1. The document discusses the use of micro geodata (MGD) for smart city applications in Japan. It provides examples of available MGD in Japan including building data, population census data, and mobile phone data.
2. Case studies presented utilize MGD for monitoring population distribution through micro population census, estimating disaster damage, and understanding traffic patterns by analyzing mobile phone GPS data.
3. The author argues that widespread use of digital maps and availability of detailed spatial data is increasing demand for more granular data. MGD is gaining attention for how it can be developed, shared and applied to realize smart cities.
Urban Landscape Elements slides for Sustainable Urban Landscape Design course.
Master Sustainable Urban Design, Razak Faculty, Universiti Teknologi Malaysia.
Urban Landscape Elements slides for Sustainable Urban Landscape Design course.
Master Sustainable Urban Design, Razak Faculty, Universiti Teknologi Malaysia.
It is an assignment on urban design basic factors, whereas a designer should keep in mind in urban designing.
Here I tried to describe factors by pointing as anyone could find a basic concept on urban design. Hope it'll be helpful.
To plan a city/region, we require base data on which information extrapolation & decisions may happen. Hence, Identify ‘data needed’, and Identify ‘needs of data’ collection
Inspection survey:
A) Direct :
Observe traffic count/ situation
Observe housing quality
Observe economic activity
Observe social parameters, etc.
B) Indirect:
Clubbing of directly observed ‘indicators’ to generate area’s possible ‘proxy’.
For e.g. housing condition + plot sizes + no. & types of vehicles + consumer goods = income range
. Personal interview/ Dialogue:
A questionnaire is designed beforehand at appropriate scale:
Nominal Scale : Yes or No
Ordinal Scale : Possible options or multiple choice questions
Interval Scale : Range/ intervals like age group or income group
Structured questions are precise and one-way
Semi-structure survey is a two-way information flow. It’s an informal dialogue in which the surveyor might receive new information from respondent/s. however, it depends on;
Behavioural factors of surveyor and respondents
Questions not to be ambiguous or long
Managing conversation and seeking pin-point answers
Judging responses without bias
Recording interview
Avoiding errors
Cross-checking with other respondents
Major land uses to be identified for analysing physical distribution and existing conditions:
Developed
Under-developed
Un-developed
Major uses marked on map are as per the defined regional/city level plans, like;
Urbanizable zone
Industrial zone
Transportation & Communication zone
roads, railways, MRTS, Seaports, Dockyards, Airports, Bus depots/ terminals, freight complexes, transmission and communication
Primary activity zone
Agriculture, poultry, rural settlements, brick kilns, extraction areas
Open area zone
Recreation zone, green buffer zone
Protected/ Eco-sensitive zone
Water bodies, forests, sanctuaries, coastal zone, wetlands, marshy zone
special area zone
Heritage & conservation zone, scenic value, tourism zone, defence area/ zone, border conflict zone
Data regarding demographic characteristics;
Population growth (natural, induced)
Population size (age-wise)
Population density
Population distribution
Gender ratio
Socio-Economic status
Religion
Marital status
Education ratio
School dropouts
Gender-wise enrolment in schools, colleges
Mortality rate (age-wise)
Birth rate
Health rate (in some surveys)
Sample types for doing household/ demographic surveys;
Simple Random sampling
Systematic sampling
Stratified sampling
Cluster sampling
Multistage sampling
There are nine steps involved in the development of a questionnaire:
Decide the information required.
Define the target respondents.
Choose the method(s) of reaching your target respondents.
Decide on question content.
Develop simple & clear wording of questions
Put the questions into a meaningful order and format.
Check the length of the questionnaire.
Pre-test the questionnaire
Develop the final survey form.
The C.B.D or Central Business District is the focal point of a city. It is the commercial, office, retail, and cultural center of the city and usually is the center point for transportation networks.
It is an assignment on urban design basic factors, whereas a designer should keep in mind in urban designing.
Here I tried to describe factors by pointing as anyone could find a basic concept on urban design. Hope it'll be helpful.
To plan a city/region, we require base data on which information extrapolation & decisions may happen. Hence, Identify ‘data needed’, and Identify ‘needs of data’ collection
Inspection survey:
A) Direct :
Observe traffic count/ situation
Observe housing quality
Observe economic activity
Observe social parameters, etc.
B) Indirect:
Clubbing of directly observed ‘indicators’ to generate area’s possible ‘proxy’.
