Using Graph Database in Spatial Data Generation

2014 ◽  
pp. 643-650 ◽  
Author(s):  
Tomasz Płuciennik ◽  
Ewa Płuciennik-Psota
Keyword(s):  
Spatial Data ◽  
Graph Database ◽  
Data Generation

scholarly journals COLLABORATION AND OUTREACH TOWARDS USE OF GEOSPATIAL TECHNOLOGIES IN INDIA WITH AN EMPHASIS ON ISRO’S EFFORT

2018 ◽  
Vol XLII-5 ◽  
pp. 51-57
Author(s):  
S. Ghosh ◽  
A. Sachan ◽  
G. Srinivas
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Technology Development ◽  
Economic Benefits ◽  
Data Generation ◽  
Geospatial Technologies ◽  
Policy Makers ◽  
Partnership Development ◽  
E Learning ◽  
Indian Industries

<p><strong>Abstract.</strong> Geospatial technology is adopted in India through ‘Digital India’, for better governance and socio-economic benefits of common citizens. Remote sensing imageries and derived products are a direct input for geospatial data generation, monitoring resources and providing citizen services. As Indian Space Research Organisation (ISRO) is responsible for technology development, launch and data distribution of Indian remote sensing satellites, there is a conscious effort for partnership development with international space agencies and Indian industries to fulfil this requirement. Simultaneously ISRO’s co-operation with government departments for geo-spatial data generation and utilization is taken as a priority. This Paper presents the effort of ISRO for cooperation with International and national agencies as well as with government departments and policy makers. In this surge of utilization of geo-spatial techniques capacity building has become very relevant. A glimpse of geospatial education through government &amp;amp; academic institutes in India and E-learning is presented here with an emphasis of ISRO’s contribution.</p>

scholarly journals Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data

2020 ◽  
Vol 12 (5) ◽  
pp. 78 ◽  
Author(s):  
Sedick Baker Effendi ◽  
Brink van der Merwe ◽  
Wolf-Tilo Balke
Keyword(s):  
Data Analysis ◽  
Real World ◽  
Spatial Data ◽  
Response Times ◽  
Graph Database ◽  
Temporal Data ◽  
Extensive Evaluation ◽  
Database Technology ◽  
Spatio Temporal ◽  
Real World Datasets

Every day large quantities of spatio-temporal data are captured, whether by Web-based companies for social data mining or by other industries for a variety of applications ranging from disaster relief to marine data analysis. Making sense of all this data dramatically increases the need for intelligent backend systems to provide realtime query response times while scaling well (in terms of storage and performance) with increasing quantities of structured or semi-structured, multi-dimensional data. Currently, relational database solutions with spatial extensions such as PostGIS, seem to come to their limits. However, the use of graph database technology has been rising in popularity and has been found to handle graph-like spatio-temporal data much more effectively. Motivated by the need to effectively store multi-dimensional, interconnected data, this paper investigates whether or not graph database technology is better suited when compared to the extended relational approach. Three database technologies will be investigated using real world datasets namely: PostgreSQL, JanusGraph, and TigerGraph. The datasets used are the Yelp challenge dataset and an ambulance response simulation dataset, thus combining real world spatial data with realistic simulations offering more control over the dataset. Our extensive evaluation is based on how each database performs under practical data analysis scenarios similar to those found on enterprise level.

scholarly journals SDG Indicator 11.3.1 and Secondary Cities: An Analysis and Assessment

2021 ◽  
Vol 10 (11) ◽  
pp. 713
Author(s):  
Melinda Laituri ◽  
Danielle Davis ◽  
Faith Sternlieb ◽  
Kathleen Galvin
Keyword(s):  
Sustainable Development ◽  
Spatial Data ◽  
Urban Form ◽  
City Planning ◽  
Population Data ◽  
Growth Patterns ◽  
Data Generation ◽  
Middle Income ◽  
Global Issues ◽  
Land Use Efficiency

Secondary cities are rapidly growing areas in low- and middle-income countries that lack data, planning, and essential services for sustainable development. Their rapid, informal growth patterns mean secondary cities are often data-poor and under-resourced, impacting the ability of governments to target development efforts, respond to emergencies, and design sustainable futures. The United Nations’ Sustainable Development Goal (SDG) 11 focuses on inclusive, safe, resilient, and sustainable cities and human settlements. SDG Indicator (SDGI) 11.3.1 calculates the ratio of land consumption rate to population growth rate to enhance inclusive and sustainable urbanization. Our paper compares three cities—Denpasar, Indonesia; Kharkiv, Ukraine; and Mekelle, Ethiopia—that were part of the Secondary Cities (2C) Initiative of the U.S. Department of State, Office of the Geographer and Global Issues to assess SDGI 11.3.1. The 2C Initiative focused on field-based participatory mapping for data generation to assist city planning. Urban form and population data are critical for calculating and visually representing this ratio. We examine the spatial extent of each city to assess land use efficiency (LUE) and track changes in urban form over time. With limited demographic and spatial data for secondary cities, we speculate whether SDGI 11.3.1 is useful for small- and medium-sized cities.

scholarly journals Precision Agriculture Workflow, from Data Collection to Data Management Using FOSS Tools: An Application in Northern Italy Vineyard

