The current study examines the relationship between shootings and police firearm arrests at a more detailed spatial and temporal level than has previous work. Using data from Philadelphia during the years 2004 to 2007 two dynamics are investigated: the relationship between a shooting and subsequent police firearm arrests nearby in space and time; and the relationship between a police firearm arrest and subsequent shootings nearby in space and time. In order to simultaneously consider spatial and temporal variation at a more micro-level, the current study uses a modified version of the Knox (1964) close pair method, a spatio-temporal clustering technique first used to study contagious diseases, and later used to analyze near-repeat patterns in the study of crime. The first question explored the relationship between a shooting and subsequent police firearm arrests. Results showed elevated patterns of firearm arrests were approximately two and a half times greater than would be expected levels of firearm arrests than if shootings and subsequent firearm arrests lacked a spatio-temporal association. Greater than expected elevated patterns persisted for up to about a fifth of a mile away and about one week but the strength of these associations waned. The observed patterns suggest an immediate and geographically targeted police response to a shooting and a somewhat sustained effort. Turning attention to the next question, an initial slightly elevated level of shootings followed a firearm arrest but for only a couple of days and about one block; shooting swiftly dropped below expected levels as one moves away in time and space. The waning and eventual significant drop in shootings may arise from ecological deterrence, but any suppression of shootings was short-lived. Overall, the current work highlights the close associations in space and time between police and offenders and suggests that police and offender activity is not simultaneous as the police response to a shooting immediately whereas potential offender's response to police actions is moderately delayed. Potential implications for theory and policy regarding both police behavior/police organizational responsiveness and ecological deterrence are discussed. / Criminal Justice

Deployment of antenna arrays is a very promising solution to reduce the Multiple Access Interference (MAI) from high data rate users in the Wideband Code Division Multiple Access (W-CDMA) system. Combining the antenna array with a RAKE receiver, both of which exploits multipath diversity, can significantly improve the system performance. In this research, we investigate the performance of these beamformer-RAKE receivers, also known as two-dimensional (2-D) RAKE receiver, for the reverse link of the W-CDMA system. We consider three different Pilot Symbol Assisted (PSA) beamforming techniques, Direct Matrix Inversion (DMI), Least-Mean Square (LMS) and Recursive Least Square (RLS) adaptive algorithms. Two different Geometrically Based Single Bounce (GBSB) statistical channel models are considered, one, which is more suitable for array processing, and the other is conductive to RAKE combining. The performances of the 2-D RAKE receivers are evaluated in these two channel models as a function of the number of antenna elements and RAKE fingers. It is shown that, in both the cases, the 2-D RAKE receiver outperforms the conventional RAKE receiver and the conventional beamformer by a significant margin. Also, the output SINR expression of a 2-D RAKE receiver with the general optimum beamformer is derived. / Master of Science

With the advent of smart sensor devices and Internet of Things (IoT) in the rapid urbanizing cities, data is being generated, collected and analyzed to solve urban problems in the areas of transportation, epidemiology, emergency management, economics, and sustainability etc. The work in this area basically involves analyzing one or more types of data to identify and characterize their impact on other urban phenomena like traffic speed and ride-sharing, spread of diseases, emergency evacuation, share market and electricity demand etc. In this work, we perform spatio-temporal analysis of various urban datasets collected from different urban application areas. We start with presenting a framework for predicting traffic demand around a location of interest and explain how it can be used to analyze other urban activities. We use a similar method to characterize and analyze spatio-temporal criminal activity in an urban city. At the end, we analyze the impact of nearby traffic volume on the electric vehicle charging demand at a charging station. / Master of Science / Because of the ubiquity of the Internet and smart devices, a tremendous amount of data has been collected from multiple sources like vehicles, purchasing details, online searches etc., which is being used to develop innovative applications. These applications aim to improve economic, social and personal lives of people through new start-of-the-art techniques like machine learning and data analytics. With this motivation in mind, we present three applications leveraging the data collected from urban cities to improve the life of people living in such cities. First, we start by using taxi trip data, collected around a given location, and use it to develop a model that can predict taxi demand for next half hour. This model can be used to schedule advertisements or dispatch taxis depending upon the demand. Second, using a similar mathematical approach, we propose a strategy to predict the number of crimes that can happen at a given location on the next day. This helps in maintaining law and order in the city. As our third and last application, we use the traffic and historical charging data to predict electric vehicle charging demand for the next day. Electricity generating power plants can use this model to prepare themselves for the higher demand emerged because of the increasing use of electric vehicles.