For e.g. housing condition + plot sizes + no. & types of vehicles + consumer goods = income range
. Personal interview/ Dialogue:
A questionnaire is designed beforehand at appropriate scale:
Nominal Scale : Yes or No
Ordinal Scale : Possible options or multiple choice questions
Interval Scale : Range/ intervals like age group or income group
Structured questions are precise and one-way
Semi-structure survey is a two-way information flow. It’s an informal dialogue in which the surveyor might receive new information from respondent/s. however, it depends on;
Behavioural factors of surveyor and respondents
Questions not to be ambiguous or long
Managing conversation and seeking pin-point answers
Judging responses without bias
Recording interview
Avoiding errors
Cross-checking with other respondents
Major land uses to be identified for analysing physical distribution and existing conditions:
Developed
Under-developed
Un-developed
Major uses marked on map are as per the defined regional/city level plans, like;
Urbanizable zone
Industrial zone
Transportation & Communication zone
roads, railways, MRTS, Seaports, Dockyards, Airports, Bus depots/ terminals, freight complexes, transmission and communication
Primary activity zone
Agriculture, poultry, rural settlements, brick kilns, extraction areas
Open area zone
Recreation zone, green buffer zone
Protected/ Eco-sensitive zone
Water bodies, forests, sanctuaries, coastal zone, wetlands, marshy zone
special area zone
Heritage & conservation zone, scenic value, tourism zone, defence area/ zone, border conflict zone
Data regarding demographic characteristics;
Population growth (natural, induced)
Population size (age-wise)
Population density
Population distribution
Gender ratio
Socio-Economic status
Religion
Marital status
Education ratio
School dropouts
Gender-wise enrolment in schools, colleges
Mortality rate (age-wise)
Birth rate
Health rate (in some surveys)
Sample types for doing household/ demographic surveys;
Simple Random sampling
Systematic sampling
Stratified sampling
Cluster sampling
Multistage sampling
There are nine steps involved in the development of a questionnaire:
Decide the information required.
Define the target respondents.
Choose the method(s) of reaching your target respondents.
Decide on question content.
Develop simple & clear wording of questions
Put the questions into a meaningful order and format.
Check the length of the questionnaire.
Pre-test the questionnaire
Develop the final survey form.
The C.B.D or Central Business District is the focal point of a city. It is the commercial, office, retail, and cultural center of the city and usually is the center point for transportation networks.
Multi-Scale Urban Analysis Using Remote Sensing and GISCSCJournals
India experienced a high rate of urbanization during the last five decades leading to concentration of population in the main cities. One of the main city is Hyderabad in India is a sprawling metropolis and an incipient megacity facing structural, environmental, social and economic problems. The objective of this study is to investigate the current pattern of land use to monitor the trends of urban growth in Hyderabad between 1997, 2007 and 2013 using satellite images and GIS. Second object is to enable a highly detailed structural characteristics of specific neighborhoods’, thus a multi-scale analysis of the urban area by remote sensing provides up-todate data of the urban morphology. This enables a value-added and more holistic view to understand urban workflows and their dependencies.
Understanding and predicting urban dynamics through new forms of geo-social d...Achilleas Psyllidis
The recent emergence of new forms of geo-social data, deriving from social media, sensors, and mobile phones, calls for an update to the methodological toolbox of social sciences. The new methods and tools need to harmonise with the inherent characteristics and challenges of the emerging data sources. This talk demonstrates how SocialGlass, a web-based system for (real-time) urban analytics, helps improve the understanding of human dynamics in modern-day cities, by capitalising on new geo-social data and pioneering data science techniques. Emphasis is on real-world applications, regarding social area analysis, crowd dynamics during large-scale events, and location prediction of new urban functions across different cities.
Presentation at the Centre for BOLD (Big, Open & Linked Data) Cities anniversary meet-up | Erasmus University Rotterdam -- May 29, 2017
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
국립재난안전연구원에서 특강한 자료입니다. 최근의 공간정보 분야 동향과 시사점에 대해 개인적인 생각을 정리해 봤습니다. 데이터 갱신주기의 단축, 실시간 공간정보 활용의 증가, 실내외
공간정보의 통합, 지하시설물에 대한 관심 증대, 새로운 방식의 분석과 시뮬레이션 기법의 등장, 그리고 이들이 어우러져 만들어내는 디지털트윈을 열쇠말로 삼아봤습니다. 마지막 부분에는 이런 동향에 대한 대응으로 가이아쓰리디에서 만들고 있는 mago3D(마고쓰리디)와 라이브드론맵에 대해 소개했습니다.
