Reading the urban socio-spatial network through space syntax and geo-tagged Twitter data

2020 ◽  
Vol 25 (6) ◽  
pp. 738-757 ◽  
Author(s):  
Aminreza Iranmanesh ◽  
Resmiye Alpar Atun
Keyword(s):  
Space Syntax ◽  
Spatial Network ◽  
Twitter Data

scholarly journals Can Speed Enhance Our Understanding Of The Role Of Spatial Connectivity? The Creation Of A ‘Spatial-Speed’ Map

2017 ◽  
Author(s):  
Paul Goodship
Keyword(s):  
Space Syntax ◽  
Urban Transport ◽  
Transport Systems ◽  
Spatial Network ◽  
Syntax Analysis ◽  
Spatial Connectivity ◽  
Space Syntax Analysis ◽  
The City ◽  
Different Parts

Throughout Latin America urban cable-cars have fast become a normal sight with urban transport systems, taking residents and tourists to and from previously isolated locations. As the popularity of these new modes of transport grows, it is important to understand the role spatial connectivity plays in integrating previously segregated communities. This is possible using a Space Syntax methodology to analyse the connectivity of a spatial network. However, this does not taking into account different forms of movement affected by transport or local landscape. Therefore, the aim of this paper to explores the use of ‘speed’ as a measurement to enhance our interpretation of spatial connectivity, through the case of Medellin. ‘Speed’ is used because it provides a clear indication of connection times between different parts of the city and is comparable throughout a variety of conditions, such as transport and walking. An average speed is therefore calculated for each segment of Medellin’s spatial network, including all forms of transport, and is then combined with the results of a standard Space Syntax analysis, forming a hybrid ‘spatial’ and ‘speed’ map. For accuracy, the results are tested against a pedestrian movement survey conducted locally nearby each cable-car station. The findings indicate that by introducing ‘speed’ as a weighted measurement, the overall spatial network of the city is not significantly improved, yet when the area surrounding each cable-car is examined closely, local ‘through’ spaces is clearer, especially when spatial conditions, or the user, is non-standard.

4 Pattern Recognition for Healing Burns to Compute Evidence: Space Syntax and Machine Learning Analysis of Burns Center Karachi

2013 ◽  
Vol 1 (1) ◽  
pp. 13
Author(s):  
Javaria Manzoor Shaikh ◽  
JaeSeung Park
Keyword(s):  
Pattern Recognition ◽  
Prediction Models ◽  
Uv Light ◽  
Healing Process ◽  
Space Syntax ◽  
Depth Map ◽  
Routine Tasks ◽  
Hyper Plane ◽  
First Time ◽  
Learning Analysis

Usually elongated hospitalization is experienced byBurn patients, and the precise forecast of the placement of patientaccording to the healing acceleration has significant consequenceon healthcare supply administration. Substantial amount ofevidence suggest that sun light is essential to burns healing andcould be exceptionally beneficial for burned patients andworkforce in healthcare building. Satisfactory UV sunlight isfundamental for a calculated amount of burn to heal; this delicaterather complex matrix is achieved by applying patternclassification for the first time on the space syntax map of the floorplan and Browder chart of the burned patient. On the basis of thedata determined from this specific healthcare learning technique,nurse can decide the location of the patient on the floor plan, hencepatient safety first is the priority in the routine tasks by staff inhealthcare settings. Whereas insufficient UV light and vitamin Dcan retard healing process, hence this experiment focuses onmachine learning design in which pattern recognition andtechnology supports patient safety as our primary goal. In thisexperiment we lowered the adverse events from 2012- 2013, andnearly missed errors and prevented medical deaths up to 50%lower, as compared to the data of 2005- 2012 before this techniquewas incorporated.In this research paper, three distinctive phases of clinicalsituations are considered—primarily: admission, secondly: acute,and tertiary: post-treatment according to the burn pattern andhealing rate—and be validated by capable AI- origin forecastingtechniques to hypothesis placement prediction models for eachclinical stage with varying percentage of burn i.e. superficialwound, partial thickness or full thickness deep burn. Conclusivelywe proved that the depth of burn is directly proportionate to thedepth of patient’s placement in terms of window distance. Ourfindings support the hypothesis that the windowed wall is mosthealing wall, here fundamental suggestion is support vectormachines: which is most advantageous hyper plane for linearlydivisible patterns for the burns depth as well as the depth map isused.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

