Computer Science
Active and Programmable Networks
Active safety systems
Ad Hoc & Sensor Network
Ad hoc networks for pervasive communications
Adaptive, autonomic and context-aware computing
Advance Computing technology and their application
Advanced Computing Architectures and New Programming Models
Advanced control and measurement
Aeronautical Engineering,
Agent-based middleware
Alert applications
Automotive, marine and aero-space control and all other control applications
Autonomic and self-managing middleware
Autonomous vehicle
Biochemistry
Bioinformatics
BioTechnology(Chemistry, Mathematics, Statistics, Geology)
Broadband and intelligent networks
Broadband wireless technologies
CAD/CAM/CAT/CIM
Call admission and flow/congestion control
Capacity planning and dimensioning
Changing Access to Patient Information
Channel capacity modelling and analysis
Civil Engineering,
Cloud Computing and Applications
Collaborative applications
Communication application
Communication architectures for pervasive computing
Communication systems
Computational intelligence
Computer and microprocessor-based control
Computer Architecture and Embedded Systems
Computer Business
Computer Sciences and Applications
Computer Vision
Computer-based information systems in health care
Computing Ethics
Computing Practices & Applications
Congestion and/or Flow Control
Content Distribution
Context-awareness and middleware
Creativity in Internet management and retailing
Cross-layer design and Physical layer based issue
Cryptography
Data Base Management
Data fusion
Data Mining
Data retrieval
Data Storage Management
Decision analysis methods
Decision making
Digital Economy and Digital Divide
Digital signal processing theory
Distributed Sensor Networks
Drives automation
Drug Design,
Drug Development
DSP implementation
E-Business
E-Commerce
E-Government
Electronic transceiver device for Retail Marketing Industries
Electronics Engineering,
Embeded Computer System
Emerging advances in business and its applications
Emerging signal processing areas
Enabling technologies for pervasive systems
Energy-efficient and green pervasive computing
Environmental Engineering,
Estimation and identification techniques
Evaluation techniques for middleware solutions
Event-based, publish/subscribe, and message-oriented middleware
Evolutionary computing and intelligent systems
Expert approaches
Facilities planning and management
Flexible manufacturing systems
Formal methods and tools for designing
Fuzzy algorithms
Fuzzy logics
GPS and location-based app
Maps are everywhere—on the Internet, in our car, and even on our mobile phone. Moreover, maps of the twenty-first century are not just paper diagrams folded like an accordion. Maps today are colorful, searchable, interactive, and shared. This transformation of the static map into dynamic and interactive multimedia reflects the integration of technological innovation and vast amounts of geographic data. The key technology behind this integration, and subsequently the maps of the twenty-first century, is geographic information systems or GIS.
Gis Geographical Information System FundamentalsUroosa Samman
Gis, Geographical Information System Fundamentals. This presentation includes a complete detail of GIS and GIS Softwares. It will help students of GIS and Environmental Science.
Computer Science
Active and Programmable Networks
Active safety systems
Ad Hoc & Sensor Network
Ad hoc networks for pervasive communications
Adaptive, autonomic and context-aware computing
Advance Computing technology and their application
Advanced Computing Architectures and New Programming Models
Advanced control and measurement
Aeronautical Engineering,
Agent-based middleware
Alert applications
Automotive, marine and aero-space control and all other control applications
Autonomic and self-managing middleware
Autonomous vehicle
Biochemistry
Bioinformatics
BioTechnology(Chemistry, Mathematics, Statistics, Geology)
Broadband and intelligent networks
Broadband wireless technologies
CAD/CAM/CAT/CIM
Call admission and flow/congestion control
Capacity planning and dimensioning
Changing Access to Patient Information
Channel capacity modelling and analysis
Civil Engineering,
Cloud Computing and Applications
Collaborative applications
Communication application
Communication architectures for pervasive computing
Communication systems
Computational intelligence
Computer and microprocessor-based control
Computer Architecture and Embedded Systems
Computer Business
Computer Sciences and Applications
Computer Vision
Computer-based information systems in health care
Computing Ethics
Computing Practices & Applications
Congestion and/or Flow Control
Content Distribution
Context-awareness and middleware
Creativity in Internet management and retailing
Cross-layer design and Physical layer based issue
Cryptography
Data Base Management
Data fusion
Data Mining
Data retrieval
Data Storage Management
Decision analysis methods
Decision making
Digital Economy and Digital Divide
Digital signal processing theory
