Relief and recovery from disasters (both natural and human-made) require a coordinated approach across several federal and state government agencies. In order to achieve optimal resource allocation and deployment of first responders, accurate and timely assessment of the impact and extent of destruction are the cornerstones to any recovery effort. Ideally, this knowledge should be gathered and shared within the first 0-24 hours (termed as "Acute Phase" by the U.S. CDC guideline) for informed decision-making. But achieving this poses significant challenges for the data collection and data harmonization processes, particularly when voluminous data are being generated from diverse and distributed sources during the disaster responses. To this end, this work developed a scalable and efficient workflow to dynamically collect and harmonize crowd-sourced geographic multi-modal data, and then assess critical infrastructure (CI) damaged during disaster events. We demonstrate the application of our framework with two real-world experiences in addressing post-disaster recovery efforts - for the Bahamas (Natural - due to Hurricane Dorian, 2019) and Beirut (Human-made - due to explosion caused by the ammonium nitrate stored in a warehouse, 2020). We have illustrated that a coordinated effort is needed for planning as well as for execution to achieve informed decision making.

The temporal nature of humans interaction with Points of Interest (POIs) in cities can differ depending on place type and regional location. Times when many people are likely to visit restaurants (place type) in Italy, may differ from times when many people are likely to visit restaurants in Lebanon (i.e. regional differences). Geosocial data are a powerful resource to model these temporal differences in cities, as traditional methods used to study cross-cultural differences do not scale to a global level. As cities continue to grow in population and economic development, research identifying the social and geophysical (e.g., climate) factors that influence city function remains important and incomplete. In this work, we take a quantitative approach, applying dynamic time warping and hierarchical clustering on temporal signatures to model geosocial temporal patterns for Retail and Restaurant Facebook POIs hours of operation for more than 100 cities in 90 countries around the world. Results show cities’ temporal patterns cluster to reflect the cultural region they represent. Furthermore, temporal patterns are influenced by a mix of social and geophysical factors. Trends in the data suggest social factors influence unique drops in temporal signatures, and geophysical factors influence when daily temporal patterns start and finish.

Buildings are one of the primary energy consumers in any city's energy use [12]. Presence and absence of humans is a major contributing factor to the energy use in a building. In this paper, we present an approach to generating a realistic model of human building occupancy throughout a typical work week. We use the Toolbox for Urban Mobility Systems (TUMS) to generate a synthetic population based on population distribution estimates, we schedule the population's daily commute based on National Household Travel Survey (NHTS) survey data, and we simulate their daily travel patterns using an agent-based transportation simulation (TRANSIMS). We process and fuse the simulation output to produce a list of the first and last seen location of each agent in the simulation. Based on the arrival at the last destination, we map each agent to one of the nearby buildings. Using these agent arrivals, as well as NHTS data, we create an hourly occupancy schedule for each building. We successfully demonstrate this workflow at the example of the Chicago Loop, a major business district in Chicago, Illinois.

Spatial Co-location Pattern (SCP) mining continues to play a critical role in understanding the morphology of urban functional spaces of world cities. It requires a large amount of fine-granular data and computing efficiency to handle the combinatorial explosion of co-location patterns. To this end, this work has two main contributions - i) We showcase a novel approach to perform SCP mining to characterize intra-city scale structure of urban functionality or co-located activity patterns using geosocial Points-of-Interest (POI) vector data. ii) We present a generalized and optimized parallel/distributed SCP mining algorithm implemented on a Hadoop MapReduce system and demonstrate the utility of our approach using the city of Berlin (Germany) as an example. The SCPs tend to vary across Berlin's municipal boroughs and at different spatial scales. Our findings on Berlin's functional structure conform to existing urban geography models. Such a data-driven exploration of massive urban POIs using distributed computing is first of its kind and can help better understand the changing dynamics of urban functionality, as well as physical, and social network structure around the world.