Big Data for New Industrialization and Urbanization Development: A Case Study...IJERA Editor
Industrialization and urbanization are considered as interdependent processes of recent economic development.
Innovations in technology and higher affordability of electronic devices have facilitated current age of big data.
Use of digital data provided modern urbanization which is an essential element of industrialization and rapid
income growth globally. Most manufacturing and service production is efficient when undertaken in urbanized
areas where organizations can readily follow best practice in technology and management. Over the past three
decades, China has achieved enormous economic growth, accompanied by a growing number of large cities.
The purpose of this paper is to identify prominent issues relating influence of big data on modern
industrialization and urbanization development in China as well as in other regions. The case study of China
was taken to understand the advancement of big data on industrialization and urbanization enhancement. It was
investigated that industrialization and the rise of the service sector appear to have influenced the growth of
urbanization, but their role was relatively small when compared to the direct effects of economic growth. In the
coming years, urbanization will become increasingly an opportunity as well as a challenge to the country‟s
effort to sustain rapid growth and maintain effective development
Spatiotemporal Land Use Patterns in UrbanizationWaqas Tariq
Urban planning was very much a design and engineering exercise with the state as a single stake holder. Mega cities with millions of population, which has undergone a series of physical as well as socio-economic changes over the last 60 years. These for in different areas of India, new planning approaches require the need to understand the complicated urban land development process. GIS- Geographic Information System and remote sensing provide the advance techniques and methods for studying urban land development and assist urban planning. This research survey firstly describes the urban expansion process till now and land use changes in the inner city.
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
Development of a Geographic Information Systems Road Network Database for Eme...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Planning Liveable Cities With Big Social DataMatt Low
Big social data – data collected from online social networks such as Twitter, Facebook, Foursquare and Yelp – can provide new insights into the dynamics of cities. Billions of data points can be harvested to understand how people move around the city and how they experience the urban environment. Deeper, real-time urban insights provide the evidence base for planning more liveable cities – building more responsive transport systems, developing unique neighbourhood identities, and designing more attractive places.
These new data sets are especially useful for addressing gaps within the urban planner’s
toolbox. Firstly, while the pace of change in cities accelerates, conventional data sets (such as Census data or surveys) are updated infrequently. Secondly, there is limited data about the invisible dimensions of cities – sentiment, movement, and social networks.
Session by Andrew Wyckoff, Director, Science, Technology and Innovation, OECD
Digitalisation has been underway for 50 years but crossed a critical threshold in last few years when over 80% of citizens in OECD countries had broadband subscriptions with the majority accessing the Internet via a smartphone. This era of ubiquitous computing is transformational, and the widespread deployment of this infrastructure means that products, activities and interactions are increasingly "digital" and can be easily shared, stored or exchanged globally via the Internet. As a consequence, data flows have grown and are a new raw material for innovation in industry and society, unleashing new business models and modes of social interaction. This transformation is just beginning and is poised to grow significantly as networked sensors and things become common-place. These changes are disruptive and also at odds with public policies – many of which are legacies of a pre-digital, analogue era. Reducing this gap and equipping policy-makers with ways to proactively seize the potential benefits and address the challenges related to digitalisation is at the core of a new cross-sectoral, multi-year project within the OECD.
These technological trends are not limited to one policy area, but their effects are particularly evident in the labour market, where they are profoundly affecting the nature of work, the structure and nature of the work environment, and the very nature of being an employee. We can’t predict exactly what the world of work will look like in the future or the specific types of jobs that will exist. What is clear, however, is that most sectors are already being affected. The platform (e.g. ‘sharing’, ‘gig’) economy offers workers great opportunities, including the flexibility of freelancing and holding multiple jobs (or gigs) to top up their income. At the same time, these new forms of work are challenging traditional institutions based on a unique employer-employee relationship. For instance, as new ways of organising work shift risk towards individual workers, who are increasingly in charge of their own training and of securing old-age and health insurance, existing models of social protection will need to be overhauled. How policy-makers, companies, employees and educators will adapt to these changes will mark the difference between being successful and being left behind.