Distributed Sensor Networks
Drives automation
Drug Design,
Drug Development
DSP implementation
E-Business
E-Commerce
E-Government
Electronic transceiver device for Retail Marketing Industries
Electronics Engineering,
Embeded Computer System
Emerging advances in business and its applications
Emerging signal processing areas
Enabling technologies for pervasive systems
Energy-efficient and green pervasive computing
Environmental Engineering,
Estimation and identification techniques
Evaluation techniques for middleware solutions
Event-based, publish/subscribe, and message-oriented middleware
Evolutionary computing and intelligent systems
Expert approaches
Facilities planning and management
Flexible manufacturing systems
Formal methods and tools for designing
Fuzzy algorithms
Fuzzy logics
GPS and location-based app
Maps are everywhere—on the Internet, in our car, and even on our mobile phone. Moreover, maps of the twenty-first century are not just paper diagrams folded like an accordion. Maps today are colorful, searchable, interactive, and shared. This transformation of the static map into dynamic and interactive multimedia reflects the integration of technological innovation and vast amounts of geographic data. The key technology behind this integration, and subsequently the maps of the twenty-first century, is geographic information systems or GIS.
Gis Geographical Information System FundamentalsUroosa Samman
Gis, Geographical Information System Fundamentals. This presentation includes a complete detail of GIS and GIS Softwares. It will help students of GIS and Environmental Science.
This presentation gives a basic idea of GIS and its uses and different softwares commonly used. It also covers difference between vector and raster. General idea of projection is also covered.
Digital Elevation Model (DEM) is the digital representation of the land surface elevation with respect to any reference datum. DEM is frequently used to refer to any digital representation of a topographic surface. DEM is the simplest form of digital representation of topography. GIS applications depend mainly on DEMs, today.
Geographical Information System (GIS) Georeferencing and Digitization, Bihar ...Kamlesh Kumar
This work is an effort to share Geographical Information System: Georeferencing, digitization and map making steps through QGIS 2.0.1
Georeferencing
Digitization of Topographical sheet
Point
Line
Area
Bihar Map
District Headquarters
Railway of Bihar
District Boundaries
Thematic Maps (Literacy & Sex Ratio)
Why GIS use is prevalent in natural resource management Evolution of the development of GIS technology and key figures Common spatial data collection techniques and input devices that are available Common GIS output processes that are typical in natural resource management The broad types of GIS software that are available.
Introduction to GIS - Basic spatial concepts - Coordinate Systems - GIS and Information Systems – Definitions – History of GIS - Components of a GIS – Hardware, Software, Data, People, Methods – Proprietary and open source Software - Types of data – Spatial, Attribute data- types of attributes – scales/ levels of measurements.
This presentation gives a basic idea of GIS and its uses and different softwares commonly used. It also covers difference between vector and raster. General idea of projection is also covered.
Digital Elevation Model (DEM) is the digital representation of the land surface elevation with respect to any reference datum. DEM is frequently used to refer to any digital representation of a topographic surface. DEM is the simplest form of digital representation of topography. GIS applications depend mainly on DEMs, today.
Geographical Information System (GIS) Georeferencing and Digitization, Bihar ...Kamlesh Kumar
This work is an effort to share Geographical Information System: Georeferencing, digitization and map making steps through QGIS 2.0.1
Georeferencing
Digitization of Topographical sheet
Point
Line
Area
Bihar Map
District Headquarters
Railway of Bihar
District Boundaries
Thematic Maps (Literacy & Sex Ratio)
Why GIS use is prevalent in natural resource management Evolution of the development of GIS technology and key figures Common spatial data collection techniques and input devices that are available Common GIS output processes that are typical in natural resource management The broad types of GIS software that are available.
Introduction to GIS - Basic spatial concepts - Coordinate Systems - GIS and Information Systems – Definitions – History of GIS - Components of a GIS – Hardware, Software, Data, People, Methods – Proprietary and open source Software - Types of data – Spatial, Attribute data- types of attributes – scales/ levels of measurements.
cf. city flows - A comparative visualization of bike sharing systemsTill Nagel
cf. city flows is a comparative visualization environment of urban bike mobility designed to help citizens casually analyze three bike-sharing systems in the context of a public exhibition space.
