DATA SCIENCE CONFERENCE


Data Science Conference is one of the leading research topics in the international research conference domain. Data Science is a conference track under the Architecture and Urban Planning Conference which aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of Architecture and Urban Planning.

internationalconference.net provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of (Architecture and Urban Planning).

Data Science is not just a call for academic papers on the topic; it can also include a conference, event, symposium, scientific meeting, academic, or workshop.

You are welcome to SUBMIT your research paper or manuscript to Data Science Conference Track will be held at .

Data Science is also a leading research topic on Google Scholar, Semantic Scholar, Zenedo, OpenAIRE, BASE, WorldCAT, Sherpa/RoMEO, Elsevier, Scopus, Web of Science.

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I. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

MARCH 19 - 20, 2019
ISTANBUL, TURKEY

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III. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

AUGUST 21 - 22, 2019
LONDON, UNITED KINGDOM

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IV. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

OCTOBER 08 - 09, 2019
NEW YORK, UNITED STATES

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V. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

DECEMBER 12 - 13, 2019
ROME, ITALY

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VI. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

FEBRUARY 13 - 14, 2020
LONDON, UNITED KINGDOM

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VII. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

APRIL 15 - 16, 2020
BARCELONA, SPAIN

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VIII. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

MAY 11 - 12, 2020
ISTANBUL, TURKEY

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IX. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

JUNE 05 - 06, 2020
SAN FRANCISCO, UNITED STATES

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X. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

JULY 20 - 21, 2020
PARIS, FRANCE

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XI. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

AUGUST 10 - 11, 2020
NEW YORK, UNITED STATES

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XII. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

SEPTEMBER 10 - 11, 2020
TOKYO, JAPAN

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XIII. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

SEPTEMBER 16 - 17, 2020
ZÜRICH, SWITZERLAND

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XIV. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

OCTOBER 21 - 22, 2020
BARCELONA, SPAIN

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XV. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

NOVEMBER 02 - 03, 2020
SAN FRANCISCO, UNITED STATES

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XVI. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

NOVEMBER 12 - 13, 2020
ISTANBUL, TURKEY

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XVII. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

NOVEMBER 19 - 20, 2020
SINGAPORE, SINGAPORE

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XVIII. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

DECEMBER 15 - 16, 2020
BANGKOK, THAILAND

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XIX. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

DECEMBER 28 - 29, 2020
PARIS, FRANCE

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XX. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

FEBRUARY 13 - 14, 2021
LONDON, UNITED KINGDOM

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XXI. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

APRIL 15 - 16, 2021
BARCELONA, SPAIN

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XXII. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

MAY 11 - 12, 2021
ISTANBUL, TURKEY

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XXIII. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

JUNE 05 - 06, 2021
SAN FRANCISCO, UNITED STATES

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XXIV. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

JULY 20 - 21, 2021
PARIS, FRANCE

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XXV. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

AUGUST 10 - 11, 2021
NEW YORK, UNITED STATES

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XXVI. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

SEPTEMBER 10 - 11, 2021
TOKYO, JAPAN

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XXVII. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

SEPTEMBER 16 - 17, 2021
ZÜRICH, SWITZERLAND

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XXVIII. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

OCTOBER 21 - 22, 2021
BARCELONA, SPAIN

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XXIX. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

NOVEMBER 02 - 03, 2021
SAN FRANCISCO, UNITED STATES

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XXX. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

NOVEMBER 12 - 13, 2021
ISTANBUL, TURKEY

FINISHED

XXXI. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

NOVEMBER 19 - 20, 2021
SINGAPORE, SINGAPORE

FINISHED

XXXII. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

DECEMBER 15 - 16, 2021
BANGKOK, THAILAND

FINISHED

XXXIII. INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

DECEMBER 28 - 29, 2021
PARIS, FRANCE

Architecture and Urban Planning Conference Call For Papers are listed below:

Previously Published Papers on "Data Science Conference"

