scholarly journals Penentuan Lokasi Lumbung Pangan Berdasarkan Gravity Location Models dengan Koordinat UTM di Provinsi Maluku Utara

2018 ◽  
Vol 1 (2) ◽  
pp. 7-16
Author(s):  
Nafisah Riskya Hasna ◽  
Adi Setiawan ◽  
Hanna Arini Parhusip
Keyword(s):  
Google Maps ◽  
Location Models ◽  
Universal Transverse Mercator

Salah satu usaha pemerintah dalam menyejahterakan masyarakat yaitu memenuhi kebutuhan pangan masyarakatnya. Pengendalian dalam penyediaan bahan pangan sangat diperlukan untuk dapat membantu dalam mengontrol distribusi bahan pangan. Dalam pengendalian penyediaan bahan pangan digunakan Gravity Location Models (GLM). GLM pada penelitian ini digunakan untuk menentukan suatu gudang yang berfungsi sebagai penghubung antara sumber-sumber pasokan dan beberapa lokasi sehingga dapat meminimalisasi biaya transportasi. Lokasi suatu gudang tersebut menggunakan koordinat geografis yang akan ditransformasikan ke koordinat UTM (Universal Transverse Mercator). Penelitian ini menggunakan data koordinat geografis pada Google Maps dan data jumlah penduduk, padi, jagung, dan ubi kayu Provinsi Maluku Utara Tahun 2014. Pengolahan data dilakukan secara manual dan menggunakan metode grid untuk mencari koordinat lokasi gudang atau lumbung pangan. Hasil dari perhitungan menggunakan (1) rumus  dan (2) metode grid adalah koordinat lokasi lumbung pangan yang memiliki biaya transportasi minimum di Provinsi Maluku Utara yaitu terletak pada Kabupaten Halmahera Timur.

PERANCANGAN APLIKASI LOKASI TAMBAL BAN DI PONOROGO BERBASIS ANDROID

KOMPUTEK ◽  
2017 ◽  
Vol 1 (1) ◽  
pp. 37
Author(s):  
Irfan Khoirul Arifin ◽  
Aliyadi Aliyadi ◽  
Yovi Litanianda
Keyword(s):  
Location Based Service ◽  
Surrounding Area ◽  
Repair Shop ◽  
Google Maps ◽  
Information Media ◽  
Service Location

The number of vehicles in Indonesia continues to increase every year. This also happened in Ponorogo regency. It will also be directly proportional to the number of people who have problems with their vehicles, such as leaked tire quotes for being nailed or other causes. And will also increase the need for tire services. For motorists who are less aware of the surrounding area when experiencing damage to motorcycle tires, then of course to find a place nearest tire patch will be quite difficult. Therefore in this study developed information media for Android-based applications to map the locations - tire patch locations in Ponorogo, as well as looking for the closest tire patch with the rider. This app is a location-based service (location-based service) to the driver with the nearest patch of the banal location. Based on the results of testing this application can help users find the location of location preservation, tar bambal patch location, tire repair shop list, and tire repair shop list distance. This application can also show each other the location in accordance with the location of google maps applications. 

scholarly journals Building Location Models for Visual Place Recognition

2015 ◽  
Vol 35 (4) ◽  
pp. 334-356 ◽  
Author(s):  
Elena S. Stumm ◽  
Christopher Mei ◽  
Simon Lacroix
Keyword(s):  
Place Recognition ◽  
Location Models ◽  
Visual Place Recognition

scholarly journals Online Analytics for Shrimp Farm Management to Control Water Quality Parameters and Growth Performance

2021 ◽  
Vol 13 (11) ◽  
pp. 5839
Author(s):  
Siriwan Kajornkasirat ◽  
Jareeporn Ruangsri ◽  
Charuwan Sumat ◽  
Pete Intaramontri
Keyword(s):  
Water Quality ◽  
Growth Performance ◽  
Mobile Application ◽  
Database Management System ◽  
Quality Data ◽  
Analysis Tool ◽  
Quality Factors ◽  
Google Maps ◽  
Water Quality Data ◽  
Shrimp Farm