Similar to Smart City and Spatial Big Data -Studies and cases in Japan- (20)
Earthquake Damage Risk Evaluation by Micro Geo Data マイクロジオデータベースによる 地震災害リス...Yuki Akiyama
Date: March 14, 2015(Sat) 9:30-
Venue: TKP Garden city Sendai Kotodai, Hall 2
"The 3 rd World Conference on Disaster Risk Reduction in Sendai ~Redesigning for resilient national land using big data of environment and disasters~" 講演資料
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Influence of Marketing Strategy and Market Competition on Business Plan
Smart City and Spatial Big Data -Studies and cases in Japan-
1. Yuki Akiyama(aki@csis.u-tokyo.ac.jp)
Assistant professor
Micro Geodata lab
Center for Spatial Information Science, University of Tokyo
Smart City and Spatial Big Data
-Studies and cases in Japan-
August 31, 2016
FOSS4G Korea
Smart City with Open Geospatial Technologies
in Emerging Cities
2. Self introduction
Name:Yuki Akiyama (秋山祐樹)
Birth place:Okayama city, Okayama pref. Japan
Affiliation:Assistant Professor
Micro Geodata laboratory
Center for Spatial Information Science, UT
Visiting Research Officer
Policy Research Institute for Land, Infrastructure,
Transport and Tourism (PRLIT)
Visiting Research Fellow
Korea Research Institute for Human Settlements
Main fields: Spatial information Science, Urban engineering
Geography
2
Urban heat island
GIS
Micro Geodata
Smart city
Tokyo
Seoul
Okayama
3. Background
Many problems in urban area
Reactivation of
city center
Aging
Traffic
management
Disaster
prevention
Facility placement
planning
Design urban planning to realize smart city
Many kinds of spatial data and census
data can support to solve them.
3
9. Many conferences related with MGD!!
http://sigspatial2016.sigspatial.org/
http://giscience.geog.mcgill.ca/https://cupum2015.mit.edu/
http://www.isprs2016-prague.com/
9
Me
10. Japanese government is looking to the
utilization of MGD.
Japanese government begins to introduce legislation
toward promotion of utilization of big data.
NHK News Web(2014/06/09)
http://www3.nhk.or.jp/news/html/20140609/k10015066701000.html
Regional Economy Society Analyzing System (RESAS)
https://resas.go.jp/
10
Japanese government is developing
legislation to use big data enciphered
personal information without
owner’s consent.
Japanese Cabinet Office is
developing the “RESAS” which
visualize various census data and
big data for Japanese local
governments.
11. The widespread use of detailed digital map creates demands
for more detailed and reliable spatial data than before.
Not only researches but also private and government
sectors are also interested in how to utilize these data.
Micro Geodata (MGD)
is gathering attention now
how to develop, share and
apply for realization of smart city.
Coming of age to utilize MGD
Recently, computers and smart phones are becoming
widespread and internet environment are being
developed rapidly.
Anyone can access detailed digital map.
11
12. 1) Available Japanese MGD
for urban monitoring
2) Examples of studies & cases to utilize MGD
2.1 Utilize person MGD
2.2 Utilize disaster big data
2.3 Utilize public big data
3) Conclusions and future works
Today’s contents 12
13. Commercial accumulation
Statistics
Eric Fischer, “Eric Fischer’s photostream”,
http://www.flickr.com/photos/walkingsf/
Continuation
Change
Emergence
Demise
LegendTime-series changes 2003-2008
Time-series tenant data
Inter-enterprise
transaction big data
Residential map
Telephone directory
Web information (SNS and search results)
MGD about Buildings, shops and enterprises
1. Available Japanese MGD for urban monitoring
15. 1) Available Japanese MGD
for urban monitoring
2) Examples of studies & cases to utilize MGD
2.1 Utilize person MGD
2.2 Develop disaster big data
2.3 Utilize public big data
3) Conclusions and future works
Today’s contents 15
16. Recently, various census data are being digitalized and we can get
them easily from websites of national and local governments.
Especially, the population census is used as base data to understand
distribution and movement of population for following urban problems.
2.1 Utilize person MGD
Urban
planning
Disaster damage
estimation and
prevention
Traffic
planning
Marketing
support
Epidemic
prevention
More detailed and reliable population data are required than before
to resolve them.
How to monitor
detailed population distribution ?
16
17. 2.1 Utilize person MGD
How to monitor
detailed population distribution ?
1. Residential population
> Micro population census (MPC)
2. Dynamic population
> Mobile phone data (CDR, GPS log etc.)
17
18. 18
Micro population census (MPC)
(Population census + residential map + housing statistics)
MPC can monitor detailed residential population.
It was realized to disaggregate population census. (Disaggregate: data processing to
reallocate data statistically based on other statistics and spatial features.)
東松原駅
LEGEND
Households
1 person
3 persons
4 persons
5 persons
≧6 persons
2 persons
Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of
Micropopulation Census through Disaggregation of National
Population Census", CUPUM2013 conference papers, 110.