By Till Nagel and Christopher Pietsch.
Urban Complexity Lab, FH Potsdam
<a>http://uclab.fh-potsdam.de/</a>
This talk introduces the project and some of its goals and visualizations, and shows our design process in analyzing the data and designing the visualizations.
cf. city flows was exhibited at the Streams and Traces in November 2015 in Berlin. Find more information at http://streamsandtraces.com/
More information coming soon.
Improving Navigation: Automated Name Extraction for Separately Mapped Pedestr...Anita Graser
Paper: http://hw.oeaw.ac.at/0xc1aa500e%200x00324afb.pdf
Abstract: Navigation instructions in pre- and on-trip routing services are usually based on street names and types, distances, and turn directions. However, in digital street graphs it is common that street names for separately mapped pedestrian and cycle links are missing. This leads to unsatisfactory instructions containing “unknown road” records. Often, these unnamed links run parallel to a named road, and it would be beneficial to use this information to generate instructions similar to “follow the sidewalk along Street A”, whereby “Street A” has to be determined by an algorithm. This paper introduces the Unnamed Link Naming Problem (ULNP) and presents a new approach to automatically extract suitable names to describe separately mapped pedestrian and cycle links. The approach has been tested using OpenStreetMap data and manually generated ground truth data for the second district of the city of Vienna, Austria. Results show that our best method achieves 90.7% correct matches in this challenging setting.
Time Manager Workshop at #QGIS2015 Conference in NodeboAnita Graser
This talk presents QGIS visualization tools with a focus on efficient use of layer styling to both explore and present spatial data. Examples include the recently added heatmap style as well as sophisticated rule-based and data-defined styles. Additionally, we will have a special look at exploring and presenting spatio-temporal data using the Time Manager plugin. As a special treat, we will look into creating time-dependent styles using expression-based styling to access the current Time Manager timestamp.
The second part is a workshop about hands-on experience with Time Manager. Time Manager makes it possible to explore spatio-temporal data by creating animations directly in QGIS. We’ll cover data requirements, configuration, as well as time-dependent styling and creating animations.Visual exploration and presentation of spatial data using QGIS
http://anitagraser.com/2015/05/22/time-manager-workshop-at-qgis2015/
IoT and the Autonomous Vehicle in the Clouds: Simultaneous Localization and M...Spark Summit
Processing real-time analytics of big data streams from sensor data will continue to be an important task as embedded technology increases and we continue to generate new types and ways of data analysis, particularly in regard to the Internet of Things (IoT). Robotics models many of these key challenges well and incorporates the possibility of high- throughput streams as well as complex online machine learning and analytics algorithms. These challenges make it an almost ideal candidate for in depth analysis of real-time streaming analytics.
We look at a simultaneous localization and mapping (SLAM) problem, an ongoing research area in robotics for autonomous vehicles, and well recognized as a non-trivial problem space in both industry and research. We will use a new integrated framework on Kafka and Spark Streaming to explore a constrained SLAM problem using online algorithms to navigate and map a space in real time.
We present benchmarks of our open-source robot’s integration with Kafka and Spark Streaming for performance against other SLAM algorithms currently in use, explore some of the challenges we faced in our implementation, and make recommendations for improvement of performance and optimization on our framework.
Finally, new to this talk, we demo real-time usage of our implementation with the Turtlebot II and explore relevant benchmarks and their implications on the future of autonomous vehicles in the IoT and cloud analytics space.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Behavioral Analytics with Smartphone Data. Talk at Strata + Hadoop World 2014...Joerg Blumtritt
Joerg Blumtritt, Datarella, at Strata+Hadoop World 2014, Barcelona
Smartphones are the most common wearable devices. In every single smartphone, more than 20 sensors continuously track all kind of behavioral and environmental data, from geo-location to even remote influences like magnetic field and local gravitation.
To develop meaningful models of users’ behavior, we need to put the technical measurements into context. This is done via interactions with an app. We at Datarella trigger the interactions on the phone by geo-fencing (coming near a pre-defined location, e.g. a retailer’s outlet), and other “complex events” using an Event Processing Language (EPL) and a Complex Event Processing Engine (CEPE).
We want also show best practices how to deal with privacy and informational self-determination, and how to give users a fair deal and transparancy to the data, that we collect by their cooperation in our projects.
