IMPLEMENTATION OF THE HAVERSINE METHOD FOR THE APPLICATION OF FINDING TOURIST ATTRACTIONS IN NANGGUNG DISTRICT

Traveling is one of the most popular hobbies nowadays. Natural tourist destinations located in the countryside are a choice for travelers. There are several tourist sites located in the country of Bogor, one of which is in the district of Nanggung. Nanggung sub-district is in the region of west Bogor, approximately 60 km from the capital city of Bogor Regency (Cibinong). The location of the Nanggung sub-district has an area of 135.25 km2, located near the foot of Mount Salak, creating a beautiful atmosphere and quite cold air. There are approximately 30 tourist destinations in the Nanggung sub-district. Base mobile application to help tourists determine the nearest destination. This mobile application helps domestic and foreign tourists select the closest tourist sites visited in Nanggung, Bogor Regency. The haversine method used to calculate the distance is the haversine method. The haversine method estimates the distance. Haversine will calculate the distance between the application user and the location of the surrounding tourist points located in the Nanggung sub-district. This study aims to calculate the length of application users to tourist sites. So that users get convenience in determining the closest location from the application user's position

British Geological Survey Annual Science Review 2012-13

The British Geological Survey (BGS) is part of the Natural Environment Research Council and is its principal supplier of national capability in geoscience. We advance understanding of the structure, properties and processes of the solid Earth system through interdisciplinary surveys, monitoring, modelling and research for the benefit of society. We are the UK’s premier provider of objective and authoritative geoscientific data, information and knowledge for creating wealth, using natural resources sustainably, reducing risk and living with the impacts of environmental change. Our vision To be the world’s leading centre for geoscience impact

The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems

Real-Time Urban Weather Observations for Urban Air Mobility

Cities of the future will have to overcome congestion, air pollution and increasing infrastructure cost while moving more people and goods smoothly, efficiently and in an eco-friendly manner. Urban air mobility (UAM) is expected to be an integral component of achieving this new type of city. This is a new environment for sustained aviation operations. The heterogeneity of the urban fabric and the roughness elements within it create a unique environment where flight conditions can change frequently across very short distances. UAM vehicles with their lower mass, more limited thrust and slower speeds are especially sensitive to these conditions. Since traditional aviation weather products for observations and forecasts at an airport on the outskirts of a metropolitan area do not translate well to the urban environment, weather data for low-altitude urban airspace is needed and will be particularly critical for unlocking the full potential of UAM. To help address this need, crowdsourced weather data from sources prevalent in urban areas offer the opportunity to create dense meteorological observation networks in support of UAM. This paper considers a variety of potential observational sources and proposes a cyber-physical system architecture, including an incentive-based crowdsensing application, which empowers UAM weather forecasting and operations

An offline–online Web-GIS Android application for fast data acquisition of landslide hazard and risk

Regional landslide assessments and mapping have been effectively pursued by research institutions, national and local governments, non-governmental organizations (NGOs), and different stakeholders for some time, and a wide range of methodologies and technologies have consequently been proposed. Land-use mapping and hazard event inventories are mostly created by remote-sensing data, subject to difficulties, such as accessibility and terrain, which need to be overcome. Likewise, landslide data acquisition for the field navigation can magnify the accuracy of databases and analysis. Open-source Web and mobile GIS tools can be used for improved ground-truthing of critical areas to improve the analysis of hazard patterns and triggering factors. This paper reviews the implementation and selected results of a secure mobile-map application called ROOMA (Rapid Offline–Online Mapping Application) for the rapid data collection of landslide hazard and risk. This prototype assists the quick creation of landslide inventory maps (LIMs) by collecting information on the type, feature, volume, date, and patterns of landslides using open-source Web-GIS technologies such as Leaflet maps, Cordova, GeoServer, PostgreSQL as the real DBMS (database management system), and PostGIS as its plug-in for spatial database management. This application comprises Leaflet maps coupled with satellite images as a base layer, drawing tools, geolocation (using GPS and the Internet), photo mapping, and event clustering. All the features and information are recorded into a GeoJSON text file in an offline version (Android) and subsequently uploaded to the online mode (using all browsers) with the availability of Internet. Finally, the events can be accessed and edited after approval by an administrator and then be visualized by the general public

