scholarly journals A Google Earth-based database management for schistosomiasis control in Zanzibar

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Ming-Zhen He ◽  
Wei Li ◽  
Saleh Juma ◽  
Fatma Kabole ◽  
Da-Cheng Xu ◽  
...  
Keyword(s):  
Data Storage ◽  
Spatial Data ◽  
Spatial Databases ◽  
Pilot Project ◽  
Google Earth ◽  
Snail Host ◽  
Schistosomiasis Control ◽  
Intermediate Snail Host ◽  
Snail Control ◽  
Human Infections

Schistosomiasis remains a serious health problem in Africa. Although a strong, coordinated agenda for research on this disease has been in place for the last 50 years in Zanzibar, data storage, retrieval of survey data and management remain problem areas. We investigated the use of Google Earth (GE) in conjunction with a hand-held, global positioning system as a pilot project for managing schistosomiasis control. In this way, risk areas can be surveyed and followed up by visualizing both the distribution of human infections and that of the intermediate snail host together with environmental information. A platform with three spatial databases was created: i) Distribution of infected humans; ii) Distribution of the intermediate snail host in ponds (infected and not infected specimens); iii) Distribution of the intermediate snail host in streams (infected and non-infected specimens). The GE spatial database increased the efficiency of follow-up case treatment as well as snail control and contributed also to the discovery of previously unknown areas in need of snail control. We conclude that this platform is advantageous not only by being useful for management and visualization of spatial data, but also because it is easy to operate and available free of charge.

scholarly journals Highly Resolved Rainfall-Runoff Simulation of Retrofitted Green Stormwater Infrastructure at the Micro-Watershed Scale

Land ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 339 ◽  
Author(s):  
Sami Towsif Khan ◽  
Fernando Chapa ◽  
Jochen Hack
Keyword(s):  
High Resolution ◽  
Spatial Data ◽  
Surface Runoff ◽  
Spatial Databases ◽  
Google Earth ◽  
Urban Land Use ◽  
Small Scale ◽  
Runoff Reduction ◽  
Intense Storm ◽  
Green Stormwater Infrastructure

Green Stormwater Infrastructure (GSI), a sustainable engineering design approach for managing urban stormwater runoff, has long been recommended as an alternative to conventional conveyance-based stormwater management strategies to mitigate the adverse impact of sprawling urbanization. Hydrological and hydraulic simulations of small-scale GSI measures in densely urbanized micro watersheds require high-resolution spatial databases of urban land use, stormwater structures, and topography. This study presents a highly resolved Storm Water Management Model developed under considerable spatial data constraints. It evaluates the cumulative effect of the implementation of dispersed, retrofitted, small-scale GSI measures in a heavily urbanized micro watershed of Costa Rica. Our methodology includes a high-resolution digital elevation model based on Google Earth information, the accuracy of which was sufficient to determine flow patterns and slopes, as well as to approximate the underground stormwater structures. The model produced satisfactory results in event-based calibration and validation, which ensured the reliability of the data collection procedure. Simulating the implementation of GSI shows that dispersed, retrofitted, small-scale measures could significantly reduce impermeable surface runoff (peak runoff reduction up to 40%) during frequent, less intense storm events and delay peak surface runoff by 5–10 min. The presented approach can benefit stormwater practitioners and modelers conducting small scale hydrological simulation under spatial data constraint.

GML Topology Data Storage Schema Design

2007 ◽  
Vol 11 (6) ◽  
pp. 701-708 ◽  
Author(s):  
Yuzhen Li ◽  
◽  
Jianming Lu ◽  
Jihong Guan ◽  
Mingying Fan ◽  
...  
Keyword(s):  
Data Storage ◽  
Spatial Data ◽  
Information Exchange ◽  
Spatial Databases ◽  
Geographical Information ◽  
Markup Language ◽  
Oracle Spatial ◽  
Geographical Data ◽  
Schema Design ◽  
Extensible Markup

Geography Markup Language (GML) was developed to standardize the representation of geographical data in extensible markup language (XML), which facilitates geographical information exchange and sharing. Increasing amounts of geographical data are being presented in GML as its use widens, raising the question of how to store GML data efficiently to facilitate its management and retrieval. We analyze topology data in GML and propose storing nonspatial and spatial data from GML documents in spatial databases (e.g, Oracle Spatial, DB2 Spatial, and PostGIS/PostgreSQL.). We then use an example to analyze the topology relation.