Get the Strata + Hadoop World in Barcelona 2014 Complete Video Compilation here: http://strataconf.com/strataeu2014/public/sv/q/608
This content describes Call Detail Records (CDR) data format, data acquisition method, visualize in Mobmap and the applications for disaster management.
Design of Industrial Robot Sorting System Based on A * Search Algorithm...........................................1
Wang Hongmei, Zhao Xueliang and Du Haitao Zhang Lanhua
Digitizing Traditional Filmmaking Process for Education and Industry.................................................... 14
Zeeshan Jawed and Dr Nandita Sengupta
The Socio-Economic Impact of Identity Thefts and Cybercrime: Preventive Measures and Solutions . 32
Dr. Nabie Y. Conteh and Quinnesha N. Staton
One of goal of this project is seamless integration of indoor and outdoor space on top of web browser. This project aims at developing a sort of plugin for WebGL glove(Web World Wind, Cesium) to expand its functionalities and usabilities to indoor space and architectural (BIM) areas. MAGO3D can import IFC (Industry Foundation Classes) data from architecture files. And then MAGO3D can visualise massive indoor data, at least 100k objects, in a single scene seamlessly with traditional outdoor 3D GIS objects. Users can now manage and handle almost every geospatial object from desktop level to space level with MAGO3D. This project will evolve to manage and service more dynamic data such as IoT (Internet of Things), climate and weather data, and transportation.
Sensing City Potential through Social Data @ ICMU2014 PanelYutaka Arakawa
Introduction of my researches in social sensing. Especially, the following two topics are explained.
1. Sightseeing spots retrieving
2. Photo spots recommendation
Smartphones can track all kind of mobility data. From the readings of the phone's sensors like gyroscope, acceleration, magnetic field (aka compass), etc. it is possible to tell not only where people had been, but what means of transportation they had used, and even, when driving a car, if they would keep their distance, if they would speed, or show other forms of dangerous behavior. Thus smartphone data can be used to bridge technology until cars become fully connected.
Markerless motion capture for 3D human model animation using depth cameraTELKOMNIKA JOURNAL
3D animation is created using keyframe based system in 3D animation software such as Blender and Maya. Due to the long time interval and the need of high expertise in 3D animation, motion capture devices were used as an alternative and Microsoft Kinect v2 sensor is one of them. This research analyses the capabilities of the Kinect sensor in producing 3D human model animations using motion capture and keyframe based animation system in reference to a live motion performance. The quality, time interval and cost of both animation results were compared. The experimental result shows that motion capture system with Kinect sensor consumed less time (only 2.6%) and cost (30%) in the long run (10 minutes of animation) compare to keyframe-based system, but it produced lower quality animation. This was due to the lack of body detection accuracy when there is obstruction. Moreover, the sensor’s constant assumption that the performer’s body faces forward made it unreliable to be used for a wide variety of movements. Furthermore, standard test defined in this research covers most body parts’ movements to evaluate other motion capture system.
We discuss how GPS and IMU work together in the context of capturing vehicle motion and a simple technique for creating a trajectory from a sample set of IMU data. After part 1 & 2 you will be able to generate a point cloud by fusing the IMU trajectory and the LiDAR data.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
1. FOSS4G 2015
14 Sep 2015
Hiroaki Sengoku, Ph.D
Satoshi Ueyama
Ritsu Sakuramachi
Mobmap: Introduction to
People Flow Analysis
2. Purpose and Summary
This course covers how to visualise and analysis time-
series data such as trajectory data using Mobmap for
beginners. In this course we will use simulated people
flow data developed by The University of Tokyo, Center
for Spatial Information Science(CSIS).
3. About microbase inc.
microbase Inc. is the company which creates micro demographic data in Japan. This
company has created simulated urban data such as people flow or people life style using
open data. The member of microbase Inc. aim to create micro demographic data all over
the world and simulation platform such as “Sim City” using these data.