Geo Location of Mobile Device

The proliferation of cellular network enabled users through various positioning tools to track locations; location information is being continuously captured from mobile phones, where a prototype is created that enables to detect locations based on using the two invariant models for Global Systems for Mobile (GSM) and Universal Mobile Telecommunications System (UMTS). The smartphone application on an Android platform applies the location-sensing run as a background process, and the localization method is based on cell phones. The proposed application is associated with remote server and used to track a smartphone without permissions and Internet. Mobile stores data location information in the database (SQLite) and then transfers it into location API to obtain locations’ result implemented in Google Maps. Track a smartphone with fixed identifiers mostly SSN (SIM (Subscriber Identity Module) Serial Number) and IMEI (International Mobile Equipment Identity) derived from an identifying string unique to the user’s device. The location result is moderately correct according to the cellular networks GSM and UMTS, which are used for obtaining location information

Explotación de nuevas oportunidades científicas de los sistemas de posicionamiento global por satélite (GNSS) desde una perspectiva intensiva en datos

[EN] With the current GNSS infrastructure development plans, over 120 GNSS satellites (including European Galileo satellites)will provide, already this decade, continuous data, in several frequencies, without interruption and on a permanent basis.This global and permanent GNSS infrastructure constitutes a major opportunity for GNSS science applications. In themeantime, recent advances in technology have contributed "de-facto" to the deployment of a large GNSS receiver arraybased on Internet of Things (IoT), affordable smart devices easy to find in everybody’s pockets. These devices – evolvingfast at each new generation – feature an increasing number of capabilities and sensors able to collect a variety ofmeasurements, improving GNSS performance. Among these capabilities, Galileo dual band smartphones receivers andAndroid’s support for raw GNSS data recording represent major steps forward for Positioning, Navigation and Timing (PNT)data processing improvements. Information gathering from these devices, commonly referred as crowdsourcing, opensthe door to new data-intensive analysis techniques in many science domains. At this point, collaboration between variousresearch groups is essential to harness the potential hidden behind the large volumes of data generated by thiscyberinfrastructure. Cloud Computing technologies extend traditional computational boundaries, enabling execution ofprocessing components close to the data. This paradigm shift offers seamless execution of interactive algorithms andanalytics, skipping lengthy downloads and setups. The resulting scenario, defined by a GNSS Big Data repository with colocatedprocessing capabilities, sets an excellent basis for the application of Artificial Intelligence / Machine Learning (ML)technologies in the context of GNSS. This unique opportunity for science has been recognized by the European SpaceAgency (ESA) with the creation of the Navigation Scientific Office, which leverages on GNSS infrastructure to deliverinnovative solutions across multiple scientific domains.[ES] Con los planes actuales de desarrollo de la infraestructura GNSS, más de 120 satélites GNSS (incluidos los satélites europeos Galileo) proporcionarán, ya en esta década, datos continuos, en varias frecuencias, sin interrupciones y de forma permanente. Esta infraestructura GNSS global y permanente constituye una gran oportunidad para las aplicaciones científicas de GNSS. Mientras tanto, avances recientes han contribuido al despliegue de una red GNSS paralela basada en la Internet de las Cosas (IoT), asequibles dispositivos inteligentes fáciles de encontrar en todos los bolsillos. Estos dispositivos, que evolucionan rápidamente con cada nueva generación, acumulan un número creciente de funcionalidades y sensores capaces de recopilar una gran variedad de mediciones. Entre estas funcionalidades, los receptores de teléfonos inteligentes de banda dual Galileo y el soporte Android para la grabación de datos GNSS sin procesar representan pasos especialmente relevantes. La recopilación de información mediante estos dispositivos, comúnmente conocida como crowdsourcing, abre la puerta a nuevas técnicas de análisis de datos en múltiples dominios científicos. Llegados a este punto, la colaboración entre diversos grupos de investigación resulta esencial para aprovechar el potencial que se esconde en los grandes volúmenes de datos generados por esta ciberinfraestructura. Las tecnologías de Cloud Computing extienden los límites computacionales tradicionales permitiendo la ejecución de componentes de procesamiento cerca de los datos. Este cambio de paradigma ofrece una rápida ejecución de algoritmos y análisis interactivos, omitiendo largas descargas y configuraciones. El escenario resultante, definido por un repositorio GNSS Big Data con capacidades de procesamiento acopladas, establece una base excelente para la aplicación de tecnologías de Inteligencia Artificial / Aprendizaje Automático (ML). Esta oportunidad única para la ciencia ha sido reconocida por la Agencia Espacial Europea (ESA) con la creación de la Oficina Científica de Navegación, que aprovecha la infraestructura GNSS para ofrecer soluciones innovadoras en múltiples dominios científicos.This work was supported by the European Space Agency as part of Research and Development Programmes under Science and Navigation Directorates. The authors would like to thank the GNSS Science Advisory Committee and ESA Navigation Support Office for their support and suggestions. We also thank our Industrial partners, involved in science use cases assessment and implementation. Thanks also to the Science and Operations technical IT Unit at ESAC supporting the deployment of the GSSC Thematic Exploitation Platform. We would like to thank all data collection providers, with special thanks to IGS, ILRS, CDDIS, BKG and IGN for their sustained and remarkable support making possible the creation of the GSSC Repository at the core of this work.Navarro, V.; Ventura-Traveset, J. (2021). A data-intensive approach to exploit new GNSS science opportunities. En Proceedings 3rd Congress in Geomatics Engineering. Editorial Universitat Politècnica de València. 43-53. https://doi.org/10.4995/CiGeo2021.2021.12740OCS435

