Exploring Imputation Techniques for Missing Data in Transportation Management Systems

2003 ◽  
Vol 1836 (1) ◽  
pp. 132-142 ◽  
Author(s):  
Brian L. Smith ◽  
William T. Scherer ◽  
James H. Conklin
Keyword(s):  
Missing Data ◽  
Urban Areas ◽  
Large Scale ◽  
Management Systems ◽  
Data Sets ◽  
Transportation Industry ◽  
Transportation Management ◽  
Reduced Data ◽  
Natural Characteristics

Many states have implemented large-scale transportation management systems to improve mobility in urban areas. These systems are highly prone to missing and erroneous data, which results in drastically reduced data sets for analysis and real-time operations. Imputation is the practice of filling in missing data with estimated values. Currently, the transportation industry generally does not use imputation as a means for handling missing data. Other disciplines have recognized the importance of addressing missing data and, as a result, methods and software for imputing missing data are becoming widely available. The feasibility and applicability of imputing missing traffic data are addressed, and a preliminary analysis of several heuristic and statistical imputation techniques is performed. Preliminary results produced excellent performance in the case study and indicate that the statistical techniques are more accurate while maintaining the natural characteristics of the data.

Building Reconstruction After Large-Scale Disasters A Case Study of Ishinomaki City After the Great East Japan Earthquake

2016 ◽  
Vol 11 (sp) ◽  
pp. 780-788 ◽  
Author(s):  
Michio Ubaura ◽  
◽  
Junpei Nieda ◽  
Masashi Miyakawa ◽  
Keyword(s):  
Land Use Planning ◽  
Urban Areas ◽  
Large Scale ◽  
Agricultural Land ◽  
Recovery Process ◽  
Great East Japan Earthquake ◽  
Planning System ◽  
Compact City ◽  
Building Reconstruction

In large-scale disasters and the subsequent recovery process, land usage and urban spatial forms change. It is therefore important to use this process as an opportunity to create a more sustainable spatial structure. This study considers the urban spatial transformations that took place after the Great East Japan Earthquake, their causes, and accompanying issues by investigating building construction in the recovery process. The authors discovered that individual rebuilding is primarily concentrated in vacant lots within the city’s existing urbanized areas. This is likely due to the spatial impact of the urban planning and agricultural land use planning system, the area division of urbanization promotion areas, and the urbanization restricted areas, all of which were in place prior to the disaster and which have guided development. On the other hand, there are areas severely damaged by tsunami in which there has been little reconstruction of housing that was completely destroyed. The authors concluded that building reconstruction in Ishinomaki City resulted in both the formation of a high-density compact city and also very low-density urban areas.

scholarly journals New Curriculum Reform in China and its Impact on Teachers

2013 ◽  
Vol 41 (2) ◽  
Author(s):  
Linyuan Guo
Keyword(s):  
Curriculum Reform ◽  
Education System ◽  
Urban Areas ◽  
Large Scale ◽  
Teaching Profession ◽  
Urban China ◽  
Complex Dimensions ◽  
Challenges And Opportunities ◽  
The Impact

China, the developing country with the largest and oldest public education system, is transforming its education system through a nation-wide curriculum reform. This large-scale curriculum change signifies China's complex and multi-dimensional processes and endeavors in empowering its educational system to meet the challenges and opportunities in the era of globalization. This paper reports on an interpretive case study with a particular interest in understanding the impact of the nation-wide curriculum reform on teachers in urban areas. Findings from this study present the complex dimensions of teachers’ lived experiences during this dramatic education change and shed new insights on the current teaching profession in urban China.

scholarly journals Determining rural areas vulnerable to illegal dumping using GIS techniques. Case study: Neamț county, Romania

2016 ◽  
Author(s):  
Florin Constantin MIHAI
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Regional Scale ◽  
Assessment Method ◽  
Household Waste ◽  
Management Systems ◽  
Field Observations ◽  
Carpathian Region ◽  
Illegal Dumping

The paper aims to mapping the potential vulnerable areas to illegal dumpingof household waste from rural areas in the extra- Carpathian region ofNeamț County. These areas are ordinary in the proximity of built-up areasand buffers areas of 1km were delimited for every locality. Based onvarious map layers in vector formats ( land use, rivers, buil-up areas,roads etc) an assessment method is performed to highlight the potentialareas vulnerable to illegal dumping inside these buffer areas at localscale. The results are corelated to field observations and currentsituation of waste management systems. The maps outline local disparitiesdue to various geographical conditions of county. This approach is anecesary tool in EIA studies particularly for rural waste managementsystems at local and regional scale which are less studied in currentliterature than urban areas.