Spatial Databases

2017 ◽  
pp. 111-149
Author(s):  
Grace L. Samson ◽  
Joan Lu ◽  
Mistura M. Usman ◽  
Qiang Xu
Keyword(s):  
Data Storage ◽  
Spatial Data ◽  
Spatial Databases ◽  
Query Language ◽  
Dimensional Space ◽  
Spatial Database ◽  
Multidimensional Data ◽  
Data Types ◽  
3 Dimensional ◽  
Storage And Retrieval

Spatial databases maintain space information which is appropriate for applications where there is need to monitor the position of an object or event over space. Spatial databases describe the fundamental representation of the object of a dataset that comes from spatial or geographic entities. A spatial database supports aspects of space and offers spatial data types in its data model and query language. The spatial or geographic referencing attributes of the objects in a spatial database permits them to be positioned within a two (2) dimensional or three (3) dimensional space. This chapter looks into the fundamentals of spatial databases and describes their basic component, operations and architecture. The study focuses on the data models, query Language, query processing, indexes and query optimization of a spatial databases that approves spatial databases as a necessary tool for data storage and retrieval for multidimensional data of high dimensional spaces.

scholarly journals Highly Resolved Rainfall-Runoff Simulation of Retrofitted Green Stormwater Infrastructure at the Micro-Watershed Scale

2020 ◽  
Author(s):  
Sami Towsif Khan ◽  
Fernando Chapa ◽  
Jochen Hack
Keyword(s):  
High Resolution ◽  
Spatial Data ◽  
Surface Runoff ◽  
Spatial Databases ◽  
Google Earth ◽  
Urban Land Use ◽  
Small Scale ◽  
Runoff Reduction ◽  
Intense Storm ◽  
Green Stormwater Infrastructure

Green Stormwater Infrastructure (GSI), a sustainable engineering design approach for managing urban stormwater runoff, has long been recommended as an alternative to conventional conveyance-based stormwater management strategies to mitigate the adverse impact of sprawling urbanization. Hydrological and hydraulic simulations of small-scale GSI measures in densely urbanized micro watersheds require high-resolution spatial databases of urban land use, stormwater structures, and topography. This study presents a highly resolved Storm Water Management Model developed under considerable spatial data constraints. It evaluates the cumulative effect of the implementation of dispersed, retrofitted, small-scale GSI measures in a heavily urbanized micro watershed of Costa Rica. Our methodology includes a high-resolution digital elevation model based on Google Earth information, whose accuracy was sufficient to determine flow patterns and slopes, as well as to approximate the subsurface stormwater structures. The model produced satisfactory results in event-based calibration and validation, which ensured the reliability of the data collection procedure. Simulating the implementation of GSI shows that dispersed, retrofitted, small-scale measures could significantly reduce impermeable surface runoff (peak runoff reduction up to 40%) during frequent, less intense storm events and delay peak surface runoff 5-10 minutes. The presented approach can benefit stormwater practitioners and modelers conducting small scale hydrological simulation under spatial data constraint.

scholarly journals Literature Mapper: A QGIS Plugin for Georeferencing Citations in Zotero

2021 ◽  
Vol 14 ◽  
pp. 117862212110092
Author(s):  
Michele M Tobias ◽  
Alex I Mandel
Keyword(s):  
Data Storage ◽  
Spatial Data ◽  
Future Research ◽  
Web Mapping ◽  
Web Map ◽  
Public Sharing ◽  
Academic Publications ◽  
Spatial Coordinates ◽  
Study Sites ◽  
Soil And Water

Many studies in air, soil, and water research involve observations and sampling of a specific location. Knowing where studies have been previously undertaken can be a valuable addition to future research, including understanding the geographical context of previously published literature and selecting future study sites. Here, we introduce Literature Mapper, a Python QGIS plugin that provides a method for creating a spatial bibliography manager as well as a specification for storing spatial data in a bibliography manager. Literature Mapper uses QGIS’ spatial capabilities to allow users to digitize and add location information to a Zotero library, a free and open-source bibliography manager on basemaps or other geographic data of the user’s choice. Literature Mapper enhances the citations in a user’s online Zotero database with geo-locations by storing spatial coordinates as part of traditional citation entries. Literature Mapper receives data from and sends data to the user’s online database via Zotero’s web API. Using Zotero as the backend data storage, Literature Mapper benefits from all of its features including shared citation Collections, public sharing, and an open web API usable by additional applications, such as web mapping libraries. To evaluate Literature Mapper’s ability to provide insights into the spatial distribution of published literature, we provide a case study using the tool to map the study sites described in academic publications related to the biogeomorphology of California’s coastal strand vegetation, a line of research in which air movement, soil, and water are all driving factors. The results of this exercise are presented in static and web map form. The source code for Literature Mapper is available in the corresponding author’s GitHub repository: https://github.com/MicheleTobias/LiteratureMapper

Estimating productivity of earthmoving operations using spatial technologies1This paper is one of a selection of papers in this Special Issue on Construction Engineering and Management.

