scholarly journals Evaluation of Distance Between Pedestrian Crossings by Students in One of the Polish Cities

2013 ◽  
Vol 59 (4) ◽  
pp. 547-559
Author(s):  
M. Kruszyna
Keyword(s):  
Data Mining ◽  
City Centre ◽  
Fuzzy Data ◽  
Two Dimensional ◽  
Data Mining Approach ◽  
Membership Value ◽  
Third Dimension ◽  
The City

Abstract In this paper, the distances between pedestrian crossings in twenty one places in the city of Wrocław, together with their evaluation by the researched groups of students, were analyzed. The database created from the collected questionnaires contains a set of two-dimensional variables: the distance between crossings and the rating of the students. The database set was analyzed using a fuzzy data mining approach to create particular clusters. Various numbers of clusters were analyzed, and the division of data into three clusters made it possible to relate the analysis to the LOS methodology. Each variable was enriched with a third dimension representing a membership value. The obtained evaluated distances are similar to values recommended in literature, although the distances highly evaluated by the students do not often occur in reality. This might suggest that there is the need to create new crossings, especially in the city centre, where pedestrian trafic is or should be important.

scholarly journals Pemetaan untuk Strategi Dakwah di Kota Semarang Menggunakan Pendekatan Data Mining (Mapping for Da'wah Strategy in Semarang City Using Data Mining Approach)

2021 ◽  
Vol 32 (1) ◽  
pp. 40
Author(s):  
Abdul Karim ◽  
Adeni Adeni ◽  
Fitri Fitri ◽  
Alifa Nur Fitri ◽  
Mustofa Hilmi ◽  
...  
Keyword(s):  
Data Mining ◽  
Successful Implementation ◽  
Optimum Number ◽  
Number Of Clusters ◽  
Islamic Studies ◽  
Data Mining Approach ◽  
The Republic ◽  
Fcm Analysis ◽  
Using Data ◽  
The City

This paper aims to explore the potential of da'wah in the city of Semarang with a data mining approach. The data mining approach is carried out by implementing the fuzzy c-means (FCM) algorithm in order to obtain the optimum number of clusters in the potential clustering of da'wah in the city of Semarang. The data used in this study from the Ministry of Religion of the Republic of Indonesia and the Central Statistics Agency (BPS) of Semarang City. The results of the FCM analysis show that the optimum number of clusters is two clusters, where the sub-district in the second cluster is an area with a high potential for da'wah. This study provides information that in effective da'wah activities, certainty and clarity is needed regarding the targets of da'wah through mapping of da'wah in the form of clustering potential da'wah. This can be a consideration of dakwah strategies for the successful implementation of da'wah studies so that an increase in the target behavior of da'wah can be achieved. The application of FCM to get the optimum cluster of potential da'wah in order to produce da'wah mapping is novelty in the field of Islamic studies, especially the science of da'wah.

scholarly journals Precise and Accurate Job Cycle Time Forecasting in a Wafer Fabrication Factory with a Fuzzy Data Mining Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Toly Chen ◽  
Richard Romanowski
Keyword(s):  
Data Mining ◽  
Cycle Time ◽  
Principal Component ◽  
Wafer Fabrication ◽  
Fuzzy Data ◽  
Lower And Upper Bounds ◽  
Forecasting Accuracy ◽  
Data Mining Approach ◽  
Backpropagation Network ◽  
Better Than

Many data mining methods have been proposed to improve the precision and accuracy of job cycle time forecasts for wafer fabrication factories. This study presents a fuzzy data mining approach based on an innovative fuzzy backpropagation network (FBPN) that determines the lower and upper bounds of the job cycle time. Forecasting accuracy is also significantly improved by a combination of principal component analysis (PCA), fuzzy c-means (FCM), and FBPN. An applied case that uses data collected from a wafer fabrication factory illustrates this fuzzy data mining approach. For this applied case, the proposed methodology performs better than six existing data mining approaches.

Instrumentation For Quantitative Microscopy

1977 ◽  
Vol 35 ◽  
pp. 206-209
Author(s):  
B. Ralph ◽  
A.R. Jones
Keyword(s):  
Volume Fraction ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Automatic Data ◽  
Phase Volume Fraction ◽  
Statistical Confidence ◽  
Resultant Data ◽  
Automatic Data Collection ◽  
Third Dimension ◽  
Evolution Of Microstructure

In all fields of microscopy there is an increasing interest in the quantification of microstructure. This interest may stem from a desire to establish quality control parameters or may have a more fundamental requirement involving the derivation of parameters which partially or completely define the three dimensional nature of the microstructure. This latter categorey of study may arise from an interest in the evolution of microstructure or from a desire to generate detailed property/microstructure relationships. In the more fundamental studies some convolution of two-dimensional data into the third dimension (stereological analysis) will be necessary.In some cases the two-dimensional data may be acquired relatively easily without recourse to automatic data collection and further, it may prove possible to perform the data reduction and analysis relatively easily. In such cases the only recourse to machines may well be in establishing the statistical confidence of the resultant data. Such relatively straightforward studies tend to result from acquiring data on the whole assemblage of features making up the microstructure. In this field data mode, when parameters such as phase volume fraction, mean size etc. are sought, the main case for resorting to automation is in order to perform repetitive analyses since each analysis is relatively easily performed.

Restricting Traffic into the City Centre: Has Madrid Central Been Effective to Reduce NO 2 Levels?

2019 ◽  
Author(s):  
Rafael Salas ◽  
María José Pérez Villadóniga ◽  
Juan Prieto Rodríguez ◽  
Ana Russo
Keyword(s):  
City Centre ◽  
The City

What Makes a City Bikeable? A Study of Intercity and Intracity Patterns of Bicycle Ridership using Mobike Big Data Records

2020 ◽  
Vol 46 (1) ◽  
pp. 55-75
Author(s):  
Ying Long ◽  
Jianting Zhao
Keyword(s):  
Big Data ◽  
Average Distance ◽  
Policy Implications ◽  
Rating System ◽  
City Centre ◽  
Average Score ◽  
Policy Makers ◽  
Usage Frequency ◽  
Share Data ◽  
The City

This paper examines how mass ridership data can help describe cities from the bikers' perspective. We explore the possibility of using the data to reveal general bikeability patterns in 202 major Chinese cities. This process is conducted by constructing a bikeability rating system, the Mobike Riding Index (MRI), to measure bikeability in terms of usage frequency and the built environment. We first investigated mass ridership data and relevant supporting data; we then established the MRI framework and calculated MRI scores accordingly. This study finds that people tend to ride shared bikes at speeds close to 10 km/h for an average distance of 2 km roughly three times a day. The MRI results show that at the street level, the weekday and weekend MRI distributions are analogous, with an average score of 49.8 (range 0–100). At the township level, high-scoring townships are those close to the city centre; at the city level, the MRI is unevenly distributed, with high-MRI cities along the southern coastline or in the middle inland area. These patterns have policy implications for urban planners and policy-makers. This is the first and largest-scale study to incorporate mobile bike-share data into bikeability measurements, thus laying the groundwork for further research.

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