2021 ◽  
Vol 10 (4) ◽  
pp. 236
Author(s):  
Elena Belcore ◽  
Stefano Angeli ◽  
Elisabetta Colucci ◽  
Maria Angela Musci ◽  
Irene Aicardi
Keyword(s):  
Data Structure ◽  
Optical Sensors ◽  
Spatial Data ◽  
Precision Agriculture ◽  
New Technologies ◽  
Software Tools ◽  
Northern Italy ◽  
Data Generation ◽  
Individual Tree ◽  
Data Infrastructure

In the past decades, technology-based agriculture, also known as Precision Agriculture (PA) or smart farming, has grown, developing new technologies and innovative tools to manage data for the whole agricultural processes. In this framework, geographic information, and spatial data and tools such as UAVs (Unmanned Aerial Vehicles) and multispectral optical sensors play a crucial role in the geomatics as support techniques. PA needs software to store and process spatial data and the Free and Open Software System (FOSS) community kept pace with PA’s needs: several FOSS software tools have been developed for data gathering, analysis, and restitution. The adoption of FOSS solutions, WebGIS platforms, open databases, and spatial data infrastructure to process and store spatial and nonspatial acquired data helps to share information among different actors with user-friendly solutions. Nevertheless, a comprehensive open-source platform that, besides processing UAV data, allows directly storing, visualising, sharing, and querying the final results and the related information does not exist. Indeed, today, the PA’s data elaboration and management with a FOSS approach still require several different software tools. Moreover, although some commercial solutions presented platforms to support management in PA activities, none of these present a complete workflow including data from acquisition phase to processed and stored information. In this scenario, the paper aims to provide UAV and PA users with a FOSS-replicable methodology that can fit farming activities’ operational and management needs. Therefore, this work focuses on developing a totally FOSS workflow to visualise, process, analyse, and manage PA data. In detail, a multidisciplinary approach is adopted for creating an operative web-sharing tool able to manage Very High Resolution (VHR) agricultural multispectral-derived information gathered by UAV systems. A vineyard in Northern Italy is used as an example to show the workflow of data generation and the data structure of the web tool. A UAV survey was carried out using a six-band multispectral camera and the data were elaborated through the Structure from Motion (SfM) technique, resulting in 3 cm resolution orthophoto. A supervised classifier identified the phenological stage of under-row weeds and the rows with a 95% overall accuracy. Then, a set of GIS-developed algorithms allowed Individual Tree Detection (ITD) and spectral indices for monitoring the plant-based phytosanitary conditions. A spatial data structure was implemented to gather the data at canopy scale. The last step of the workflow concerned publishing data in an interactive 3D webGIS, allowing users to update the spatial database. The webGIS can be operated from web browsers and desktop GIS. The final result is a shared open platform obtained with nonproprietary software that can store data of different sources and scales.

scholarly journals Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry

2021 ◽  
Author(s):  
Sercan GÜLCİ ◽  
Kıvanç YÜKSEL ◽  
Selçuk GÜMÜŞ ◽  
Michael WİNG
Keyword(s):  
Spatial Data ◽  
Landsat 8 ◽  
Data Generation ◽  
Sentinel 2

Riso-Tree: An Efficient and Scalable Index for Spatial Entities in Graph Database Management Systems

2021 ◽  
Vol 7 (3) ◽  
pp. 1-39
Author(s):  
Yuhan Sun ◽  
Mohamed Sarwat
Keyword(s):  
Spatial Data ◽  
Database Management ◽  
Spatial Location ◽  
Database Management Systems ◽  
Management Systems ◽  
Graph Database ◽  
Index Maintenance ◽  
Spatial Attributes ◽  
Graph Queries ◽  
Spatial Entities

With the ubiquity of spatial data, vertexes or edges in graphs can possess spatial location attributes side by side with other non-spatial attributes. For instance, as of June 2018, the Wikidata knowledge graph contains 48,547,142 data items (i.e., vertexes) and 13% of them have spatial location attributes. The article proposes Riso-Tree, a generic efficient and scalable indexing framework for spatial entities in graph database management systems. Riso-Tree enables the fast execution of graph queries that involve different types of spatial predicates (GraSp queries). The proposed framework augments the classic R-Tree structure with pre-materialized sub-graph entries. The pruning power of R-Tree is enhanced with the sub-graph information. Riso-Tree partitions the graph into sub-graphs based on their connectivity to the spatial sub-regions. The proposed index allows for the fast execution of GraSp queries by efficiently pruning the traversed vertexes/edges based upon the materialized sub-graph information. The experiments show that the proposed Riso-Tree achieves up to two orders of magnitude faster execution time than its counterparts when executing GraSp queries on real graphs (e.g., Wikidata). The strategy of limiting the size of each sub-graph entry ( PN max ) is proposed to reduce the storage overhead of Riso-Tree. The strategy can save up to around 70% storage without harming the query performance according to the experiments. Another strategy is proposed to ensure the performance of the index maintenance (Irrelevant Vertexes Skipping). The experiments show that the strategy can improve performance, especially for slow updates. It proves that Riso-Tree is useful for applications that need to support frequent updates.

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