Overview of positioning technologies from fitness-to-purpose point of view

Even though Location Based Services (LBSs) are being more and more widely-used and this shows a promising future, there are still many challenges to deal with, such as privacy, reliability, accuracy, cost of service, power consumption and availability. There is still no single low-cost positioning technology which provides position of its users seamlessly indoors and outdoors with an acceptable level of accuracy and low power consumption. For this reason, fitness of positioning service to the purpose of LBS application is an important parameter to be considered when choosing the most suitable positioning technology for an LBS. This should be done for any LBS application, since each application may need different requirements. Some location-based applications, such as location-based advertisements or Location-Based Social Networking (LBSN), do not need very accurate positioning input data, while for some others, e.g. navigation and tracking services, highly-accurate positioning is essential. This paper evaluates different positioning technologies from fitness-to-purpose point of view for two different applications, public transport information and family/friend tracking

Discrete choice models with capacity constraints: an empirical analysis of the housing market of the greater Paris region

Discrete choice models are based on the idea that each user can choose both freely and independently from other users in a given set of alternatives. But this is not the case in several situations. In particular, limitations and interactions can occur when the number of available products of one type is smaller than the total demand for this type. As a consequence, some individuals can be denied their preferred choice. We develop a methodology to address those constraints and we apply it to residential location choice, where our empirical data suggest that availability constraints may bias actual choices. The analysis provides some theoretical developments and elaborates an iterative procedure for estimating demand in the presence of capacity constraints. The empirical application relies on the location choice model developed and estimated in [6] for Ile de France (Paris region) and generalizes it to integrate capacity constraints.Residential location, constrained Logit, capacity constraints, sampling, Ilede- France

Integrated Parking System For Troubleshooting Parking Location Search In Big City Based Internet Of Things (IoT)

Parking space is one of the important needs for vehicle owners.along with the increasing number of vehicles, especially in big cities have an impact on the difficulty of finding parking locations. Sometimes it takes a few minutes even hours just to find the location of available parking. This is the background of making Finding Parking. Application Finding Parking is an online parking search system based on the Internet of Things.on the parking location there are infrared sensors that aims to detect the presence of vehicles, data from existing sensors this parking location will be processed and sent to users who look for parking locations via Android to know the location of the nearest parking. The purpose of this Final Project is to help vehicle owners find available parking locations.Finding Parking application created by Waterfall method, Java programming language, MySQL database that is run by users through Android smartphone .Based on the tests that have been done can be obtained results that Finding Parking application is able to provide information to the owner of the vehicle regarding the availability of parking locations

Identification and estimation of nonclassical nonlinear errors-in-variables models with continuous distributions using instruments

While the literature on nonclassical measurement error traditionally relies on the availability of an auxiliary dataset containing correctly measured observations, this paper establishes that the availability of instruments enables the identification of a large class of nonclassical nonlinear errors-in-variables models with continuously distributed variables. The main identifying assumption is that, conditional on the value of the true regressors, some "measure of location" of the distribution of the measurement error (e.g. its mean, mode or median) is equal to zero. The proposed approach relies on the eigenvalue-eigenfunction decomposition of an integral operator associated with specific joint probability densities. The main identifying assumption is used to order the eigenfunctions so that the decomposition is unique. The authors propose a convenient sieve-based estimator, derive its asymptotic properties and investigate its finite-sample behavior through Monte Carlo simulations. An example of application to the relationship between earnings and divorce rates is also provided.

Pembuatan Aplikasi Sistem Informasi Geografis Kampus Universitas Diponegoro Berbasis Android

Diponegoro University is one of the largest university in Central Java with approximately 1.352.054 m2 area of campus in Tembalang. For an area with that number, the information's availability about location of each major in University of Diponegoro was insufficient. The need for information on the campus demanding the availability of information systems that are informative and provide convenience for everyone who needs information about the campus. This research using spatial data coordinates of the position and photos of objects and attribute data in the form of a campus building inventory data. Android popularity makes it being this application's platform while the coordinate data collection using GPS handheld. Maps using Google Maps then developed using Android SDK framework., MySQL with phpMyAdmin features used as database.. The final result of this research is the application of Diponegoro University campus map Android-based equipped with information of each building, major, lecturers, and images to look around the location of building we were looking for

Characterizing Driving Context from Driver Behavior

Because of the increasing availability of spatiotemporal data, a variety of data-analytic applications have become possible. Characterizing driving context, where context may be thought of as a combination of location and time, is a new challenging application. An example of such a characterization is finding the correlation between driving behavior and traffic conditions. This contextual information enables analysts to validate observation-based hypotheses about the driving of an individual. In this paper, we present DriveContext, a novel framework to find the characteristics of a context, by extracting significant driving patterns (e.g., a slow-down), and then identifying the set of potential causes behind patterns (e.g., traffic congestion). Our experimental results confirm the feasibility of the framework in identifying meaningful driving patterns, with improvements in comparison with the state-of-the-art. We also demonstrate how the framework derives interesting characteristics for different contexts, through real-world examples.Comment: Accepted to be published at The 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2017

Project-, problem-, and inquiry-based learning

Inquiry-based learning and related approaches such as project- and problem-based learning respond to the increased availability of information in a networked world by emphasizing the location and application of information by the learner rather than its transmission from teacher to learner. The role of teacher necessarily shifts toward being a designer and facilitator of projects through which students learn rather than the primary source of knowledge in the classroom. That shift is facilitated by the application of digital technologies to initiate learning activities, access and process information, and present results. It confronts teachers with challenges in relation to the relative emphases on content and process in learning and assessment, and the role of learners in deciding what is learned and how

Improving the Quality of Healthcare Using Big Data

In India there is a lack of doctorrsquos availability in rural areas compare to urban areas because of which the number of deaths is increasing in the rural areas. To solve this issue we are building an android application (Healtho) which will recommend the disease based on the symptoms given by the end user. Basically, a recommended system will be used by using Hadoop with mahout that is a Big Data concept. By using android as a platform we can provide higher availability of the system to the end user and provide some emergency services like location of nearby Hospitals and blood bank. The system also provides the medicine time (Meditime) in which the end user may come to know at what time the medicine is to be taken. This system could mostly be used by the people who live in rural area because there is lack of doctorrsquos availability and hospitals.nbs