ELEKSPOT: EVALUATION PLAN Minkyu Lee 2009. 9. 4. Agenda  Project Goal  Objective of Evaluation  Case Study: OpenStreetMap  Quality of GI  Phases.

Slides:



Advertisements
Similar presentations
Guidelines for Basic Geographic Data DA AIP Phase 2 Kickoff Workshop September 25-26, 2008 Boulder, Colorado, USA Yoshikazu FUKUSHIMA Secretariat.
Advertisements

Map matching algorithm for data conflation – an open source approach
An Interactive-Voting Based Map Matching Algorithm
Community Mapping ACM Mid-Southeast Conference November 12, 2010
FM-BASED INDOOR LOCALIZATION TsungYun 1.
1 Fieldwork logistics and data quality control procedures Kathleen Beegle Workshop 17, Session 2 Designing and Implementing Household Surveys March 31,
Locating in fingerprint space: wireless indoor localization with little human intervention. Proceedings of the 18th annual international conference on.
Accuracy Characterization for Metropolitan-scale Wi-Fi Localization Yu-Chung Cheng (UCSD, Intel Research) Yatin Chawathe (Intel Research) Anthony LaMarca.
ACCURACY CHARACTERIZATION FOR METROPOLITAN-SCALE WI-FI LOCALIZATION Presented by Jack Li March 5, 2009.
A Platform for the Evaluation of Fingerprint Positioning Algorithms on Android Smartphones C. Laoudias, G.Constantinou, M. Constantinides, S. Nicolaou,
Accuracy Characterization for Metropolitan-scale Wi-Fi Localization Ying Wang, Xia Li Ying Wang, Xia Li.
A reactive location-based service for geo-referenced individual data collection and analysis Xiujun Ma Department of Machine Intelligence, Peking University.
IT Learning Programme The Evaluations Tool – Workshop 3 18 th May 2010.
How Many Volunteers Does It Take To Map An Area Well? Dr Muki Haklay Department of Civil, Environmental and Geomatic Engineering, UCL
Homework 2 Similar to the experiments done in the paper “Online Game QoE Evaluation using Paired Comparisons “ “Online Game QoE Evaluation using Paired.
Kurt Menke, GISP OpenStreetMap. What is it? OpenStreetMap (OSM) Not software It's a collaborative project to create a free & editable map of the world.
Review: Accuracy Characterization for Metropolitan-scale Wi-Fi Localization Authors: Cheng, Chawathe, LaMacra, Krumm 2005 Slides Adapted from Cheng, MobiSys.
1 Service Learning Group D Mentors  Dr. M. N. Kaumal  Dr. H. R. Perera Cooperate Partner.
Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) Model  ADAPTS scheduling process model: –Simulation of how activities are planned.
Ubiquitous Advertising: the Killer Application for the 21st Century Author: John Krumm Presenter: Anh P. Nguyen
Integrated Urban Forest Assessments: Stewardship Capacity and Operations Weston Brinkley, Cascade Land Conservancy Kathy Wolf, U of WA/Forest Service Research.
Workshop 3. Overview of Workshop 3 Today we will explore how the following processes can inform improvements in practice: Enabling the participation of.
Prof. Dong-Soo Han’s Intelligent Service Integration Laboratory (I.S.I Lab.), CS KAIST, 2010.
Comparing Small Area Statistics Across UK Nations: Scale, time and availability Small Area Stats Event Edinburgh, Scotland 5 October, 2010 Brian Webb /
Research Methods Key Points What is empirical research? What is the scientific method? How do psychologists conduct research? What are some important.
Research methodology Data Collection tools and Techniques.
 New Cartographers Open Street Map. Outline for Today’s Class  Recap/Clarifications  What’s Next? Crisis Mapping Technologies  Quick NGO Team Meeting.
United Nations Regional Workshop on the 2010 World Programme on Population and Housing Censuses: Census Evaluation and Post Enumeration Surveys Bangkok,
Department of Innovation & Technology City of Boston Five Key Ways to Be a Successful Project Manager March 2014.
The OpenStreetMap project The OpenStreetMap Project is one of the best known examples of Volunteered Geographic Information on the Internet today. The.