Real-estate values Building Age
Future PopulationMicro CensusPersonal LifeStyle
10. What s mobmap?
• Tool for visualising and analyzing time-series data (ex.GPS log)
• Show animation for time-series change on Google Maps
• Windows, Mac, Linux supports
1500,1,1,1998/10/01 06:00:00,139.9249985549,35.7318406842,2,7,4110309,14,97,33,,97
3700,1,1,1998/10/01 06:00:00,139.9123053021,35.753511987,1,10,4112107,10,97,33,,97
7300,1,1,1998/10/01 06:00:00,139.9132597066,35.7134959947,1,7,4114009,8 ,97,40,,97
5500,1,1,1998/10/01 06:00:00,139.9374260851,35.7387718937,2,12,4113004,14,97,32,,97
9500,1,1,1998/10/01 06:00:00,139.9268670539,35.6868715236,1,2,4115011,12,97,26,,97
9700,1,1,1998/10/01 06:00:00,139.9238668934,35.6892555155,2,6,4115016,14,97,32,,97
11400,1,1,1998/10/01 06:00:00,139.9293917865,35.6808909812,1,6,4115107,9 ,97,36,,97
11800,1,1,1998/10/01 06:00:00,139.9077829215,35.6792209637,2,6,4115202,14,97,21,,97
10100,1,1,1998/10/01 06:00:00,139.9298447577,35.684551261,1,1,4115014,12,97,26,,97
11. What s mobmap?
Input Output
time-series data(CSV)
Route data(KML)
Movie(MP4)
Mesh data(CSV)
Polygon data(KML)
16. Data used this hands-on
①Simulated People Flow data(as time-series data)
2013-07-01.csv 2013-07-07.csv
2013-10-07.csv 2013-10-13.csv
2013-12-16.csv 2013-12-22.csv
2013-07-22.csv 2013-07-28.csv
2013-09-16.csv 2013-09-22.csv
2013-12-24.csv 2013-12-29.csv
2013-08-08.csv 2013-08-11.csv
2013-09-16.csv 2013-09-22.csv
2013-12-24.csv 2013-12-29.csv
Metropolitan
Chukyo
Kansai
17. Data used this hands-on
②Commercial accumulation statistics(Polygon data)
ca_2011_13.kml
ca_2011_23.kml
ca_2011_27.kml
Tokyo
Nagoya
Osaka
③Stay population data(pstay)
pstay_sample.csv
19. Practice data① Simulated People Flow data
Simulated People Flow data is made from geo-
tagged Tweet data(presented by Nightlei Co.,
Ltd.)
I'm at Ramen Jiro Meguro shop (Meguro-ku)
139.70714271068635.6341373645078
ex)
20. Practice data① Simulated People Flow data
This data is created as following estimation and interpolation methods
from geo-tagged Tweet data.
・Home estimate
・Stay time
estimation
・Path
interpolation
Home place is defined as a city and district
which users have frequently checked in on
morning and a holiday. Finally, the place is
determined at random in the city.
Virtual stay time is set in advance per category
of the check-in (movie, amusement, etc)
Paths are interpolated based on the places
between check-in places using road data
(cooperation: Hiroshi kanasugi, People Flow
Team at Tokyo University CSIS)
21. Raw geo tagged tweet data on a map (without the interpolations)
Step1
22. Home place and stay time are given to geo tagged tweet data according
to the check-in on map (night-time).
Step2
Virtual stay time per about 250
check-in place category
23. Paths are interpolated (only in the road) for creating Simulated People Flow data per 5 min
using INFORMATION PLATFORM FOR PEOPLE FLOW ANALYSIS by the university of Tokyo CSIS.
Step3
"STUDY OF INFORMATION PLATFORM FOR PEOPLE FLOW ANALYSIS IN URBAN AREA", the
36th Japan Society of Civil Engineering information use technology symposium, pp.111-114,
2011 about Yoshihide Sekimoto, your Satoshi Usui Hiroshi kanasugi, Yusuke Masuda,
24. Practice data① Simulated People Flow data
id sex date lat lon category1 category2 mode
categor
y
105 male
2013-07-01
22:10:39 35.71899231 139.31707368 MOVE
105 male
2013-07-01
22:15:39 35.71513008 139.31903984 MOVE
105 male
2013-07-01
22:20:39 35.71300252 139.31492206home arrival MOVE 8
105 male
2013-07-01
22:25:39 35.71483377 139.31029481
arts_enter
tainment Art Gallery MOVE 4
105 male
2013-07-01
22:30:39 35.71591093 139.30722089home arrival STAY 8
1071 male
2013-07-01
00:00:00 35.72355807 139.73582609home departure STAY 8
Following four attributes are necessary for using Mobmap.