  • Destination Port Detection for Vessels: An Analytic Tool for Optimizing Port Authorities Resources
    Authors: Lubna Eljabu, Mohammad Etemad, Stan Matwin, Keywords: Spatial temporal data mining, trajectory mining, trajectory similarity, resource optimization. DOI:10.5281/zenodo. Abstract: Port authorities have many challenges in congested ports to allocate their resources to provide a safe and secure loading/unloading procedure for cargo vessels. Selecting a destination port is the decision of a vessel master based on many factors such as weather, wavelength and changes of priorities. Having access to a tool which leverages Automatic Identification System (AIS) messages to monitor vessel’s movements and accurately predict their next destination port promotes an effective resource allocation process for port authorities. In this research, we propose a method, namely, Reference Route of Trajectory (RRoT) to assist port authorities in predicting inflow and outflow traffic in their local environment by monitoring AIS messages. Our RRo method creates a reference route based on historical AIS messages. It utilizes some of the best trajectory similarity measures to identify the destination of a vessel using their recent movement. We evaluated five different similarity measures such as Discrete Frechet Distance (DFD), Dynamic Time ´ Warping (DTW), Partial Curve Mapping (PCM), Area between two curves (Area) and Curve length (CL). Our experiments show that our method identifies the destination port with an accuracy of 98.97% and an f-measure of 99.08% using Dynamic Time Warping (DTW) similarity measure.
  • Spatial Data Science for Data Driven Urban Planning: The Youth Economic Discomfort Index for Rome
    Authors: Iacopo Testi, Diego Pajarito, Nicoletta Roberto, Carmen Greco, Keywords: Data science, spatial analysis, composite index, Rome, urban planning, youth economic discomfort index. DOI:10.5281/zenodo. Abstract: Today, a consistent segment of the world’s population lives in urban areas, and this proportion will vastly increase in the next decades. Therefore, understanding the key trends in urbanization, likely to unfold over the coming years, is crucial to the implementation of sustainable urban strategies. In parallel, the daily amount of digital data produced will be expanding at an exponential rate during the following years. The analysis of various types of data sets and its derived applications have incredible potential across different crucial sectors such as healthcare, housing, transportation, energy, and education. Nevertheless, in city development, architects and urban planners appear to rely mostly on traditional and analogical techniques of data collection. This paper investigates the prospective of the data science field, appearing to be a formidable resource to assist city managers in identifying strategies to enhance the social, economic, and environmental sustainability of our urban areas. The collection of different new layers of information would definitely enhance planners' capabilities to comprehend more in-depth urban phenomena such as gentrification, land use definition, mobility, or critical infrastructural issues. Specifically, the research results correlate economic, commercial, demographic, and housing data with the purpose of defining the youth economic discomfort index. The statistical composite index provides insights regarding the economic disadvantage of citizens aged between 18 years and 29 years, and results clearly display that central urban zones and more disadvantaged than peripheral ones. The experimental set up selected the city of Rome as the testing ground of the whole investigation. The methodology aims at applying statistical and spatial analysis to construct a composite index supporting informed data-driven decisions for urban planning.
  • Platform Urbanism: Planning towards Hyper-Personalisation
    Authors: Provides Ng, Keywords: Platform urbanism, hyper-personalisation, urban residency, digital data. DOI:10.5281/zenodo. Abstract: Platform economy is a peer-to-peer model of distributing resources facilitated by community-based digital platforms. In recent years, digital platforms are rapidly reconfiguring the public realm using hyper-personalisation techniques. This paper aims at investigating how urban planning can leapfrog into the digital age to help relieve the rising tension of the global issue of labour flow; it discusses the means to transfer techniques of hyper-personalisation into urban planning for plasticity using platform technologies. This research first denotes the limitations of the current system of urban residency, where the system maintains itself on the circulation of documents, which are data on paper. Then, this paper tabulates how some of the institutions around the world, both public and private, digitise data, and streamline communications between a network of systems and citizens using platform technologies. Subsequently, this paper proposes ways in which hyper-personalisation can be utilised to form a digital planning platform. Finally, this paper concludes by reviewing how the proposed strategy may help to open up new ways of thinking about how we affiliate ourselves with cities.
  • Review of the Road Crash Data Availability in Iraq
    Authors: Abeer K. Jameel, Harry Evdorides, Keywords: Data availability, Iraq, road safety. DOI:10.5281/zenodo. Abstract: Iraq is a middle income country where the road safety issue is considered one of the leading causes of deaths. To control the road risk issue, the Iraqi Ministry of Planning, General Statistical Organization started to organise a collection system of traffic accidents data with details related to their causes and severity. These data are published as an annual report. In this paper, a review of the available crash data in Iraq will be presented. The available data represent the rate of accidents in aggregated level and classified according to their types, road users’ details, and crash severity, type of vehicles, causes and number of causalities. The review is according to the types of models used in road safety studies and research, and according to the required road safety data in the road constructions tasks. The available data are also compared with the road safety dataset published in the United Kingdom as an example of developed country. It is concluded that the data in Iraq are suitable for descriptive and exploratory models, aggregated level comparison analysis, and evaluation and monitoring the progress of the overall traffic safety performance. However, important traffic safety studies require disaggregated level of data and details related to the factors of the likelihood of traffic crashes. Some studies require spatial geographic details such as the location of the accidents which is essential in ranking the roads according to their level of safety, and name the most dangerous roads in Iraq which requires tactic plan to control this issue. Global Road safety agencies interested in solve this problem in low and middle-income countries have designed road safety assessment methodologies which are basing on the road attributes data only. Therefore, in this research it is recommended to use one of these methodologies.