2012 ◽  
Vol 39 (9) ◽  
pp. 1072-1082 ◽  
Author(s):  
Ali Montaser ◽  
Ibrahim Bakry ◽  
Adel Alshibani ◽  
Osama Moselhi
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Data Fitting ◽  
Google Earth ◽  
Simulation Software ◽  
Distribution Functions ◽  
Automated Classification ◽  
Cycle Times ◽  
Automated Method ◽  
Spreadsheet Application

This paper presents an automated method for estimating productivity of earthmoving operations in near-real-time. The developed method utilizes Global Positioning System (GPS) and Google Earth to extract the data needed to perform the estimation process. A GPS device is mounted on a hauling unit to capture the spatial data along designated hauling roads for the project. The variations in the captured cycle times were used to model the uncertainty associated with the operation involved. This was carried out by automated classification, data fitting, and computer simulation. The automated classification is applied through a spreadsheet application that classifies GPS data and identifies, accordingly, durations of different activities in each cycle using spatial coordinates and directions captured by GPS and recorded on its receiver. The data fitting was carried out using commercially available software to generate the probability distribution functions used in the simulation software “Extend V.6”. The simulation was utilized to balance the production of an excavator with that of the hauling units. A spreadsheet application was developed to perform the calculations. An example of an actual project was analyzed to demonstrate the use of the developed method and illustrates its essential features. The analyzed case study demonstrates how the proposed method can assist project managers in taking corrective actions based on the near-real-time actual data captured and processed to estimate productivity of the operations involved.

Unmanned Aerial Vehicle (UAV) time-lapse monitoring of the instability processes affecting Varano Hill: A case study of the ancient Roman site of Villa Arianna

2021 ◽  
Author(s):  
Renato Somma ◽  
Alfredo Trocciola ◽  
Daniele Spizzichino ◽  
Alessandro Fedele ◽  
Gabriele Leoni ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Data ◽  
Morphological Evolution ◽  
Archaeological Site ◽  
Pilot Project ◽  
Time Lapse ◽  
3D Models ◽  
Slope Instability ◽  
Test Case ◽  
Slope Instabilities

<p>The archaeological site of Villa Arianna - located on Varano Hill, south of Vesuvius - offer tantalizing information regarding first-century AD resilience to hydrogeological risk. Additionally, the site provides an important test case for mitigation efforts of current and future geo-hazard. Villa Arianna, notable in particular for its wall frescoes, is part of a complex of Roman villas built between 89 BC and AD 79 in the ancient coastal resort area of Stabiae. This villa complex is located on a morphological terrace that separates the ruins from the present-day urban center of Castellammare di Stabia. The Varano hill is formed of alternating pyroclastic deposits, from the Vesuvius Complex, and alluvial sediments, from the Sarno River. The area, in AD 79, was completely covered by PDCs from the Plinian eruption of Vesuvius. Due to the geomorphological structure the slope is prone to slope instability phenomena that are mainly represented by earth and debris flows, usually triggered by heavy rainfall. The susceptibility is worsened by changes in hydraulic and land-use conditions mainly caused by lack of maintenance of mitigation works. Villa Arianna is the subject of a joint pilot project of the INGV-ENEA-ISPRA that includes non-invasive monitoring techniques such as the use of UAVs to study the areas of the slope at higher risk of instability. The project, in particular, seeks to implement innovative mitigation solutions that are non-destructive to the cultural heritage. UAVs represent the fastest way to produce high-resolution 3D models of large sites and allow archaeologists to collect accurate spatial data that can be used for 3D GIS analyses. Through this pilot project, we have used detailed 3D models and high-resolution ortho-images for new analyses and documentation of the site and to map the slope instabilities that threatens the Villa Arianna site. Through multi-temporal analyses of different data acquisitions, we intend to define the detailed morphological evolution of the entire Varano slope. These analyses will allow us to highlight priority areas for future low-impact mitigation interventions.</p>

scholarly journals Land Cover Classification Based on Cloud Computing Platform

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Nghia Viet Nguyen ◽  
Thu Hoai Thi Trinh ◽  
Hoa Thi Pham ◽  
Trang Thu Thi Tran ◽  
Lan Thi Pham ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Spatial Data ◽  
Google Earth ◽  
Computer Hardware ◽  
Commercial Software ◽  
Computing Platform ◽  
Land Cover Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.

scholarly journals A Survey on Spatio-temporal Data Analytics Systems

2022 ◽  
Author(s):  
Md Mahbub Alam ◽  
Luis Torgo ◽  
Albert Bifet
Keyword(s):  
Programming Languages ◽  
Spatial Data ◽  
Data Analytics ◽  
Spatial Databases ◽  
Location Based Services ◽  
Temporal Data ◽  
Wide Range ◽  
Data Volume ◽  
Spatio Temporal ◽  
Gis Software

Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

Sign in / Sign up

Export Citation Format

Share Document