"id" (user ID), "date" (time information), "lat", "lon"
25. Practice data① Simulated People Flow
Path interpolation of the practice data is given only data of
"MOVE" (during movement), and it is interpolated for every 5
minutes. The railroad network is not reflected by course
interpolation.
Other user
Time
yyyy-mm-dd HH:MM:SS
Category of the stay spot Detailed category of the stay spot
※The information such as twitter id deleted it from the viewpoint of privacy protection
id sex date lat lon category1 category2 mode
categor
y
105 male
2013-07-01
22:10:39 35.71899231 139.31707368 MOVE
105 male
2013-07-01
22:15:39 35.71513008 139.31903984 MOVE
105 male
2013-07-01
22:20:39 35.71300252 139.31492206home arrival MOVE 8
105 male
2013-07-01
22:25:39 35.71483377 139.31029481
arts_enter
tainment Art Gallery MOVE 4
105 male
2013-07-01
22:30:39 35.71591093 139.30722089home arrival STAY 8
1071 male
2013-07-01
00:00:00 35.72355807 139.73582609home departure STAY 8
26. For people to want to play with more Simulated People
Flow data
http://www.cs.uic.edu/ wolfson/html/p2p.html
http://research.microsoft.com/apps/pubs/?id=152883
University of Illinois Chicago school (around Illinois)
Microsoft Research(around Beijing)
27. Practice ② Commercial accumulation data(Polygon data)
Estimated commercial area such as downtown from yellow page which Zenrin
Co., Ltd. offers by Yuki Akiyama, a researcher at the university of Tokyo CSIS.
28. Practice ② Commercial accumulation data(Polygon data)
Researchers can use it
under collaborative
research with the university
of Tokyo from (JORAS)
Unit:
Prefecture unit
(all over Japan)
Time:
2010
2011
29. Practice data③ Transient population data
Transient population data of
the stores around Yoyogi-
Uehara Station (Japan) is
created using crowdsourcing
applications by the PStay
project , a crowd souring
project at micro geo data
workshop.
The PStay project collects the
transient population of a place
and quantity of traffic, the
parking number by
crowdsourcing.
http://geodata.csis.u-tokyo.ac.jp/mgd/?page_id=926
33. Read data 1
•Choose "Moving Objects" among a button forming a
line in the welcome page and open the CSV file
34. Read data 2
• Before loading data, mobmap shows a preview of data
• When the data include lonlat located in Japan, the lonlat columns are
automatically selected.
• You click and change a column as necessary column.
You can change a column when clicking
35. Read data 3
•Click "Start loading" of the lower part. Without any
errors, Mobmap starts reading all data.
Start reading in
38. Practice①
Date changes
There are "Play", "Stop", "forwarding" button like a movies player.
Each object begins to move when the Play button is clicked.
39. Layer list
•Add the layer that was formed by Read data to a list of
layers of the left pane
•The movable thing can replace order
Additional layer
40. Layer setting
•You can select detailed setting including the indication
method of the layer in a list of layers.
Change order of layers Display layer Delete layer
41. Read Polygon KML
• Add the layer from the drop-down menu
• The polygon supports only KML and WGS84
sample data
commercialDistricts.kml
43. Read data 4 (application)
• When you want to load an another attribute excepting
basic attributes, input "a field name : data type" in the
additional line.
Enter category:int
44. Read data 5 (application)
• To change Marker option, choose "By attribute" and
change a field name in Vary by attribute .
50. Path Visualisation 4
visualise people flow at a specified time
2.Drag to choose time span
1.Click
Tips: when you choose time span, press-and-hold Shift
and drag, and you can get regular time.
Time span selection
54. Practice②
Read Transient population data
(pstay_sample.csv) and Display it
separating by color. A line called
"est_pop" shows population per minute.
55. Reading time series data
•Were you able to display it when you changed time
so that the color of the marker changed?
