Fast Automatic Vehicle Annotation for Urban Traffic Surveillance

Automatic vehicle detection and annotation for streaming video data with complex scenes is an interesting but challenging task for intelligent transportation systems. In this paper, we present a fast algorithm: detection and annotation for vehicles (DAVE), which effectively combines vehicle detection and attributes annotation into a unified framework. DAVE consists of two convolutional neural networks: a shallow fully convolutional fast vehicle proposal network (FVPN) for extracting all vehicles' positions, and a deep attributes learning network (ALN), which aims to verify each detection candidate and infer each vehicle's pose, color, and type information simultaneously. These two nets are jointly optimized so that abundant latent knowledge learned from the deep empirical ALN can be exploited to guide training the much simpler FVPN. Once the system is trained, DAVE can achieve efficient vehicle detection and attributes annotation for real-world traffic surveillance data, while the FVPN can be independently adopted as a real-time high-performance vehicle detector as well. We evaluate the DAVE on a new self-collected urban traffic surveillance data set and the public PASCAL VOC2007 car and LISA 2010 data sets, with consistent improvements over existing algorithms

Amenity complexity and urban locations of socio-economic mixing

Cities host diverse people and their mixing is the engine of prosperity. In turn, segregation and inequalities are common features of most cities and locations that enable the meeting of people with different socio-economic status are key for urban inclusion. In this study, we adopt the concept of economic complexity to quantify the ability of locations -- on the level of neighborhoods and amenities -- to attract diverse visitors from various socio-economic backgrounds across the city. We construct the measures of neighborhood complexity and amenity complexity based on the local portfolio of diverse and non-ubiquitous amenities in Budapest, Hungary. Socio-economic mixing at visited third places is investigated by tracing the daily mobility of individuals and by characterizing their status by the real-estate price of their home locations. Results suggest that measures of ubiquity and diversity of amenities do not, but neighborhood complexity and amenity complexity are correlated with the urban centrality of locations. Urban centrality is a strong predictor of socio-economic mixing, but both neighborhood complexity and amenity complexity add further explanatory power to our models. Our work combines urban mobility data with economic complexity thinking to show that the diversity of non-ubiquitous amenities, central locations, and the potentials for socio-economic mixing are interrelated

Geographic Data Mining and Knowledge Discovery

Geographic data are information associated with a location on the surface of the Earth. They comprise spatial attributes (latitude, longitude, and altitude) and non-spatial attributes (facts related to a location). Traditionally, Physical Geography datasets were considered to be more valuable, thus attracted most research interest. But with the advancements in remote sensing technologies and widespread use of GPS enabled cellphones and IoT (Internet of Things) devices, recent years witnessed explosive growth in the amount of available Human Geography datasets. However, methods and tools that are capable of analyzing and modeling these datasets are very limited. This is because Human Geography data are inherently difficult to model due to its characteristics (non-stationarity, uneven distribution, etc.). Many algorithms were invented to solve these challenges -- especially non-stationarity -- in the past few years, like Geographically Weighted Regression, Multiscale GWR, Geographical Random Forest, etc. They were proven to be much more efficient than the general machine learning algorithms that are not specifically designed to deal with non-stationarity. However, such algorithms are far from perfect and have a lot of room for improvement. This dissertation proposed multiple algorithms for modeling non-stationary geographic data. The main contributions are: (1) designed a novel method to evaluate non-stationarity and its impact on regression models; (2) proposed the Geographic R-Partition tree for modeling non-stationary data; (3) proposed the IDW-RF algorithm, which uses the advantages of Random Forests to deal with extremely unevenly distributed geographic datasets; (4) proposed the LVRF algorithm, which models geographic data using a latent variable based method. Experiments show that these algorithms are very efficient and outperform other state-of-the-art algorithms in certain scenarios

Searching for innovation in market and transition economies: evidence across Europe

Searching for externally available knowledge has been characterized as a vital part of the innovation process. The availability of such innovation impulses, however, critically depends on the environment a firm is operating in. Little is known on how institutional infrastructures for innovation differ with respect to the munificence in providing innovation impulses. In this paper, we suggest that these differences are particularly pronounced between established market economies and transition economies. We argue that these differences shape a firm's search pattern and that the search pattern subsequently moderates the relationship between innovation inputs and outputs. Based on a sample of more than 4,500 firms from ten European countries we find that search strategies differ considerably between established market and transition economies. Search in transition economies is characterized by much more variety. However, management capacity in these countries is a particularly scarce resource which is why focused search strategies turn out to be most successful. --Search strategies,open innovation,transition economies,institutional infrastructure for innovation

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Sparse Model Selection using Information Complexity

This dissertation studies and uses the application of information complexity to statistical model selection through three different projects. Specifically, we design statistical models that incorporate sparsity features to make the models more explanatory and computationally efficient. In the first project, we propose a Sparse Bridge Regression model for variable selection when the number of variables is much greater than the number of observations if model misspecification occurs. The model is demonstrated to have excellent explanatory power in high-dimensional data analysis through numerical simulations and real-world data analysis. The second project proposes a novel hybrid modeling method that utilizes a mixture of sparse principal component regression (MIX-SPCR) to segment high-dimensional time series data. Using the MIX-SPCR model, we empirically analyze the S\&P 500 index data (from 1999 to 2019) and identify two key change points. The third project investigates the use of nonlinear features in the Sparse Kernel Factor Analysis (SKFA) method to derive the information criterion. Using a variety of wide datasets, we demonstrate the benefits of SKFA in the nonlinear representation and classification of data. The results obtained show the flexibility and the utility of information complexity in such data modeling problems

Spatio-temporal mixed membership models for criminal activity

Abstract We suggest a probabilistic approach to study crime data in London and highlight the benefits of defining a statistical joint crime distribution model which provides insights into urban criminal activity. This is achieved by developing a hierarchical mixture model for observations, crime occurrences over a geographical study area, that are grouped according to multiple time stamps and crime categories. The mixture components correspond to spatial crime distributions over the study area and the goal is to infer, based on the observations, how and to what degree the latent distributions are shared across the groups.</jats:p

Improved targeted outdoor advertising based on geotagged social media data

With as many as 4 million passenger journeys within the London Underground system every weekday, the advertisement spaces across the stations hold considerable potential. However, the planning of specific advertisements across time and space is difficult to optimize as little is known about passers-by. Therefore, in order to generate detailed and quantifiable spatio-temporal information which is particular to each station area, we have explored local social media data. This research demonstrates how local interests can be mined from geotagged Tweets by using Latent Dirichlet Allocation, an unsupervised topic modelling method. The relative popularity of each of the key topics is then explored spatially and temporally between the station areas. Overall, this research demonstrates the value of using Geographical Information System and text-mining techniques to generate valuable spatio-temporal information on popular interests from Twitter data