Qunying Huang

Bio: Qunying Huang is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Cloud computing & Geospatial analysis. The author has an hindex of 24, co-authored 89 publications receiving 2520 citations. Previous affiliations of Qunying Huang include George Mason University.

Big Data and cloud computing: innovation opportunities and challenges

Abstract: Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i...

Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?

Abstract: The geospatial sciences face grand information technology (IT) challenges in the twenty-first century: data intensity, computing intensity, concurrent access intensity and spatiotemporal intensity. These challenges require the readiness of a computing infrastructure that can: (1) better support discovery, access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries; (2) provide real-time IT resources to enable real-time applications, such as emergency response; (3) deal with access spikes; and (4) provide more reliable and scalable service for massive numbers of concurrent users to advance public knowledge. The emergence of cloud computing provides a potential solution with an elastic, on-demand computing platform to integrate – observation systems, parameter extracting algorithms, phenomena simulations, analytical visualization and decision support, and to provide social impact and user feedback – the essential eleme...

Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery

Abstract: Social media data have emerged as a new source for detecting and monitoring disaster events. A number of recent studies have suggested that social media data streams can be used to mine actionable data for emergency response and relief operation. However, no effort has been made to classify social media data into stages of disaster management (mitigation, preparedness, emergency response, and recovery), which has been used as a common reference for disaster researchers and emergency managers for decades to organize information and streamline priorities and activities during the course of a disaster. This paper makes an initial effort in coding social media messages into different themes within different disaster phases during a time-critical crisis by manually examining more than 10,000 tweets generated during a natural disaster and referencing the findings from the relevant literature and official government procedures involving different disaster stages. Moreover, a classifier based on logistic regression is trained and used for automatically mining and classifying the social media messages into various topic categories during various disaster phases. The classification results are necessary and useful for emergency managers to identify the transition between phases of disaster management, the timing of which is usually unknown and varies across disaster events, so that they can take action quickly and efficiently in the impacted communities. Information generated from the classification can also be used by the social science research communities to study various aspects of preparedness, response, impact and recovery.

Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study

Abstract: A new methodology is introduced that leverages data harvested from social media for tasking the collection of remote-sensing imagery during disasters or emergencies. The images are then fused with multiple sources of contributed data for the damage assessment of transportation infrastructure. The capability is valuable in situations where environmental hazards such as hurricanes or severe weather affect very large areas. During these types of disasters it is paramount to ‘cue’ the collection of remote-sensing images to assess the impact of fast-moving and potentially life-threatening events. The methodology consists of two steps. First, real-time data from Twitter are monitored to prioritize the collection of remote-sensing images for evolving disasters. Commercial satellites are then tasked to collect high-resolution images of these areas. Second, a damage assessment of transportation infrastructure is carried out by fusing the tasked images with contributed data harvested from social media such as Flickr and Twitter, and any additional available data. To demonstrate its feasibility, the proposed methodology is applied and tested on the 2013 Colorado floods with a special emphasis in Boulder County and the cities of Boulder and Longmont.

Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us?

Abstract: Individual activity patterns are influenced by a wide variety of factors. The more important ones include socioeconomic status SES and urban spatial structure. While most previous studies relied heavily on the expensive travel-diary type data, the feasibility of using social media data to support activity pattern analysis has not been evaluated. Despite the various appealing aspects of social media data, including low acquisition cost and relatively wide geographical and international coverage, these data also have many limitations, including the lack of background information of users, such as home locations and SES. A major objective of this study is to explore the extent that Twitter data can be used to support activity pattern analysis. We introduce an approach to determine users’ home and work locations in order to examine the activity patterns of individuals. To infer the SES of individuals, we incorporate the American Community Survey ACS data. Using Twitter data for Washington, DC, we analyzed the activity patterns of Twitter users with different SESs. The study clearly demonstrates that while SES is highly important, the urban spatial structure, particularly where jobs are mainly found and the geographical layout of the region, plays a critical role in affecting the variation in activity patterns between users from different communities.

Remote Sensing And Image Interpretation

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Using Mpi Portable Parallel Programming With The Message Passing Interface

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