  • Microclimate Variations in Rio de Janeiro Related to Massive Public Transportation
    Authors: Marco E. O. Jardim, Frederico A. M. Souza, Valeria M. Bastos, Myrian C. A. Costa, Nelson F. F. Ebecken, Keywords: Big data, IoT, public transportation, public health system. DOI:10.5281/zenodo.3299529 Abstract: Urban public transportation in Rio de Janeiro is based on bus lines, powered by diesel, and four limited metro lines that support only some neighborhoods. This work presents an infrastructure built to better understand microclimate variations related to massive urban transportation in some specific areas of the city. The use of sensor nodes with small analytics capacity provides environmental information to population or public services. The analyses of data collected from a few small sensors positioned near some heavy traffic streets show the harmful impact due to poor bus route plan.
  • Automatic Thresholding for Data Gap Detection for a Set of Sensors in Instrumented Buildings
    Authors: Houda Najeh, Stéphane Ploix, Mahendra Pratap Singh, Karim Chabir, Mohamed Naceur Abdelkrim, Keywords: Building system, time series, diagnosis, outliers, delay, data gap. DOI:10.5281/zenodo.2571656 Abstract: Building systems are highly vulnerable to different kinds of faults and failures. In fact, various faults, failures and human behaviors could affect the building performance. This paper tackles the detection of unreliable sensors in buildings. Different literature surveys on diagnosis techniques for sensor grids in buildings have been published but all of them treat only bias and outliers. Occurences of data gaps have also not been given an adequate span of attention in the academia. The proposed methodology comprises the automatic thresholding for data gap detection for a set of heterogeneous sensors in instrumented buildings. Sensor measurements are considered to be regular time series. However, in reality, sensor values are not uniformly sampled. So, the issue to solve is from which delay each sensor become faulty? The use of time series is required for detection of abnormalities on the delays. The efficiency of the method is evaluated on measurements obtained from a real power plant: an office at Grenoble Institute of technology equipped by 30 sensors.
  • Estimation of Train Operation Using an Exponential Smoothing Method
    Authors: Taiyo Matsumura, Kuninori Takahashi, Takashi Ono, Keywords: Exponential smoothing method, open data, operation estimation, train schedule. DOI:10.5281/zenodo.1316818 Abstract: The purpose of this research is to improve the convenience of waiting for trains at level crossings and stations and to prevent accidents resulting from forcible entry into level crossings, by providing level crossing users and passengers with information that tells them when the next train will pass through or arrive. For this paper, we proposed methods for estimating operation by means of an average value method, variable response smoothing method, and exponential smoothing method, on the basis of open data, which has low accuracy, but for which performance schedules are distributed in real time. We then examined the accuracy of the estimations. The results showed that the application of an exponential smoothing method is valid.
  • A Study of the Adaptive Reuse for School Land Use Strategy: An Application of the Analytic Network Process and Big Data
    Authors: Wann-Ming Wey, Keywords: Adaptive reuse, analytic network process, big data, land use strategy. DOI:10.5281/zenodo.1315635 Abstract: In today's popularity and progress of information technology, the big data set and its analysis are no longer a major conundrum. Now, we could not only use the relevant big data to analysis and emulate the possible status of urban development in the near future, but also provide more comprehensive and reasonable policy implementation basis for government units or decision-makers via the analysis and emulation results as mentioned above. In this research, we set Taipei City as the research scope, and use the relevant big data variables (e.g., population, facility utilization and related social policy ratings) and Analytic Network Process (ANP) approach to implement in-depth research and discussion for the possible reduction of land use in primary and secondary schools of Taipei City. In addition to enhance the prosperous urban activities for the urban public facility utilization, the final results of this research could help improve the efficiency of urban land use in the future. Furthermore, the assessment model and research framework established in this research also provide a good reference for schools or other public facilities land use and adaptive reuse strategies in the future.
  • Integration of Big Data to Predict Transportation for Smart Cities
    Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin, Keywords: Big data, bus headway prediction, machine learning, public transportation. DOI:10.5281/zenodo.1315633 Abstract: The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.
  • Elemental Graph Data Model: A Semantic and Topological Representation of Building Elements
    Authors: Yasmeen A. S. Essawy, Khaled Nassar, Keywords: Building information modeling, elemental graph data model, geometric and topological data models, and graph theory. DOI:10.5281/zenodo.1132216 Abstract: With the rapid increase of complexity in the building industry, professionals in the A/E/C industry were forced to adopt Building Information Modeling (BIM) in order to enhance the communication between the different project stakeholders throughout the project life cycle and create a semantic object-oriented building model that can support geometric-topological analysis of building elements during design and construction. This paper presents a model that extracts topological relationships and geometrical properties of building elements from an existing fully designed BIM, and maps this information into a directed acyclic Elemental Graph Data Model (EGDM). The model incorporates BIM-based search algorithms for automatic deduction of geometrical data and topological relationships for each building element type. Using graph search algorithms, such as Depth First Search (DFS) and topological sortings, all possible construction sequences can be generated and compared against production and construction rules to generate an optimized construction sequence and its associated schedule. The model is implemented in a C# platform.

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