Anonymous Localization of Wireless Terminals in Indoors Shahrokh Valaee Wireless and Internet Research Lab (WIRLab) Dept of Electrical and Computer Engineering.
Crowdsourcing Color Perceptions using Mobile Devices Jaejeung Kim 1, Sergey Leksikov 1, Punyotai Thamjamrassri 2, Uichin Lee 1, Hyeon-Jeong Suk 2 1 Dept.
Mobile commerce services in Slovenia from user’s point of view Uroš Hribar, univ.dipl.org eCommerce Center Faculty of Organizational Sciences University.
Action Research for 21 st Century Schools Workshop 2.
ACTION PLAN Third Group Training Course in Application of Information and Communications Technology to Production and Dissemination of Official Statistics.
GeoPlannerSM for ArcGIS®: An Introduction
OpenStreetMap The Free Wiki World Map. The name of the project i am researching is open street map. The address is org/
Human Tracking System Using DFP in Wireless Environment 3 rd - Review Batch-09 Project Guide Project Members Mrs.G.Sharmila V.Karunya ( ) AP/CSE.
Naam, Afdeling 07/01/2009 To localise or to be localised with Wi-Fi in the Hubei Museum? Edward Verbree, Sisi Zlatanova, Karl van Winden, Eva van der Laan,
ArcGIS Editor for OpenStreetMap: Contributing Data Christine White.
War Walking vs. War Driving Trying to find the reasons why war walking radio map performs better.
restricted external Evaluating the vinspired 24/24 programme Ewan King, director OPM 30 September
Travel Data and the Smartphone: Building an International Travel Dataset One Android User at a Time GIL TAL MICHAEL NICHOLAS MATTHEW FAVETTI.
Training Program Music and Movement
Jin Yan Embedded and Pervasive Computing Center
Fall Recap ___ Chapter at University Semester 2013.
Introduction to IEEE ICDM Data Mining Contest (ICDM DMC 2007)
Web: ~ laoudias/pages/platform.htmlhttp://www2.ucy.ac.cy/ ~ laoudias/pages/platform.html
TIU Tracking System Introduction Intel's large and complex validation labs contain many Testing Interface Unit's(TIU) used in validating hardware. A TIU.
OpenStreetMap and open maps in Estonia. MTÜ Avatud maakaardi selts Registered NGO “Open Map Society” Established 2009 August Why? To do projects Estonian.
Indoor positioning systems Kyle Hampton. Outline Introduction Uses Players Techniques Challenges Future Conclusion.
Hybrid Indoor Positioning with Wi-Fi and Bluetooth: Architecture and Performance IEEE Mobile Data Management 2013 Artur Baniukevic†, Christian S. Jensen‡,
Smartphone-based Wi-Fi Pedestrian-Tracking System Tolerating the RSS Variance Problem Yungeun Kim, Hyojeong Shin, and Hojung Cha Yonsei University Bing.
Population vs Sample Population = The full set of cases Sample = A portion of population The need to sample: More practical Budget constraint Time constraint.
Micromapping Marek Strassenburg-Kleciak aka Marek Kleciak.
® IndoorGML Introduction to the OGC Standard for Indoor Spatial Information and several applications Ki-Joune Li, IndoorGML Chair George Percivall, OGC.
Esri UC 2014 | Technical Workshop | Enhancing Web Map Performance in ArcGIS Online Julia Guard & Melanie Summers.
Improved socio-economic services for a more social microfinance.
Sensor-Assisted Wi-Fi Indoor Location System for Adapting to Environmental Dynamics Yi-Chao Chen, Ji-Rung Chiang, Hao-hua Chu, Polly Huang, and Arvin Wen.
Urban Sensing Based on Human Mobility
Enhancing Web Map Performance in ArcGIS Online
Information Presentation
Project: Integrating Indoor Localization to Gaming
Shankar Chandrasekaran
AirPlace Indoor Positioning Platform for Android Smartphones
Deep Visual-Semantic Alignments for Generating Image Descriptions
Making a good thematic map – Extracting or collecting geographic data
The bottom-up approach: Challenges in the production of statistical grid data Rina Tammisto European Forum for Geostatistics, Workshop 1- 3 October 2008.
Extracting or collecting geospatial data
ArcGIS Editor for OpenStreetMap: Contributing data
Presentation transcript:

ELEKSPOT: EVALUATION PLAN Minkyu Lee

Agenda  Project Goal  Objective of Evaluation  Case Study: OpenStreetMap  Quality of GI  Phases of Evaluation

Project Goal  Project Goal  Enabling mobile devices to recognize indoor urban places based on Wi-Fi signal strength.  Question  How to cover very large amount of urban places?  Answer  Key is to build urban radiomap by citizen participation

Objective of Evaluation  There are many factors to evaluate…  What we want to show in this research is…  Then, we have to evaluate that It is possible to build city-wide radiomap by citizen participation (crowdsourcing) How crowdsourced urban radiomap is good

Case Study: OpenStreetMap  OpenStreetMap Overview  To build free street map for major cities by crowdsourcing based on wiki.

Case Study: OpenStreetMap  The 5 steps to making a map  Collect Data  Upload Data  Create/Edit OSM data  Label Data, and Add details  Render and Use Map!

Case Study: OpenStreetMap  Mapping party  To encourage and help people to edit map  Experienced mappers initiates

Case Study: OpenStreetMap  Evaluation of OpenStreetMap  M. Haklay, “How good is OpenStreetMap information? A comparative study of OpenStreetMap and Ordnance Survey datasets for London and the rest of England”  Ordnance Survey vs. OpenStreetMap  Comparison Factors Positional accuracy Completeness

Quality of GI (ISO 19113,19114)  Lineage  Positional accuracy  Attribute accuracy  Logical consistency  Completeness  Semantic accuracy  Usage, purpose and constraints  Temporal quality

Phases of Evaluation  Planning  Schedule, Budget, Area selection, …  Data Collection  Urban Radiomapping Workshop  Data Analysis  Accuracy  Completeness

Planning  Selected Area  Seoul, Gangnam-gu, Samseong-dong Building: 3200 Room: Public Space: 914

Planning  Schedule  Workshops About 200 places can be collected at a workshop Period : ~ Workshop: 5 times (once/twice a week) Participants: 5 ~ 10 persons  Budget  Devices 5 more T-mobile G1 devices. (60 만원 *5= 약 300 만원 )  Workshops 5 Workshops (10 명 *7 만원 (1 인 )*5 회 = 약 350 만원 )

Data Collection  Workshop Agenda  Project overview (20 min.)  Usage training and guide (40 min.)  Data collection (6 hrs)  Data upload and editing (40 min.)  Questionnaire (20 min.)  Eating and drinking! (~)

Data Collection  Target  Public spaces only (café, shop, theater, …)  Room-level  Mandatory Data  Place ID  Name  Place Scale  Latitude/Longitude  Fingerprints  Picture  Tags

Data Collection  Recruiting volunteers  Lab. members  Recruit in KAIST community (ara, eye)  Recruit in Android developer community

Data Analysis  Accuracy  Positional accuracy Precision Error distance (logical distance)  Attribute accuracy Room-levelFloor-levelBuilding-level 92.3%98.6%99.7% ExactAdj. roomSame floorSame bldg.Other bldg. 92.3%5.2%2%0.5%0% Place IDNameScaleTagsPosition 88%91.8%93%96.2%91.2% example

Data Analysis  Accuracy  Test dataset Collect test dataset after data collection Select randomly 300 public places in samseong-dong Collect 10 fingerprints for each place Conduct experiment in laboratory

Data Analysis  Completeness  Region completeness (samseong-dong) Building (%) – visualize on Google map Room (%)  Public space completeness (semseong-dong) Compare against public places in Daum Theme Map. Completeness (%)  Completeness of specific building (COEX) Room-coverage (%)

Data Analysis  Etc. (Statistics)  Averages APs in samseong-dong  Number of APs for each tag (café, shop, theater, …)  Number of contributed places for each person  …

Q&A  Thank you for listening