Geolocation Android Mobile Phones Using GSM/UMTS

The proliferation of cellular network enabled users through various positioning tools to track locations, location information is being continuously captured from mobile phones, created a prototype that enables detected location based on using the two invariant models for Global Systems for Mobile (GSM) and Universal Mobile Telecommunications System (UMTS). The smartphone application on an Android platform applies the location sensing run as a background process and the localization method is based on cell phones. The proposed application is associated with remote server and used to track a smartphone without permissions and internet. Mobile stored data location information in the database (SQLite), then transfer it into location API to obtain locations result implemented in Google Maps. Track a smartphone with fixed identifiers mostly SSN (SIM (Subscriber Identity Module) Serial Number) and IMEI (International Mobile Equipment Identity) derived from an identifying string unique to the user's device. The result located place is Moderate correct according to the (GSM) and (UMTS) cellular networks which is used for obtaining location information

Citizen Science and Geospatial Capacity Building

This book is a collection of the articles published the Special Issue of ISPRS International Journal of Geo-Information on “Citizen Science and Geospatial Capacity Building”. The articles cover a wide range of topics regarding the applications of citizen science from a geospatial technology perspective. Several applications show the importance of Citizen Science (CitSci) and volunteered geographic information (VGI) in various stages of geodata collection, processing, analysis and visualization; and for demonstrating the capabilities, which are covered in the book. Particular emphasis is given to various problems encountered in the CitSci and VGI projects with a geospatial aspect, such as platform, tool and interface design, ontology development, spatial analysis and data quality assessment. The book also points out the needs and future research directions in these subjects, such as; (a) data quality issues especially in the light of big data; (b) ontology studies for geospatial data suited for diverse user backgrounds, data integration, and sharing; (c) development of machine learning and artificial intelligence based online tools for pattern recognition and object identification using existing repositories of CitSci and VGI projects; and (d) open science and open data practices for increasing the efficiency, decreasing the redundancy, and acknowledgement of all stakeholders