18
19. 19
東松原駅
Attribute table
Building type house
Longitute 139.65633
Latitude 35.663664
Area[m2
] 105.34
Family type 8
Household size 5
Householder [age-gender] 45 - 1
Spouse [age - gender] 40 - 2
Number of child 2
Information of children 5-1 | 10 - 2
Number of parent 1
Information of parent 75-2
Number of others 0
Information of others None
LEGEND
Households
1 person
3 persons
4 persons
5 persons
≧6 persons
2 persons
Micro population census
(Population census + residential map + housing statistics)
Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of
Micropopulation Census through Disaggregation of National
Population Census", CUPUM2013 conference papers, 110.
MPC can monitor detailed residential population.
It was realized to disaggregate population census. (Disaggregate: data processing to
reallocate data statistically based on other statistics and spatial features.)
19
20. 20
LEGEND
Households
1 person
3 persons
4 persons
5 persons
≧6 persons
2 persons
Micro population census
(Population census + residential map + housing statistics)
Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of
Micropopulation Census through Disaggregation of National
Population Census", CUPUM2013 conference papers, 110.
20
21. 21
LEGEND
Households
1 person
3 persons
4 persons
5 persons
≧6 persons
2 persons
Micro population census
(Population census + residential map + housing statistics)
Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of
Micropopulation Census through Disaggregation of National
Population Census", CUPUM2013 conference papers, 110.
21
22. LEGEND
Households
1 person
3 persons
4 persons
5 persons
≧6 persons
2 persons
Micro population census
(Population census + residential map + housing statistics)
Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of
Micropopulation Census through Disaggregation of National
Population Census", CUPUM2013 conference papers, 110.
22
23. LEGEND
Households
1 person
3 persons
4 persons
5 persons
≧6 persons
2 persons
The Important thing to understand this data
This data is estimated data.
Values of each point are necessarily match as actual states.
Micro population census
(Population census + residential map + housing statistics)
Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of
Micropopulation Census through Disaggregation of National
Population Census", CUPUM2013 conference papers, 110.
23
24. Aggregation by grid (250m square meter)
Micro population census
(Population census + residential map + housing statistics)
24
25. Aggregation by grid (250m square meter)
LEGEND
Population
0 - 250
501 - 1000
1001 - 2000
2001 -
251 - 500
Aim of the Micropopulation census
=Development the new population census which can be aggregated
into arbitrary spatial unit.
Micro population census
(Population census + residential map + housing statistics)
25
26. Aggregation by city blocks
LEGEND
Population
0 - 50
101 - 250
251 - 500
501 -
51 - 100
Micro population census
(Population census + residential map + housing statistics)
26
27. LEGEND
Aging rate
(Over 65 yrs. old)[%]
0 - 10
20 - 30
30 - 50
50 -
10 - 20
27
Monitoring of aging rate
(rate of population over 65years old in 250m square grid)
27
28. 凡例
高齢化率
(65歳以上割合)[%]
0 - 10
20 - 30
30 - 50
50 -
10 - 20
28
Monitoring of aging rate
(rate of population over 65years old in 250m square grid)
LEGEND
Aging rate
(Over 65 yrs. old)[%]
0 - 10
20 - 30
30 - 50
50 -
10 - 20
28
29. 29
Monitoring of aging rate
(rate of population over 65years old in 250m square grid)
29
30. 4.3 billion people use mobile phones in 2014 *1.
Many mobile phones have GPS function.
Studies to monitor dynamic population using mass mobile
phone GPS data attract considerable attention for resolve
various urban problems *2.
*1: DESIGNBAM, “5 billion people will use mobile phones by 2017”,
http://designbam.com/2013/10/04/5-billion-people-will-use-mobile-phones-by-2017/
*2: Sekimoto, Y., Horanont T. and Shibasaki, R., 2011. Trend of People Flow Analysis
Technology Using Mobile Phone. IPSJ Magazine,. 52(12): 1522-1530. (In Japanese)
31. 4
Application example of mobile phone GPS data
Example in USA
This map shows characteristics of
each road based on dynamic
population calculated from mass
mobile phone GPS data in San
Francisco. This result is actually
used for traffic planning in the city.
PHYS.ORG, “Cellphone, GPS data suggest new
strategy for alleviating traffic tie-ups”,
http://phys.org/news/2012-12-cellphone-gps-
strategy-alleviating-traffic.html
bc: How popular they are as connectors
between other roads
Kroad : Number of geographic areas that
contribute to traffic on a particular road
31
32. 5
Example in Kenya
Three quarters of Kenyan
use mobile phones.
