Topology Density Map for Urban Data Visualization and Analysis

Feng, Zezheng; Li, Haotian; Zeng, Wei; Yang, Shuang–Hua; Qu, Huamin

doi:10.1109/tvcg.2020.3030469

Cited by 30 publications

(16 citation statements)

References 50 publications

(70 reference statements)

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“…When utilizing the density map in an urban area, the road network's topology is an essential factor to be considered. Therefore, Feng et al (2021) proposed a novel method named topology density map (Figure 3c) to visualize the urban data. They took the topology of the road network, the direction of the road into account which makes the density estimation more accurate in visualizing urban data.…”

Section: Improving Current Visualization Techniques To Visualize Mass...mentioning

confidence: 99%

“…The results for the model can be encoded into different visual techniques with different visual representations. For instance, Feng et al (2021) proposed a novel approach to draw a density map to visualize the traffic data, considering the road topology and temporal variants. This novel density map can reveal the impact of the POIs (point of interests) in the urban area.…”

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confidence: 99%

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A survey of visual analytics in urban area

Feng

Yang

et al. 2022

Expert Systems

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Nowadays, the population has been overgrowing due to urbanization, yielding many severe problems in the urban area, including traffic congestion, unbalanced distribution of urban hotspots, air pollution and so on. Due to the uncertainty of the urban environment, it always needs to integrate experts' domain knowledge into solving these issues. In recent years, the visual analytics method has been widely used to assist domain experts in solving urban problems with its intuitiveness, interactivity and interpretability. In this survey, we first introduce the background of urban computing, present the motivation of visual analytics in the urban area and point out the characteristics of visual analytics methods. Second, we introduce the most frequently used urban data, analyse the main properties and provide an overview on how to use these data. Thereafter, we propose our taxonomy for visual analytics in the urban area and illustrate the taxonomy. The taxonomy provides four levels for visual analytics on urban data from a new perspective based on the four stages in data mining.Four levels from our taxonomy include: descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics. Finally, we conclude this survey by discussing the limitations of the existing related works and the challenges to visual analytics in the urban area.data mining, urban data, visual analytics, visualization | INTRODUCTIONRecently, the demand for building smart cities has been accelerated by the desire for high-quality daily lives for human beings and technological development. However, urbanization causes many human behaviours in the urban area and generates multi-source heterogeneous urban data.Meanwhile, an increase in the population can also lead to many severe problems, including traffic congestion, unbalanced distribution of urban hotspots, air pollution and many other domains. Paulos and Goodman (2004) first proposed the term 'urban computing', and many researchers have moved into this area so far. For example, in previous research, people can use the GPS trajectory data collected from vehicles to analyse traffic conditions (Xin et al., 2011) and keep safe traffic control (Abbasi et al., 2020). Furthermore, urban planners adopt cell phone data (Cho et al., 2011) collected from the base stations to explore the human mobility problem and optimize the location of urban hotspots. They also use air quality data collected from many sensors located in an urban area to analyse the air quality (Deng et al., 2019;Harbola et al., 2021). Moreover, such data can also be used on public health analysis (Antweiler et al., 2021), transit route planning, crime analysis (Svicarovic et al., 2021) and decision-making for emergency response (Johnson & Jankun-Kelly, 2021). This is beneficial to the development of a modern city and raises the happiness quotient.

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Section: Improving Current Visualization Techniques To Visualize Mass...mentioning

confidence: 99%

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confidence: 99%

A survey of visual analytics in urban area

Feng

Yang

et al. 2022

Expert Systems

Self Cite

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“…Visualization has been widely used for exploratory data analysis and decision making in various domains including stock trading [39], online education [23] and urban planning [15]. However, most existing tools for creating visualizations heavily rely on users' manual specifications [19].…”

Section: Visualization Recommendationmentioning

confidence: 99%

KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommendation

Li,

Wang,

Zhang

et al. 2021

Preprint

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Fig. 1. This figure illustrates the overall workflow of KG4Vis. We extract features from existing dataset-visualization pairs and construct a knowledge graph (KG). Then the embeddings of entities and relations in the KG are learned. Based on the embeddings, we conduct inference on a new dataset and finally recommend multiple visualizations. Also, various rules are extracted based on the embeddings and presented together with recommended visualizations to improve the interpretability of visualization recommendation.

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“…In particular, they showed that thefts tend to concentrate more than robberies and robberies more than burglaries. Poveda (2012) studied socio-economic and violent crime in seven cities, showing that cities' economic deprivation (SPICER et al, 2016) VitalVizor Ye, 2018), (FENG et al, 2020) Trajgraph (Huang et al, 2016) VitalVizor Ye, 2018) and high population density are strong factors in homicide rates. Additionally, they found that economic growth, inequality, poverty, and human capital had a negative influence on violent crimes.…”

Section: Socio-economic Based Crime Analysismentioning

confidence: 99%

“…Discussed and suggested by domain experts, we decided to take the streets intersection as the 'analysis unit' for practical and substantial reasons: (i) due the availability of input data, urban features are unknown for us (FENG et al, 2020); (ii) When a crime event occurs generally, the police officer reports the location as the intersection of streets (BRAGA; HUREAU; PA-PACHRISTOS, 2011); (iii) we can only measure the average of the criminality in a road segment, whilst the crime events positions are not exact (BRAGA; HUREAU; PAPACHRISTOS, 2011); (iv) Qualitative research suggests that robbers find street corners to be good locations to locate potential victims (JACOBS, 2000;JEAN, 2007); and (v) inner-city residents meet, socialize, and sometimes live out significant portions of their daily lives on streets corners (LIEBOW, 2003).…”

Section: Data Modellingmentioning

confidence: 99%

Visual Crime Pattern Analysis

Garcia-Zanabria¹

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Studying and analyzing crime patterns in big cities is a challenging spatio-temporal problem. The difficulty of the problem is linked to different factors such as data modeling, unsophisticated hotspot detection techniques, spatio-temporal patterns, and study delimitation. Previous works have mostly focused on the analysis of crimes with the intent of uncovering patterns associated to social factors, seasonality, and urban activities in whole districts, regions, and neighborhoods. Those tools can hardly allow micro-scale crime analysis closely related to crime opportunity, whose understanding is fundamental for planning preventive actions. Given that, enabling a combined analysis of spatial patterns and the visualization of the different crime patterns hidden in their evolution over time is another challenge faced by most crime analysis tools. In this dissertation, we propose a set of approaches for interactive visual crime analysis. Relying on machine learning methods, statistical and mathematical mechanisms, and visualization, each proposed methodology focus on solving specific crime-related problems. These proposed tools to explore specific locations of the city turned out to be essential for domain experts to accomplish their analysis in a bottom-up fashion, revealing how urban features related to mobility, passerby behavior, and presence of public infrastructures (e.g., terminals of public transportation and schools) can influence the quantity and type of crimes. The effectiveness and usefulness of the proposed methodologies have been demonstrated with a comprehensive set of quantitative and qualitative analyses, as well as case studies performed by domain experts involving real data of different-sized cities. The experiments show the capability of our approaches in identifying different crime-related phenomena.

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Topology Density Map for Urban Data Visualization and Analysis

Cited by 30 publications

References 50 publications

A survey of visual analytics in urban area

A survey of visual analytics in urban area

KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommendation

Visual Crime Pattern Analysis

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