56. Symbol size emphasis
A marker can change its color and size
depending on the attribute per minute
Change Markers presets to
"Large Scaling Marker"
59. Attribute query
Enter field name = level
category=4
Enter
1:retail store (various)
2:traffic
3:restaurant
4:entertainment, leisure
5:retail store (food)
6:Education
7:Other
8:Home
Category
62. High property search
¦¦(OR)、&&(AND)
In the case of plural conditions
category=4 || category = 8
OR sentense(or)
AND sentense(and)
category<4 && category > 1
64. About spatial query
Deselecting
select of the polygon
select of the rectangle
select of the line gate
Choose a movement object from the select button
of the upper part menu
70. Gate function
• Choose a person, a thing via a certain spot
• Line gate (appoint it in a segment of a line)
• Polygon gate (appoint it in a domain)
72. Line gate application 1
Apply to the expressway along Haneda Airport
①click a line gate button
②it can pull a line when drag
it over a map.
73. Line gate application 2
The details are coordinated by a line choice option
After pull a line,
a menu is displayed
by the line upper part
OK button Direction choice (up, down, both)
Bookmark of line
Cancel
78. Polygon gate application 1
Choose the polygon data of the commerce
accumulation data that we ve read it before.
79. Polygon gate application 2
The attribute of the chosen polygon is shown
choose a polygon layer in a combo box
80. Polygon gate application 3
A detail menu of polygon can be shown when a line of
the polygon ID is clicked
Indication of the choice polygon (in a map)
Single choice
Deselecting
Polygon gate function
After having developed the line of the
table, click a button
81. Polygon gate application 4
Choose only the movement object which passes a
polygon by choosing a button "point + edge" or points
only"
93. Coopration with other software
• Be careful the attribute because only the first record is reflected
• If it is sex not to change in time series, there is no problem
Read QGIS
96. Cooperation with QGIS
Because Mobmap is specialized in the visualising and
analyzing moving trace data, the operation of the
general GIS is carried out on QGIS
Example:
•Coordinate transformation (cases of the rectangular
coordinates system plane a file)
•File conversion to KML form, CSV form
•Space analysis, operation such as the buffering
98. Animation Export function① Adjust screen
adjust a screen for the animation export.
Please put time bars together
at the time when you want to
start an animation.
If animation export
preparations are possible,
and then click this button.
100. Animation export function③ input output information
Detailed setting of the animation to output
Output size
set it from here to
raise flame
By the default
setting, output a
share for ten
minutes in
animation
reproduction one
second.
eg: In the case of
15sec, it is
150min
107. Visualising Mesh data
2.Choose Mesh CSV and open the CSV file
NationalCensus__3JTokyo-2010.csv or
NationalCensus_4JTokyo-2010.csv .
1.Click
•Ex. National Population Census in Tokyo
113. Visualising night-time population
The ratio of each local night-time population are shown. The data
in this hands-on doesn’ have the magnification factor and
completeness of parameter so the ratio is 0% largely.
121. Practice
Using Simulated People Flow data, decide the target area and
find the characteristic trend of the place and consider the
reason. Finally, have an effective presentation using mobmap
movie function.
122. Presentation
Please upload a movie which you tried in practice as an
animation in YouTube. After creating the movie, tell us the
movie URL.
123. Summary
Using Mobmap, We learned the method to
visualise and analyze GIS data with the
time-series data.
This exercise provide for simulated people
flow data as sample data. Also, you can
handle your own data as well.
125. Thanks
For creating data and this exercise, Hiroshi
Kanasugi helped us to interpolate and create
the Simulated People Flow data.
The Simulated data is made from the geo
tagged tweet data by Ishikawa, Nightlei Co.,
Ltd..
We appreciate them.
127. Reading mesh CSV
• Only CSV is the correspondence in the current version.
• It is not for analysis but for drawing.
Sample data
Census-MeshTest2005_3.csv
@static-mesh
@use-mesh-code 3
36533748 0
49395673 0
51394139 0
53393642 0
53393653 0
*process a format as follows to display it in mobmap.
The first line
describe it in the
first row with
"@static-mesh"
The second line
describe "@ use-
mesh-code" in the first
row
describe a scale of
the mesh in the
second row
ex) In the case of the
third mesh
-> 3
After the third line
Value
(population))
Mesh code
128. Reading in typhoon data
• It reads in the behavior of the typhoon from the website
Source: degital typhoon data
129. Reading in typhoon data
• Enter the URL of the typhoon page of the digital typhoon
http://agora.ex.nii.ac.jp/digital-typhoon/summary/wnp/s/201115.html.ja
130. Reading in typhoon data
• Display the movement trace of the typhoon with an
animation