The response of global soil respiration (Rs) to climate change determines how long the land can continue acting as a carbon sink in the future. This dissertation research identifies how temporal and spatial variation in environmental factors affects global scale Rs modeling and predictions of future Rs under global warming. Chapter 1 describes the recommend time range for measuring Rs across differing climates, biomes, and seasons and found that the best time for measuring the daily mean Rs is 10:00 am in almost all climates and biomes. Chapter 2 describes commonly used surrogates in Rs modeling and shows that air temperature and soil temperature are highly correlated and that they explain similar amounts of Rs variation; however, average monthly precipitation between 1961 and 2014, rather than monthly precipitation for a specific year, is a better predictor in global Rs modeling. Chapter 3 quantifies the uncertainty generated by four different assumptions of global Rs models. Results demonstrate that the time-scale of the data, among other sources, creates a substantial difference in global estimates, where the estimate of global annual Rs based on monthly Rs data (70.85 to 80.99 Pg C yr-1) is substantially lower than the current benchmark for land models (98 Pg C yr-1). Chapter 4 simulates future global Rs rates based on two temperature scenarios and demonstrates that temperature sensitivity of Rs will decline in warm climates where the level of global warming will reach 3°C by 2100 relative to current air temperature; however, these regional decelerations will be offset by large Rs accelerations in the boreal and polar regions. Chapter 5 compares CO2 fluxes from turfgrass and wooded areas of five parks in Blacksburg, VA and tests the ability of the Denitrification-Decomposition model to estimate soil temperature, moisture and CO2 flux across the seasons. Cumulatively, this work provides new insights into the current and future spatial and temporal heterogeneity of Rs and its relationship with environmental factors, as well as key insights in upscaling methodology that will help to constrain global Rs estimates and predict how global Rs will respond to global warming in the future. / Ph. D. / CO₂ flux emitted from global soil is the second largest carbon exchange between the land and atmosphere. Accurately estimating global soil CO₂ flux and how it responds to climate change is critical to predict terrestrial carbon stocks. The objectives of this dissertation are to evaluate how time-scale affects our ability to estimate global soil CO₂ flux. In Chapter 1, we show that the best time period for measuring daily mean soil CO₂ flux is at around 10:00 am in almost all climate regions and vegetation types. The previously recommended time range (09:00 am and 12:00 pm) reasonably captures the daily mean soil CO₂ flux. The results from Chapter 2 indicate that air temperature is a good proxy for soil temperature in modeling global soil CO₂ flux. However, monthly precipitation is a uniformly poor proxy for soil water content; instead, average monthly precipitation is a better predictor for global soil CO₂ flux modeling. Chapter 3 demonstrates that the time-scale used in parameterizing models strongly affects the prediction of global CO₂ flux. When using monthly time-scale soil CO₂ flux and air temperature data, soil CO₂ flux increases as air temperature increases at air temperatures below 27 ℃, but soil CO₂ flux begins to decrease when air temperature is over 27 ℃. However, when using annual time-scale data, this response to temperature is masked, soil CO₂ flux increases as air temperature increases in all temperature conditions. As a result, the estimate of global annual soil CO₂ flux, based on monthly soil respiration data (70.85 to 80.99 Pg C yr⁻¹ ), is lower than the estimate based on the annual soil respiration data (98 Pg C yr⁻¹ ). Chapter 4 shows that if the level of global warming maintains its current rate (3ºC by the year 2100), then the annual soil CO₂ flux will either decrease or remains the same in arid, winter-dry temperate and tropical climate regions. However, these regional decelerations were offset by large soil CO₂ flux accelerations in the boreal and polar regions. Chapter 5 shows a significant difference in CO₂ flux among the five selected parks in Blacksburg, VA. The Denitrification-Decomposition model, despite having been developed for agriculture and undeveloped lands, closely estimates soil temperature, moisture and CO₂ flux across the seasons and therefore can be used to estimate and understand CO₂ fluxes from urban ecosystems in future studies. This study highlights that the relationship between soil CO₂ fluxes and environmental factors such as air temperature and precipitation differs from region to region. The study also demonstrates that daily and monthly time-scale soil CO₂ fluxes and environmental data help constrain global soil CO₂ flux estimates and help to predict how global soil CO₂ fluxes will respond to global warming in the future.

