comprehensively improve urban resilience.

scholarly journals The opendata geoportal of the Lamma Consortium

2016 ◽  
Author(s):  
Manuela Corongiu ◽  
Riccardo Mari ◽  
Raffaella Ferrari ◽  
Lorenzo Bottai ◽  
Valentina Grasso ◽  
...  
Keyword(s):  
Open Source ◽  
Spatial Data ◽  
Information Source ◽  
Open Data ◽  
Weather Forecast ◽  
Regional Government ◽  
Data Infrastructure ◽  
Creative Commons ◽  
Data Platform ◽  
Data Portal

The new Lamma Open Data platform (http://dati.lamma.toscana.it) allows data download related to information delivered / managed by the Consortium, encouraging the reuse both at technical and legal level. The datasets, over 220, belong to the weather forecast and geospatial topics above all, but they are in continuous updating, both spatial and no spatial (such as administrative documentation). Lamma open data platform integrates in a harmonised interface, most off the spatial dataset already available through the Lamma geoportal (http://geoportale.lamma.rete.toscana.it/MapStore/public/), now available for download as open data. The particularity of meteorological information is their organization in models, archives and formats according to the type of information, source of acquisition and level of elaboration. These formats are not all functional or directly manageable in their entirety, as data to be made available and immediately accessible. The datasets therefore require a preliminary phase of evaluation and analysis of the contents to identify the most appropriate elements for publication via filters and elaborations that maintain the significance of the variables to be highlighted. A synergic and integrated infrastructure for spatial data has been carried out through open source softwares. The LaMMA Geoportal integrates, in a single simple but powerful interface, the functionalities of research, display and download of the available data. This objective is to provide a ready-to-use tool for all users who do not intend to connect directly to the services offered or to download (and therefore reutilize) the data: in this case we relied on the software Open Source MapStore. The open data platform is directly connected to the Geonetwork metadata catalogue that in turn automatically provide a real-time ingestion of datasets in geoportal. The Lamma open data infrastructure has been implemented by the use of CKAN software. All the datasets are made available according to the CC-BY license - Attribution Creative Commons. That choice will allow an easier federation with Open Tuscany (http://dati.toscana.it/), the open data portal of Tuscany Regional Government that until now has hosted, as supplementary task, some Lamma Consortium datasets. The open data infrastructure has been implemented thanks to the Life+IMAGINE European contribution and with the support of the Geosolutions company.

scholarly journals Assessment of Temperature Variability Effect on Rice Production in Nasarawa State, Nigeria

2020 ◽  
pp. 91-100
Author(s):  
Oladeinde Stephen Olufemi ◽  
Magaji I. Joshua ◽  
Ekpo Abraham Salamatu
Keyword(s):  
Spatial Data ◽  
Negative Impact ◽  
Temperature Variability ◽  
Rice Production ◽  
Spatial Data Analysis ◽  
Effect Of Temperature ◽  
Maximum Temperature ◽  
Daily Maximum ◽  
The South ◽  
Governmental Institutions

The output of cereal farmlands is imperative for sustainable global food security. Quantity of production from cereal croplands are partly a function of climatic elements and are connected to the pulses of climatic variation. Hence, this paper assessed temperature variability effect on rice production in Nasarawa State, Nigeria. Daily maximum and minimum temperature data were obtained from the Nigerian Meteorological Agency and converted into monthly averages while annual rice production data was obtained from the office of Nasarawa State’s Agricultural Development Programme. Acquired data were analysed using Linear Multiple Regression Model, coefficient of variation and spatial data analysis techniques. Although rice production in the State is being affected by the fluctuations in both minimum and maximum monthly temperature, the later poses grave concern for sustainability of rice production with a negative effect size of -3.145 and a coefficient value of -191,324.30 metric tons. This negative impact of maximum temperature fluctuations on rice production indicates that rice production in Nasarawa State is vulnerable to climate variability with increasing maximum temperature. LGAs in the south senatorial district has more favourable locations for rice production in comparison to those in the North and West districts given that less temperature fluctuation was observed in the former. Government and non-governmental institutions as well as individuals planning to establish rice farm project(s) in the study area should consider doing so in the South Senatorial District in order to avoid the adverse effect of temperature variability.

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