Mobile phone GPS data
are used for animal
quarantine.
When livestock disease
occurs, Kenyan farmer
deliver and share
information of them with
location information.
http://www.un.org/apps/news/sto
ry.asp?NewsID=44259&Cr=livestoc
k&Cr1=#.VFLsW_l_u51
Application example of mobile phone GPS data 32
33. Japanese mobile phone GPS data
We can monitor estimate populations of every
hours by 250m square grids (updated everyday).
・Real time data based on GPS logs by mobile phones
・It covers throughout Japan.
Congestion analysis (Zenrin Data Com Co., Ltd.)
http://lab.its-mo.com/densitymap/
33
34. Estimated numbers of visitor in each commercial area
Shinjuku
Shibuya
Legend
Estimated number
of visitors
Application 1: Estimation of Dynamic population
in commercial areas
34
秋山祐樹 ・Teerayut Horanont・柴崎亮介,2013年,「大規模
人流データを用いた商業地域における来訪者数の時系列分析」,
第22回地理情報システム学会講演論文集(CD-ROM, C-5-4)
35. Time-series numbers of visitor in each commercial area
Shinjuku
Shibuya
Legend
Estimated number
of visitors
Application 1: Estimation of Dynamic population
in commercial areas
Number of Shops:198
Average number of daily visitors:3,039
Average Hourly number of visitors in weekdays
0
500
1,000
1,500
2,000
2,500
3,000
3,500
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
35
秋山祐樹 ・Teerayut Horanont・柴崎亮介,2013年,「大規模
人流データを用いた商業地域における来訪者数の時系列分析」,
第22回地理情報システム学会講演論文集(CD-ROM, C-5-4)
36. Time-series numbers of visitor in each commercial area
Shinjuku
Legend
Estimated number
of visitors
Application 1: Estimation of Dynamic population
in commercial areas
Number of Shops:198
Average number of daily visitors:3,039
Average Hourly number of visitors in weekdays
0
500
1,000
1,500
2,000
2,500
3,000
3,500
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
36
秋山祐樹 ・Teerayut Horanont・柴崎亮介,2013年,「大規模
人流データを用いた商業地域における来訪者数の時系列分析」,
第22回地理情報システム学会講演論文集(CD-ROM, C-5-4)
Shibuya渋谷
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Number of Shops:1,580
Average number of daily visitors:92,619
Average Hourly number of visitors in weekdays
37. 37
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
22,000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Averagenumberofvisitorsinweekday
O’clock
浅草 歌舞伎町 原宿 八重洲1丁目 下北沢南口
Asakusa Kabuki-cho Harajuku Yaesu-1 Shimo-kitazawa
It is realized to integrate the Commercial Accumulation Statistics with the mobile
census data by ZDC in the city center of Tokyo. (365days in 2012)
Application 1: Estimation of Dynamic population
in commercial areas
!
37
38. 25
Regional characteristics in each grids were calculated as follows.
Numbers of
each kind of
stay points can
be calculated in
all grids.
Regional
characteristics
are defined by
this method.
Akiyama, Y. and Shibasaki, R., 2014, "Time-series Monitoring of Area Characteristics Using Mass Person Flow Data by Mobile Phone GPS Data -
Case study in Greater Tokyo Region-",The International Symposium on City Planning 2014,SS03,S03-12.
Application 2: Estimation of regional characteristics 38
39. Time-series estimation of regional characteristics in Tokyo using
mobile census data in 2012
39
Akiyama, Y. and Shibasaki, R., 2014, “Time-series Monitoring of Area Characteristics Using Mass Person Flow Data
by Mobile Phone GPS Data“, The International Symposium on City Planning – Vietnam 2014, SS03,S03-12.
Application 2: Estimation of regional characteristics 39
40. Akiyama, Y. and Shibasaki, R., 2014, “Time-series Monitoring of Area Characteristics Using Mass Person Flow Data
by Mobile Phone GPS Data“, The International Symposium on City Planning – Vietnam 2014, SS03,S03-12.
Number of grids throughout Kanto region by 500m square grid
Population of each types throughout Kanto region by 500m square grid
0
10,000
20,000
30,000
40,000
50,000
0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00
Numberofgrid
Time
Commercial/Sightseeing Residential Business Transport Mixed
0
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00
Population
Time
Commercial/Sightseeing Residential Business Transport Mixed
Application 2: Estimation of regional characteristics 40
41. Application 3: Event detection
Akiyama, Y., Ueyama, S., Shibasaki, R. and Adachi, R., 2016, “Event Detection Using Mobile Phone Mass GPS Data and Their Reliability Verification
by MDSP/OLS Night light Image”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-2, 77-84.