Mobile Phones as a Lens into Slum Dynamics

2012 ◽  
pp. 334-352
Author(s):  
Amy Wesolowski ◽  
Nathan Eagle
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Urban Areas ◽  
Large Scale ◽  
Mobile Phone Data ◽  
Data Sets ◽  
Location Data ◽  
Planning Decisions ◽  
Large Scale Data ◽  
Slum Dwellers

The worldwide adoption of mobile phones is providing researchers with an unprecedented opportunity to utilize large-scale data to better understand human behavior. This chapter highlights the potential use of mobile phone data to better understand the dynamics driving slums in Kenya. Given slum dwellers informal and transient lifetimes (in terms of places of employment, living situations, etc.), comprehensive longitude behavioral data sets are rare. Working with communication and location data from Kenya’s leading mobile phone operator, the authors use mobile phone data as a window into the social, mobile, and economic dimensions of slum dwellers. The authors address questions about the functionality of slums in urban areas in terms of economic, social, and migratory dynamics. In particular, the authors discuss economic mobility in slums, the importance of social networks, and the connectivity between slums and other urban areas. With four years until the 2015 deadline to meet the Millennium Development Goals, including the goal to improve the lives of slum dwellers worldwide, there is a great need for tools to make development and urban planning decisions more beneficial and precise.

A Dynamic Scaling Approach in Hadoop YARN

2022 ◽  
Vol 12 (2) ◽  
pp. 0-0
Keyword(s):  
Large Scale ◽  
Distributed Processing ◽  
Dynamic Scaling ◽  
Data Sets ◽  
Large Scale Data ◽  
The People ◽  
Big Data Applications ◽  
Scaling Process ◽  
And Performance

In Cloud based Big Data applications, Hadoop has been widely adopted for distributed processing large scale data sets. However, the wastage of energy consumption of data centers still constitutes an important axis of research due to overuse of resources and extra overhead costs. As a solution to overcome this challenge, a dynamic scaling of resources in Hadoop YARN Cluster is a practical solution. This paper proposes a dynamic scaling approach in Hadoop YARN (DSHYARN) to add or remove nodes automatically based on workload. It is based on two algorithms (scaling up/down) which are implemented to automate the scaling process in the cluster. This article aims to assure energy efficiency and performance of Hadoop YARN’ clusters. To validate the effectiveness of DSHYARN, a case study with sentiment analysis on tweets about covid-19 vaccine is provided. the goal is to analyze tweets of the people posted on Twitter application. The results showed improvement in CPU utilization, RAM utilization and Job Completion time. In addition, the energy has been reduced of 16% under average workload.

scholarly journals Multivariate Pointwise Information-Driven Data Sampling and Visualization

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 699
Author(s):  
Soumya Dutta ◽  
Ayan Biswas ◽  
James Ahrens
Keyword(s):  
Large Scale ◽  
Scientific Data ◽  
Data Sets ◽  
Data Sampling ◽  
Sampled Data ◽  
Statistical Association ◽  
Sampling Algorithm ◽  
Data Summarization ◽  
Multiple Variables ◽  
Reduced Data

With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets.

scholarly journals URBAN GROWTH SCENARIOS OF A FUTURE MEGA CITY: CASE STUDY AHMEDABAD

2016 ◽  
Vol III-2 ◽  
pp. 165-172 ◽  
Author(s):  
A. Lehner ◽  
V. Kraus ◽  
K. Steinnocher
Keyword(s):  
Remote Sensing ◽  
Urban Growth ◽  
Urban Areas ◽  
Large Scale ◽  
Smart Cities ◽  
Urban Migration ◽  
Indian Government ◽  
Urban Remote Sensing ◽  
Urban Growth Scenarios

The study of urban areas and their development focuses on cities, their physical and demographic expansion and the tensions and impacts that go along with urban growth. Especially in developing countries and emerging national economies like India, consistent and up to date information or other planning relevant data all too often is not available. With its Smart Cities Mission, the Indian government places great importance on the future developments of Indian urban areas and pays tribute to the large-scale rural to urban migration. The potentials of urban remote sensing and its contribution to urban planning are discussed and related to the Indian Smart Cities Mission. A case study is presented showing urban remote sensing based information products for the city of Ahmedabad. Resulting urban growth scenarios are presented, hotspots identified and future action alternatives proposed.

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