Identifying suitable sites for pump irrigation system from open source and shallow tube well in selected areas of Davao Del Sur through geographic information system-based water resources assessment

Abstrak: Sub DAS Amprong secara administrasi masuk pada wilayah Kabupaten Malang dan Kota Malang. Meliputi lima Kecamatan yakni: Kedungkandang, Poncokusumo, Tumpang, Pakis dan Jabung. Risiko bencana longsor tergolong tinggi pada kawasan ini. Maka dari itu, penelitian ini bertujuan untuk melakukan pengurangan risiko bencana longsor mengunakan pendeketaan GIS (Geographic Information System). Menggunakan GIS distribusi tingkat risiko akan dapat diketahui dengan baik, sehingga mampu memberikan solusi yang lebih akurat. Penelitian ini meliputi empat tahapan: 1) pemetaan bahaya longsor, 2) pemetaan kerentanan bencana, 3) pemetaan kapasitas bencana, 4) pemetaan risiko bencana. Hasilnya diketahui bahwa kecamatan Jabung dan Poncokusumo merupakan wialayah dengan tingkat risiko longsor paling tinggi. Upaya yang dapat dilakukan untuk mengurangi tingkat risiko dapat dilakukan melalui mitigasi bencana secara struktural dan nonstruktural. Wilayah dengan risiko tinggi bukan merupakan kawasan pemukiman, namun memiliki aktivitas utama berupa pertanian. Oleh karena itu perlu adanya manajemen risiko bencana longsor dalam usaha longsor seperti: dengan cara: 1) pengaturan sistem irigasi dengan baik, 2) penerapan sistem terasering, dan 3) pemasangan bronjong pada kaki lereng. Abstract: Amprong watershed is administratively included in Malang Regency and Malang City. Includes five districts namely: Kedungkandang, Poncokusumo, Tumpang, Pakis and Jabung. The risk of landslides is classified high in this region. Therefore, this research aims to reduce the risk of landslides using GIS (Geographic Information System). Using GIS the distribution of risk levels will be well known, so as to provide a more accurate solution. This research includes four stages: 1) mapping of landslide hazards, 2) mapping of disaster vulnerability, 3) mapping of disaster capacity, 4) mapping of disaster risk. The results are known that the Jabung and Poncokusumo sub-districts are areas with the highest risk of landslides. Efforts that can be made to reduce the level of risk can be done through structural and nonstructural disaster mitigation. High risk areas are not residential areas, but have major activities in the form of agriculture. Therefore, it is necessary to have landslide risk management, such as: by: 1) regulating the irrigation system properly, 2) applying the terracing system, and 3) installing gabions at the foot of the slope.

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ГЕОИНФОРМАЦИОННАЯ БАЗА ДАННЫХ ПО ОЦЕНКЕ МЕЛИОРАТИВНОГО СОСТОЯНИЯ ОРОШАЕМЫХ И ОСУШАЕМЫХ ЗЕМЕЛЬ ЮГА ЕВРОПЕЙСКОЙ ЧАСТИ РОССИИ

НАУЧНО-МЕТОДИЧЕСКОЕ ОБЕСПЕЧЕНИЕ РАЗВИТИЯ МЕЛИОРАТИВНО-ВОДОХОЗЯЙСТВЕННОГО КОМПЛЕКСА ◽

10.37738/vniigim.2020.36.89.036 ◽

2020 ◽

pp. 4

Author(s):

A.A. Buber ◽

E.L. Ratkovich ◽

Y.A. Homutov

Keyword(s):

Information System ◽

Geographic Information System ◽

Open Source ◽

Water Use ◽

Russian Federation ◽

Geographic Information ◽

Hydraulic Structures ◽

Topographic Map ◽

The Russian Federation

Для визуального отображения сведений о государственных гидромелиоративных системах, их составе, показателях, отдельно расположенных гидротехнических сооружениях и информации по использованию воды и сбросу загрязняющих веществ в исследуемых регионах, была использована общедоступная географическая информационная система с открытым кодом QGIS 3.4. ГИС-проект содержит: топографическую карту с нанесенными границами областей и векторные слои, включающие данные о гидромелиоративных системах, составе гидротехнических сооружений, показателям орошаемых и осушаемых земель и водопользованию по субъектам РФ 1,2,3.The open-source geographic information system QGIS 3.4 was used to visually display information about state hydro-reclamation systems, their composition, indicators, separately located hydraulic structures, and information on water use and discharge of pollutants in the study regions. The GIS project contains: a topographic map with the applied regions borders and vector layers, including data on hydro-reclamation systems, hydraulic structures composition, indicators of irrigated and drained lands and water use for the subjects of the Russian Federation.

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Application of Epidemiological Geographic Information System: An Open-Source Spatial Analysis Tool Based on the OMOP Common Data Model

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17217824 ◽

2020 ◽

Vol 17 (21) ◽

pp. 7824

Author(s):

Jaehyeong Cho ◽

Seng Chan You ◽

Seongwon Lee ◽

DongSu Park ◽

Bumhee Park ◽

...

Keyword(s):

United States ◽

Information System ◽

Geographic Information System ◽

Spatial Analysis ◽

Health Outcomes ◽

Open Source ◽

Geographical Distribution ◽

The United States ◽

Geographic Information ◽

Common Data Model

Background: Spatial epidemiology is used to evaluate geographical variations and disparities in health outcomes; however, constructing geographic statistical models requires a labor-intensive process that limits the overall utility. We developed an open-source software for spatial epidemiological analysis and demonstrated its applicability and quality. Methods: Based on standardized geocode and observational health data, the Application of Epidemiological Geographic Information System (AEGIS) provides two spatial analysis methods: disease mapping and detecting clustered medical conditions and outcomes. The AEGIS assesses the geographical distribution of incidences and health outcomes in Korea and the United States, specifically incidence of cancers and their mortality rates, endemic malarial areas, and heart diseases (only the United States). Results: The AEGIS-generated spatial distribution of incident cancer in Korea was consistent with previous reports. The incidence of liver cancer in women with the highest Moran’s I (0.44; p < 0.001) was 17.4 (10.3–26.9). The malarial endemic cluster was identified in Paju-si, Korea (p < 0.001). When the AEGIS was applied to the database of the United States, a heart disease cluster was appropriately identified (p < 0.001). Conclusions: As an open-source, cross-country, spatial analytics solution, AEGIS may globally assess the differences in geographical distribution of health outcomes through the use of standardized geocode and observational health databases.

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