We defined sudden population
concentration spatio-temporally as the
“Event”.
Events contains positive events
(festivals, concerts, ceremonies etc.) and
negatives (traffic accidents, disasters
etc.).
41
42. 15
4. Result
Akiyama, Y., Ueyama, S., Shibasaki, R. and Adachi, R., 2016, “Event Detection Using Mobile Phone Mass GPS Data and Their Reliability Verification
by DMSP/OLS Night light Image”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-2, 77-84.
42
43. 15
4. Result
A: Open event on the Iwakuni
base of US air force(May 5)
C: Omagari firework display
(August 26)
Akiyama, Y., Ueyama, S., Shibasaki, R. and Adachi, R., 2016, “Event Detection Using Mobile Phone Mass GPS Data and Their Reliability Verification
by DMSP/OLS Night light Image”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-2, 77-84.
43
44. 4. Result
Akiyama, Y., Ueyama, S., Shibasaki, R. and Adachi, R., 2016, “Event Detection Using Mobile Phone Mass GPS Data and Their Reliability Verification
by MDSP/OLS Night light Image”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-2, 77-84.
15
D: New Year events in Saijo
Inari shrine
holidays)
Many events were held in
New Year holidays
intensively
F: Typhoon effect (September 30 ~ October 1)
Event Meshes with a small number of Event
visitors. Many tourists were stuck due to the
effect of a severe typhoon.
44
45. 1) Available Japanese MGD
for urban monitoring
2) Examples of studies & cases to utilize MGD
2.1 Utilize person MGD
2.2 Develop disaster big data
2.3 Utilize public big data
3) Conclusions and future works
Today’s contents 45
46. Japanese government estimates that victims by Tokai-
Tonankai earthquakes are over 300 thousands.
Calculation and estimation of micro-scale regional
disaster risks throughout Japan is very important for
planning of earthquake disaster prevention in Japan.
2.2 Develop disaster big data
Y, Akiyama., Y, Ogawa., H, Sengoku., R, Shibasaki. And T, Kato., “Development of Micro Geo Data for Evaluation
of Disaster Risk and Readiness by Large-scale Earthquakes Throughout Japan”, Proceeding of Annual
conference on Infrastructure and Management, 2013, 392. (in Japanese)
Y, Ogawa., Y, Akiyama. and R, Shibasaki, “The Development of Method to Evaluate the damage of Earthquake
Disaster Considering Community-based Emergency Response Throughout Japan”, GI4DM2013, 2013, TS03-1.
Using various MGD and census data…
Estimation data about
earthquake damage risk and
death rate of each building
= Disaster big data
46
47. Estimation of disaster risk and first responder power of all buildings
Building collapse risk
①Est. structure
②Est. building age
Existing census data Micro Geo Data (MGD) Earthquake motion
Human damage risk
⑤Resident(age, gender etc.)
first responder power
⑥Expected number of rescuee
⑦Distance from fire stations
Realization of environment for estimation of damage situation in an
arbitrary spatial unit scale-seamlessly.
→Development micro scale base data throughout Japan
Building fire risk
③Est. fire resistance performance
④Est. fire occurrence ratio
Housing and land census
Population census
etc.
Residential map
Telephone directory
Commercial accumulation
etc.
Seismic activity map
2.2 Develop disaster big data 47
48. Seismic
input
Estimation of damage and first responder power in each building
Bldg.
structure
Bldg.
age
fire occurrence
ratio
Fire spread
cluster
Residents
Rescue power by
fire authorities
Mutual power by
neighborhood
Calculation of
collapse risk
Calculation of
fire risk
Calculation of mutual power
Calculation of rescue power
Number of recsuee
from collapsed
buildings rescued
by neighborhoods.
Fire extinguished possibility
by fire authorities
2.2 Develop disaster big data 48
49. Building unit →Aggregation data
=Scale seamless damage estimation
Municipality District Grid City block
Collapse risk
Fire risk
Mutual power
Rescue power
Fire extinguished possibility
by fire authorities
-
-
Disaster
risks
First responder
powers
Damage
estimation
Estimated
number of
casualty
Number of recsuee
from collapsed
buildings rescued
by neighborhoods.