An online analytic service system was designed as a web and a mobile application for shrimp farmers and shrimp farm managers to manage the growth performance of shrimp. The MySQL database management system was used to manage the shrimp data. The Apache Web Server was used for contacting the shrimp database, and the web content displays were implemented with PHP script, JavaScript, and HTML5. Additionally, the program was linked with Google Charts to display data in various graphs, such as bar graphs and scatter diagrams, and Google Maps API was used to display water quality factors that are related to shrimp growth as spatial data. To test the system, field survey data from a shrimp farm in southern Thailand were used. Growth performance of shrimp and water quality data were collected from 13 earthen ponds in southern peninsular Thailand, located in the Surat Thani, Krabi, Phuket, and Satun provinces. The results show that the system allowed administrators to manage shrimp and farm data from the field sites. Both mobile and web applications were accessed by the users to manage the water quality factors and shrimp data. The system also provided the data analysis tool required to select a parameter from a list box and shows the association between water quality factors and shrimp data with a scatter diagram. Furthermore, the system generated a report of shrimp growth for the different farms with a line graph overlay on Google Maps™ in the data entry suite via mobile application. Online analytics for the growth performance of shrimp as provided by this system could be useful as decision support tools for effective shrimp farming.

On agglomeration in competitive location models

2014 ◽  
Vol 246 (1-2) ◽  
pp. 31-55 ◽  
Author(s):  
Vladimir Marianov ◽  
H. A. Eiselt
Keyword(s):  
Competitive Location ◽  
Location Models ◽  
Competitive Location Models

scholarly journals “In cities, it’s not far, but it takes long”: comparing estimated and replicated travel times to reach life-saving obstetric care in Lagos, Nigeria

2021 ◽  
Vol 6 (1) ◽  
pp. e004318
Author(s):  
Aduragbemi Banke-Thomas ◽  
Kerry L M Wong ◽  
Francis Ifeanyi Ayomoh ◽  
Rokibat Olabisi Giwa-Ayedun ◽  
Lenka Benova
Keyword(s):  
Travel Time ◽  
Friction Surface ◽  
Obstetric Care ◽  
Travel Times ◽  
Night Time ◽  
Google Maps ◽  
Surface Approach ◽  
Low Resource ◽  
Life Saving ◽  
The Cost

BackgroundTravel time to comprehensive emergency obstetric care (CEmOC) facilities in low-resource settings is commonly estimated using modelling approaches. Our objective was to derive and compare estimates of travel time to reach CEmOC in an African megacity using models and web-based platforms against actual replication of travel.MethodsWe extracted data from patient files of all 732 pregnant women who presented in emergency in the four publicly owned tertiary CEmOC facilities in Lagos, Nigeria, between August 2018 and August 2019. For a systematically selected subsample of 385, we estimated travel time from their homes to the facility using the cost-friction surface approach, Open Source Routing Machine (OSRM) and Google Maps, and compared them to travel time by two independent drivers replicating women’s journeys. We estimated the percentage of women who reached the facilities within 60 and 120 min.ResultsThe median travel time for 385 women from the cost-friction surface approach, OSRM and Google Maps was 5, 11 and 40 min, respectively. The median actual drive time was 50–52 min. The mean errors were >45 min for the cost-friction surface approach and OSRM, and 14 min for Google Maps. The smallest differences between replicated and estimated travel times were seen for night-time journeys at weekends; largest errors were found for night-time journeys at weekdays and journeys above 120 min. Modelled estimates indicated that all participants were within 60 min of the destination CEmOC facility, yet journey replication showed that only 57% were, and 92% were within 120 min.ConclusionsExisting modelling methods underestimate actual travel time in low-resource megacities. Significant gaps in geographical access to life-saving health services like CEmOC must be urgently addressed, including in urban areas. Leveraging tools that generate ‘closer-to-reality’ estimates will be vital for service planning if universal health coverage targets are to be realised by 2030.

scholarly journals 3 Stars on Yelp, 4 Stars on Google Maps

2021 ◽  
Vol 4 (CSCW3) ◽  
pp. 1-25
Author(s):  
Hanlin Li ◽  
Brent Hecht
Keyword(s):  
Google Maps
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