It is estimated that 50% of the global population lives in urban areas occupying just 0.4% of the Earth's surface. Understanding urban activity constitutes monitoring population density and its changes over time, in urban environments. Currently, there are limited mechanisms to non-intrusively monitor population density in real-time. The pervasive use of cellular phones in urban areas is one such mechanism that provides a unique opportunity to study population density by monitoring the mobility patterns in near real-time. Cellular carriers such as AT&T harvest such data through their cell towers; however, this data is proprietary and the carriers restrict access, due to privacy concerns. In this work, we propose a system that passively senses the population density and infers mobility patterns in an urban area by monitoring power spectral density in cellular frequency bands using periodic beacons from each cellphone without knowing who and where they are located. A wireless sensor network platform is being developed to perform spectral monitoring along with environmental measurements. Algorithms are developed to generate real-time fine-resolution population estimates.

Crowd-sourced and volunteered information, social media, and participatory sensors are capable of providing real-time activity data. Monitoring these sources in time of relevance and then using them to gather operational knowledge is important during crisis management. Beyond that, it's important to curate this information for geo-spatial research purposes, including land use classification and population occupancy analysis. In this demonstration, we will showcase PlanetSense - a geo-spatial research platform built to harness the existing power of archived data and add to that, the dynamics of heterogeneous real-time streaming data from social media and volunteered sources, seamlessly integrated with sophisticated machine learning algorithms and visualization tools. A demonstration will focus on - 1) Recent initiative emphasizing the need to harness crowd-sources, volunteered, and social media data at scale; 2) Anatomy and insight into data collection workflow. We will show the ability to harvest and process several terabytes of raw data in real-time; 3) A detailed discussion with insight into more than 20 sources of data will be given. These sources include text, sensors, as well as imagery data; 4) PlanetSense's end to end distributed architecture will be discussed with focus on collecting and processing high-volumes of streaming data in a Geo-Data Cloud. Data fusion methods and algorithms for integrating disparate data sources with existing legacy products. Data analytics and machine learning methods for generating operational intelligence on the fly; 5) In addition, PlanetSense "App" platform will be shown with hands-on application enabling interested audience to quickly develop and deploy solutions. 6) Several case studies will be discussed relevant to, land use classification, monitoring transient population, high-resolution occupancy analysis, mapping special events population, ability to uncover global breaking events and reactions in near-real time, ability to track protest, unrest, and monitor other societal turbulences as they happen, and real-time monitoring of infrastructure outages.

Geospatial intelligence has traditionally relied on the use of archived and unvarying data for planning and exploration purposes. In consequence, the tools and methods that are architected to provide insight and generate projections only rely on such datasets. Albeit, if this approach has proven effective in several cases, such as land use identification and route mapping, it has severely restricted the ability of researchers to inculcate current information in their work. This approach is inadequate in scenarios requiring real-time information to act and to adjust in ever changing dynamic environments, such as evacuation and rescue missions. In this work, we propose PlanetSense, a platform for geospatial intelligence that is built to harness the existing power of archived data and add to that, the dynamics of real-time streams, seamlessly integrated with sophisticated data mining algorithms and analytics tools for generating operational intelligence on the fly. The platform has four main components -- i) GeoData Cloud -- a data architecture for storing and managing disparate datasets; ii) Mechanism to harvest real-time streaming data; iii) Data analytics framework; iv) Presentation and visualization through web interface and RESTful services. Using two case studies, we underpin the necessity of our platform in modeling ambient population and building occupancy at scale.

Transportation simulation technologies should accurately model traffic demand, distribution, and assignment parameters for urban environment simulation. These three parameters significantly impact transportation engineering benchmark process, are also critical in realizing realistic traffic modeling situations. In this paper, we model and characterize traffic density distribution of thousands of locations, intersection, and roadways around the world. The traffic densities are generated from millions of images collected over several years and processed using computer vision techniques. The resulting traffic density distribution time series are then analyzed. It is found using the goodness-of-fit test that the traffic density distributions follow heavy-tail models such as Weibull in over 90% of analyzed locations. Moreover, a heavy-tail gives rise to long-range dependence and self-similarity, which we studied by estimating the Hurst exponent (H). Our analysis based on seven different Hurst estimators strongly indicates that the traffic distribution patterns are stochastically self-similar (0.5 ≤ H ≤ 1.0). We believe this is an important finding that will influence the design and development of the next generation traffic simulation techniques and also aid in accurately modeling traffic engineering of urban systems. In addition, it shall provide a much-needed input for the development of smart cities.