2.2 Develop disaster big data 49
50. Earthquake Disaster damage estimation by MGDEarthquake Disaster
damage estimation by MGD
(250m square grid)
Estimated death
rate [%]
In case of earthquake which will
occur 2% in 50years
51. Estimated death
rate [%]
In case of earthquake which will
occur 2% in 50years
Earthquake Disaster
damage estimation by MGD
(250m square grid)
52. Estimated death
rate [%]
In case of earthquake which will
occur 2% in 50years
Earthquake Disaster
damage estimation by MGD
(250m square grid)
53. Estimated death
rate [%]
In case of earthquake which will
occur 2% in 50years
Earthquake Disaster
damage estimation by MGD
(250m square grid)
54. Estimated death
rate [%]
In case of earthquake which will
occur 2% in 50years
Earthquake Disaster
damage estimation by MGD
(250m square grid)
55. Estimated death
rate [%]
In case of earthquake which will
occur 2% in 50years
Earthquake Disaster
damage estimation by MGD
(250m square grid)
56. Estimated death
rate [%]
In case of earthquake which will
occur 2% in 50years
Earthquake Disaster
damage estimation by MGD
(250m square grid)
58. This result received a lot of attention in Japan.
Medias (NHK)
Magazines and books
Get research funds
(Ministry of Land, Infrastructure, Transport and Tourism etc.)
2.2 Develop disaster big data
Utilized disaster drill for citizens
58
59. 1) Available Japanese MGD
for urban monitoring
2) Examples of studies & cases to utilize MGD
2.1 Utilize person MGD
2.2 Develop disaster big data
2.3 Utilize public big data
3) Conclusions and future works
Today’s contents 59
60. 2.3 Utilize public data 60
Local governments have many precious public data.
Now we resolve a problem of local government using
various public data.
The problem of local government is increasing Vacant
house.
Local government want to monitor locations of vacant houses.
61. 2.3 Utilize public data 61
Collect some public data without private information
(citizen’s names)
Vacant house?
1) Resident Register information
of each house
2) Consumption data of city water
of each house
3) Property tax data of each house
> Building age and structure
We developed estimating model of vacant house to integrate public
data with results of field surveys in some sample areas.
e.g. No residents, no water consumption and no taxation on the house A,
> The house is estimated a vacant house.
62. 2.3 Utilize public data 62
City block unit
Result: Estimate Number of vacant house (Kagoshima city)
This research project is ongoing in some cities.
Tokyo
Seoul
Kagoshima
This result was provided for housing
and city planning sector of
Kagoshima city.
Our result shows there are about 1,700
vacant houses out of about 32,000 houses
(5.36%) in city center of Kagoshima city.
63. 1) Available Japanese MGD
for urban monitoring
2) Examples of studies & cases to utilize MGD
2.1 Utilize person MGD
2.2 Develop disaster big data
2.3 Utilize public big data
3) Conclusions and future works
Today’s contents 63
64. Applicative studies which were difficult before are
being realized.
・Today is data “abundant” and “accumulating” era.
・Unrealizable studies (only ideas) can be realized by MGD.
Anyone can develop an environment to handle and
analyze MGD.
・We can acquire high spec PC and high-capacity HDD at low prices.
Our approaches expect to support policy development
based on quantitative bases.
・It is expected that data-driven policies will have persuasive power
for citizens.
・Conduct of policies based on data will support to realize the
smart city.
Conclusions
3 Conclusions and future works 64
65. Challenges
Development of human resources who can handle and
analyze MGD is needed.
・We need to acquire skills to handle and understand MGD.
> Programming skills to handle big data
> Skills to handle GIS software for visualization
> Knowledge of statistics to analyze results
Integration of MGD with field data
・We need to understand MGD is not universal data.
・To integrate MGD with field data, MGD will be used more than now.
> Skill and sense to integrate field data with MGD
Collaboration with government sectors for smart cities
・To realize smart cities, we need to collaborate with national and
local governments and to share our researches and their
tasks.
・It is difficult to collect useful public data. However it is possible if
our suggestion is helpful for local government and citizens.
3 Conclusions and future works 65
66. Thank you for your kind attention
<Contact>
Yuki Akiyama(aki@csis.u-tokyo.ac.jp)
Assistant professor
Center for Spatial Information Science (CSIS)
The University of Tokyo
URL: http://akiyama-lab.jp/yuki/
(You can download some my papers and slides)
Web page of Micro Geo Data Forum
http://microgeodata.jp/
Search「秋山祐樹」or” akiyama.yuuki”!