Using Topological Analysis to Support Event-Guided Exploration in Urban Data

Doraiswamy, Harish; Ferreira, Nivan; Damoulas, Theodoros; Freire, Juliana; Silva, Cláudio T.

doi:10.1109/tvcg.2014.2346449

Cited by 65 publications

(42 citation statements)

References 43 publications

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“…Even though our focus in this work is to enable seamless interaction on visual analysis tools, we would like to note that the spatial aggregation has utility in a variety of applications in several fields. For example, this type of query is commonly used to generate scalar functions for topological data analysis [11,16,37]. While these applications might not require interactivity per se, having fast response times would definitely improve analysis efficiency.…”

Section: Select Agg(a I ) From P R Where Ploc Inside Rgeometry [Anmentioning

confidence: 99%

GPU rasterization for real-time spatial aggregation over arbitrary polygons

et al. 2017

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Visual exploration of spatial data relies heavily on spatial aggregation queries that slice and summarize the data over different regions. These queries comprise computationally-intensive point-inpolygon tests that associate data points to polygonal regions, challenging the responsiveness of visualization tools. This challenge is compounded by the sheer amounts of data, requiring a large number of such tests to be performed. Traditional pre-aggregation approaches are unsuitable in this setting since they fix the query constraints and support only rectangular regions. On the other hand, query constraints are defined interactively in visual analytics systems, and polygons can be of arbitrary shapes. In this paper, we convert a spatial aggregation query into a set of drawing operations on a canvas and leverage the rendering pipeline of the graphics hardware (GPU) to enable interactive response times. Our technique trades-off accuracy for response time by adjusting the canvas resolution, and can even provide accurate results when combined with a polygon index. We evaluate our technique on two large real-world data sets, exhibiting superior performance compared to index-based approaches.

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Section: Select Agg(a I ) From P R Where Ploc Inside Rgeometry [Anmentioning

confidence: 99%

GPU rasterization for real-time spatial aggregation over arbitrary polygons

et al. 2017

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“…However, a major 122 drawback of visual data integration is that it can result in information loss (Lins et al 2013) and can be 123 onerous to operate as it often requires a large amount of trial-and-error to adequately explore a data set (Gu 124 and Wang 2013). Additionally, previous works (Bocconi et al 2015;Lopez et al 2012) have utilized pre-125 defined schemas representing certain task demands to integrate fixed data, sensor data and live social media 126 data while other studies have aimed to directly integrate the knowledge embedded in urban data through 127 machine learning (Zheng 2015) or computational typological analysis (Doraiswamy et al 2014). While 128 useful for specific tasks, such methods have limited flexibility and reusability because they introduce task-129 specific biases.…”

Section: Demand Centric Integration Methods 118mentioning

confidence: 99%

Urban Data Integration Using Proximity Relationship Learning for Design, Management, and Operations of Sustainable Urban Systems

Gupta

Yang

Jain

2019

J. Comput. Civ. Eng.

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10The world is rapidly urbanizing with 66% of the world's population expected to reside in cities by 2050. 11 This massive influx of new urban citizens is putting enormous pressure on city systems and bringing forth 12 challenges at the intersection of urban infrastructure, governance and the environment. As a result, 13 researchers and practitioners have turned to new advanced sensing and data analytics developed under the 14 burgeoning "smart city" movement to improve the design, management and operations of urban systems. 15However, data emerging from urban systems has been challenging to integrate, organize and analyze due 16 to their natural spatial, temporal and typological heterogeneity. In this paper, an Urban Data Integration 17 (UDI) framework is introduced that is capable of integrating heterogeneous urban data. The proposed UDI 18 framework is extensible to multiple types of urban systems, scalable to the growing amount and quickly 19 changing urban data streams and interpretable enough to help inform municipal decision-making. The UDI 20 framework utilizes a series of novel proximity relationship learning algorithms to automatically reconstruct 21 urban data in a graph database. The merits, applicability and efficacy of the proposed framework is 22 demonstrated by validating and testing it on data from a mid-size city in the United States and by 23 benchmarking its interpretability and computational performance for a typical urban analytics scenario 24 against current practice (i.e., a relational database). Results indicate that the UDI framework provides easier 25 and more computationally efficient exploration and querying of urban data and in turn can enable new 26 computational approaches to urban system design, management and operations. 27Keyword: data integration, graph database, proximity learning, smart city, urban data 28The heterogeneity of urban data and the nascent field of urban analytics both point towards the need for 53 integrating urban data early and often. For example, if new data becomes available on the air quality along 54 a main traffic corridor, a municipal official could want to map this new information to existing data sources 55 on related systems (e.g., traffic, roads), re-run analytical queries to understand mutual influences and then 56 take appropriate actions. Maintaining a high-level of interpretability is vital during the integration process 57 as the goal is to support urban design and operational decisions by municipal officials, policy-makers and 58 engineers. As a result, a useful urban data integration framework must be extensible to multiple urban 59 systems (and not system specific), scalable to the growing amounts of quickly changing urban data streams 60 and interpretable such that it can inform decision-making. 61In this paper, an Urban Data Integration (UDI) framework is introduced that integrates heterogeneous urban 62 data while maintaining extensibility, scalability and interpretability. The proposed UDI framework utilizes 63 a series of novel proxi...

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“…Doraiswamy et al . [DFD*14] formalized the relationship of regions in traffic data using topological persistence over a scalar function. The scalar function is usually but not constrained to the densities of traffic over a spatial domain.…”

Section: Structure‐wise Representations and Techniquesmentioning

confidence: 99%

Graphs in Scientific Visualization: A Survey

Wang

Tao

2016

Computer Graphics Forum

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Graphs represent general node‐link diagrams and have long been utilized in scientific visualization for data organization and management. However, using graphs as a visual representation and interface for navigating and exploring scientific data sets has a much shorter history, yet the amount of work along this direction is clearly on the rise in recent years. In this paper, we take a holistic perspective and survey graph‐based representations and techniques for scientific visualization. Specifically, we classify these representations and techniques into four categories, namely partition‐wise, relationship‐wise, structure‐wise and provenance‐wise. We survey related publications in each category, explaining the roles of graphs in related work and highlighting their similarities and differences. At the end, we reexamine these related publications following the graph‐based visualization pipeline. We also point out research trends and remaining challenges in graph‐based representations and techniques for scientific visualization.

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Using Topological Analysis to Support Event-Guided Exploration in Urban Data

Cited by 65 publications

References 43 publications

GPU rasterization for real-time spatial aggregation over arbitrary polygons

GPU rasterization for real-time spatial aggregation over arbitrary polygons

Urban Data Integration Using Proximity Relationship Learning for Design, Management, and Operations of Sustainable Urban Systems

Graphs in Scientific Visualization: A Survey

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