Recent years have seen a huge increase in the amount of data collected from multiple sources: mobile phones are ubiquitous, social networks are widely used, cities are more and more connected and the mobility of people and goods has risen to a global scale. The Big Data Era has opened the doors to new kinds of studies that were unthinkable with previous qualitative methods: human behavior can now be analyzed with a fine-grained resolution, patterns of mobility and behavior can be extracted from the incredible amount of data collected every day. Modern large cities are becoming more and more interconnected and this phenomenon leads to an increasing communication and activities’ synchronization. Due to the amount of data available or for anonymization reasons, it is often necessary to aggregate data spatially and temporally. A natural representation of clustered mobility data is the temporal network representation. In this thesis we focus on these two aspects of spatial distance in human mobility: (i) we study the synchronization of 76 Italian cities, using mobile phone data, showing that both distance between cities and city size determine the synchronization in communication rhythms. Moreover, we show that the effect of the distance in synchronization decreases when the size of the city increases; (ii) we investigate how clustering continuous spatio-temporal data affects spatio-temporal network measures for real-life and synthetic datasets and analyze how spatio-temporal networks’ measures vary at different aggregation levels.

Visual analytics of large amounts of spatio-temporal data is challenging due to the overlap and clutter from movements of multiple objects. A common approach for analyzing such data is to consider how groups of items cluster and move together in space and time. However, most methods for showing Spatio-temporal Cluster (STC) properties, concentrate on a few dimensions of the cluster (e.g. the cluster movement direction or cluster density) and many other properties are not represented. Furthermore, while representing multiple attributes of clusters in a single view existing methods fail to preserve the original shape of the cluster or distort the actual spatial covering of the dataset. In this thesis, I propose a simple yet effective visualization, FlockViz, for showing multiple STC data dimensions in a single view by preserving the original cluster shape. To evaluate this method I develop a framework for categorizing the wide range of tasks involved in analyzing STCs. I conclude this work through a controlled user study comparing the performance of FlockViz with alternative visualization techniques that aid with cluster-based analytic tasks. Finally the exploration capability of FlockViz is demonstrated in some real life data sets such as fish movement, caribou movement, eagle migration, and hurricane movement. The results of the user studies and use cases confirm the advantage and novelty of the novel FlockViz design for visual analytic tasks.

Response of flexible structures — such as cable-supported bridges and aircraft wings — is associated with a number of uncertainties in structural and flow parameters. This thesis is aimed at efficient uncertainty quantification in a few such flow and flow-induced structural response problems. First, the uncertainty quantification in the lift force exerted on a submerged body in a potential flow is considered. To this end, a new method — termed here as semi-intrusive stochastic perturbation (SISP) — is proposed. A sensitivity analysis is also performed, where for the global sensitivity analysis (GSA) the Sobol’ indices are used. The polynomial chaos expansion (PCE) is used for estimating these indices. Next, two stability problems —divergence and flutter — in the aeroelasticity are studied in the context of reliability based design optimization (RBDO). Two modifications are proposed to an existing PCE-based metamodel to reduce the computational cost, where the chaos coefficients are estimated using Gauss quadrature to gain computational speed and GSA is used to create nonuniform grid to reduce the cost even further. The proposed method is applied on a rectangular unswept cantilever wing model. Next, reliability computation in limit cycle oscillations (LCOs) is considered. While the metamodel performs poorly in this case due to bimodality in the distribution, a new simulation-based scheme proposed to this end. Accordingly, first a reduced-order model (ROM) is used to identify the critical region in the random parameter space. Then the full-scale expensive model is run only over a this critical region. This is applied to the rectangular unswept cantilever wing with cubic and fifth order stiffness terms in its equation of motion. Next, the wind speed is modeled as a spatio-temporal process, and accordingly new representations of spatio-temporal random processes are proposed based on tensor decompositions of the covariance kernel. These are applied to three problems: a heat equation, a vibration, and a readily available covariance model for wind speed. Finally, to assimilate available field measurement data on wind speed and to predict based on this assimilation, a new framework based on the tensor decompositions is proposed. The framework is successfully applied to a set of measured data on wind speed in Ireland, where the prediction based on simulation is found to be consistent with the observed data.