Controversial Analysis:- Sentimental Analysis of Twitter Data

2017 ◽  
Vol 7 (4) ◽  
pp. 96-100
Author(s):  
Samarth Jaykar Shetty ◽  
◽  
Badal Rakesh Thosani ◽  
Lenherd Deon Olivera ◽  
Supriya Kamoji ◽  
...  
Keyword(s):  
Twitter Data

Utilizing Twitter Data Analysis and Deep Learning to Identify Drug Use (Preprint)

2019 ◽  
Author(s):  
Joseph Tassone ◽  
Peizhi Yan ◽  
Mackenzie Simpson ◽  
Chetan Mendhe ◽  
Vijay Mago ◽  
...  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Deep Learning ◽  
Decision Tree ◽  
Semantic Meaning ◽  
Predictive Capability ◽  
Logistic Regression Models ◽  
Twitter Data ◽  
Data Points ◽  
Positive Classification

BACKGROUND The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. OBJECTIVE Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. METHODS Twitter social media tweets and attribute data were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset 3,696,150 rows. The predictive classification power of multiple methods was compared including regression, decision trees, and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. RESULTS The logistic regression and decision tree models utilized 12,142 data points for training and 1041 data points for testing. The results calculated from the logistic regression models respectively displayed an accuracy of 54.56% and 57.44%, and an AUC of 0.58. While an improvement, the decision tree concluded with an accuracy of 63.40% and an AUC of 0.68. All these values implied a low predictive capability with little to no discrimination. Conversely, the CNN-based classifiers presented a heavy improvement, between the two models tested. The first was trained with 2,661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. CONCLUSIONS Predictive analysis without a CNN is limited and possibly fruitless. Attribute-based models presented little predictive capability and were not suitable for analyzing this type of data. The semantic meaning of the tweets needed to be utilized, giving the CNN-based classifier an advantage over other solutions. Additionally, commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased scores, improving the predictive capability. CLINICALTRIAL None

scholarly journals The Impact of the Ring Roads on the Location Pattern of Shops in Town and City Centres. A Space Syntax Approach

2021 ◽  
Vol 13 (7) ◽  
pp. 3927
Author(s):  
Akkelies van Nes
Keyword(s):  
Urban Areas ◽  
Space Syntax ◽  
Optimal Location ◽  
Spatial Integration ◽  
Street Network ◽  
Location Pattern ◽  
Ring Road ◽  
Road Projects ◽  
The Impact ◽  
City Centres

This contribution demonstrates how inner ring roads change the location pattern of shops in urban areas with the application of the space syntax method. A market rational behaviour persists, in that shop owners always search for an optimal location to reach as many customers as possible. If the accessibility to this optimal location is affected by changes in a city’s road and street structure, it will affect the location pattern of shops. Initially, case studies of inner ring road projects in Birmingham, Coventry, Wolverhampton, Bristol, Tampere, and Mannheim show how their realisation affect the spatial structure of the street network of these cities and the location pattern of shops. The results of the spatial integration analyses of the street and road network are discussed with reference to changes in land-use before and after the implementation of ring roads, and current space syntax theories. As the results show, how an inner ring road is connected to and the type of the street network it is imposed upon dictates the resulting location pattern of shops. Shops locate and relocate themselves along the most spatially-integrated streets. Evidence on how new road projects influence the location pattern of shops in urban centres are useful for planning sustainable city centres.

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