This work treats the representation of moving objects and operations over these objects. Intro­duces the support for spatio-temporal data in Oracle Database 10g and presents two designs of moving objects database structure. Upon these designs a database was implemented using the user-defined data types. Sample application provides a graphical output of stored spatial data and allows us to call an implemented spatio-temporal operations. Finally, an evaluation of achieved results is done and possible extensions of project are discussed.

In this work, we present a workflow for semi-automatic extraction of geo-references and motion events from the book 'The Travels of Marco Polo'. These are then used to create 3D renderings of the space and movement which allows readers to visually trace Marco Polo's route themselves to provide the exprience of the entirety of the journey

Anwender aus verschiedensten Fachgebieten stellen weitreichende Anforderungen an die Modellierung der Zeitdimension in den Geoinformationssystemen. Von Interesse ist dabei oft speziell eine Analyse der Dynamik der betrachteten Phänomene. Das Erfordernis, geeignete Methoden zur Erfassung und Verarbeitung von Zeitinformationen bereitzustellen, stand in den letzten Jahrzehnten im Fokus diverser Untersuchungen und Publikationen. Im Kontext dieser Entwicklung ist im Jahr 2002 auch eine ISO-Norm (ISO19108:2002, Zeitliche Schema) zu dieser Thematik erschienen. Die Möglichkeiten, die hinsichtlich dessen von aktuellen Geoinformationssystemen angeboten werden, bleiben im Allgemeinen hinter der genannten Norm zurück. In dieser Arbeit wird untersucht, inwiefern das Konzept des Zeitlichen Schemas der ISO durch einen Abstrakten Datentyp in einer Geodatenbank umgesetzt werden kann. Es erfolgt die Konzeption hierfür notwendiger Objekte und Funktionen. Ziel ist dabei die integrierte Erfassung, Verwaltung und Analyse von Zeitpunkten, -perioden sowie einfacher zeitlicher Komplexe. Weiter wird für den Informationsaustausch zwischen Nutzer und Datenbanksystem, aufbauend auf bestehenden Standards, eine textuelle Repräsentation der Objekte konzipiert. Die Umsetzbarkeit des Konzepts wurde durch eine prototypische Implementierung, in einer PostgreSQL-Datenbank mit PostGIS-Erweiterung, untersucht. Die prinzipielle Machbarkeit konnte nachgewiesen werden, Details werden in der Arbeit behandelt. / Users from various fields of expertise place a wide range of demands on the subject of time modelling in geographic information systems. Of particular interest in this area is often the analysis of the dynamics of spatial features. The demand of providing suitable methods for the collection and processing of time data has been the focus of diverse research projects and publications over the last decades. In the context of the previously mentioned development, an ISO standard (ISO19108:2002, Temporal Schema) regarding this issue was published in 2002. In general, the capabilities of today’s geographic information systems are far behind the theoretical potential provided by this ISO standard. This bachelor thesis examines the feasibility of how the Temporal Schema of the ISO standard can be implemented in a Geodatabase with an abstract data type. Therefore necessary objects and functions are conceptualized. The aim is the integrated capture, management and analysis of instants, periods of time and simple temporal complexes. Furthermore, according to existing standards, a textual representation of objects is designed for the exchange of information between users and database systems. The practicability of the concept is examined with an implementation prototype in a PostgreSQL database, using the PostGIS extension. In summary, it can be said that the theoretical feasibility of this approach is proven, the details are discussed in this thesis.