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For a given set of spatial locations (e.g., criminal activities, disease outbreak, etc.), circular hotspot detection (CHD) identifies circular zones (or hotspots) of significantly high concentration of activity points in the given space. Furthermore, the prevalence of activities inside these zones is considerably higher than outside them. CHD is essential for numerous societal applications, including criminology, epidemiology, etc. The existing methods for CHD consider one of the given activity points as the center of the circular zone (or hotspot). Hence, these methods are incapable of ignoring small gaps (like mountains, rivers, etc.) in the spatial contiguity of hotspots and are not geographically robust. Geographically robust CHD (GR-CHD) requires enumeration of those candidate circles where the hotspot center is not necessarily an activity point. Unfortunately, this enumeration unreasonably increases the enumerated circles and makes GR-CHD a computationally expensive problem. Therefore, in this paper, GR-CHD is modeled as a single-objective optimization problem. Further, three swarm intelligence (SI) algorithms, namely particle swarm optimization, grey wolf optimizer, and salp swarm algorithm, are applied to the proposed model to detect geographically robust hotspots. Finally, the performances of the presented SI-based schemes and state-of-the-art cubic grid circle (CGC) algorithm have been compared. They are evaluated over the controlled synthetic datasets and the crime dataset of Chicago city for the year 2019. The obtained results indicate that the computational times required by the SI-based schemes are significantly less than the CGC algorithm. Also, the quality of hotspots detected by SI-based algorithms is either improved or at par with the CGC algorithm. [ABSTRACT FROM AUTHOR]

Accurate demand prediction of bike-sharing is an important prerequisite to reducing the cost of scheduling and improving the user satisfaction. However, it is a challenging issue due to stochasticity and non-linearity in bike-sharing systems. In this paper, a model called pseudo-double hidden layer feedforward neural networks is proposed to approximately predict actual demands of bike-sharing. Specifically, to overcome limitations in traditional back-propagation learning process, an algorithm, an extreme learning machine with improved particle swarm optimization, is designed to construct learning rules in neural networks. The performance is verified by comparing with other learning algorithms on the dataset of Streeter Dr bike-sharing station in Chicago. [ABSTRACT FROM AUTHOR]

Given a geometric space and a set of weighted spatial points, the Size Constrained k Simple Polygons (SCkSP) problem identifies k simple polygons that maximize the total weights of the spatial points covered by the polygons and meet the polygon size constraint. The SCkSP problem is important for many societal applications including hotspot area detection and resource allocation. The problem is NP-hard; it is computationally challenging because of the large number of spatial points and the polygon size constraint. Our preliminary work introduced the Nearest Neighbor Triangulation and Merging (NNTM) algorithm for SCkSP to meet the size constraint while maximizing the total weights of the spatial points. However, we find that the performance of the NNTM algorithm is dependent on the t-nearest graph. In this paper, we extend our previous work and propose a novel approach that outperforms our prior work. Experiments using Chicago crime and U.S. Federal wildfire datasets demonstrate that the proposed algorithm significantly reduces the computational cost of our prior work and produces a better solution. [ABSTRACT FROM AUTHOR]

The design of collaborative robotics, such as driver-assisted operations, engineer a potential automation of decision-making predicated on unobtrusive data gathering of human users. This form of 'somatic surveillance' (Hayles, Unthought: the power of the cognitive nonconscious. University of Chicago Press, Chicago, 2017, p. 11) increasingly relies on behavioural biometrics and sensory algorithms to verify the physiology of bodies in cabin interiors. Such processes secure cyber-physical space, but also register user capabilities for control that yield data as insured risk. In this technical re-formation of human–machine interactions for control and communication 'a dissonance of attribution' (Hancock et al., Proc Natl Acad Sci 116(16):7684, 2019. https://doi.org/10.1073/pnas.1805770115) is created between perceptions of phenomena, materials and decision-making. This reconfigures relations not only between humans and machines, objects and subjects, but possibly disrupts attributive functions in the social system of Law. What it requires is shifting a legal accountability for action from a sovereignty of the human to a new materialist account based on a 'cognitive assemblage' between physiological data, computation and algorithmic sensing. This paper investigates the function of law as a guidance system to acknowledge this account of sensory and algorithmic computation as autonomous 'sensing agents' (Hansen, Feed-forward: on the future of twenty-first-century media. University of Chicago Press, Chicago, 2015) that may be accountable in situations of risk. This assemblage of robotic computation and sensory determination requires a clearer legal differentiation across the current static terminologies of person, property, liability and rights that maintain strict separations of object from subject. To neglect this, we argue, law will solely impute attributions of error to humans despite evidence of operation via mutual control. [ABSTRACT FROM AUTHOR]

Answering statistical queries about streams of online arriving data is becoming increasingly important. Often, such data includes multiple-attributes, so data elements can be viewed as points in a multi-dimensional universe. This paper extends existing works on streaming algorithms by studying the ability to perform box queries on online multi-dimensional data streams. We develop three algorithms C-DARQ , DARQ and MARQ that support such capabilities for a large number of statistical functions including (but not limited to) counting, frequency estimation, heavy-hitters etc. We also apply our algorithms in distributed settings, in which measurements are recorded independently by multiple sites (e.g., multiple routers), and the goal is to obtain a global network analysis. The protocols are analyzed and evaluated over synthetic dataset, Chicago dataset, and a Facebook dataset from Kaggle in multiple dimensions (up to 10). Our algorithms asymptotically improve the space bounds as well as update and query performance of existing works. Unlike known approaches, our algorithms can also be used to solve a larger class of problems beyond counting. We further discuss extending our work to the sliding window model and when the dimensions' bounds are a-priori unknown. [ABSTRACT FROM AUTHOR]

We investigate the scheduling of garbage trucks in the city of Chicago. Analysis of data collected from the system shows that city blocks differ in the rate at which garbage is collected. However, in the current system, each truck visits the dumpsite two times each day. Our approach is to devise a flexible routing scheme in which some routes visit the dumpsite only once per day, while others visit the dumpsite twice per day depending on the blocks assigned to the route. We use a Markov decision process to model the impact on capacity of using flexible routes. This provides a dynamic scheduling algorithm that adjusts the number of dumpsite visits throughout the week to maximize service level. Results of the model suggest a potential reduction in truck capacity of 12-16% for a set of five pilot wards. This paper shows that flexible schedules can significantly reduce the capacity required to operate a system in the presence of variability. [ABSTRACT FROM AUTHOR]

The central purpose of this study is to further evaluate the quality of the performance of a new algorithm. The study provides additional evidence on this algorithm that was designed to increase the overall efficiency of the original k-means clustering technique--the Fast, Efficient, and Scalable k-means algorithm (FES-k-means). The FES-k-means algorithm uses a hybrid approach that comprises the k-d tree data structure that enhances the nearest neighbor query, the original k-means algorithm, and an adaptation rate proposed by Mashor. This algorithm was tested using two real datasets and one synthetic dataset. It was employed twice on all three datasets: once on data trained by the innovative MIL-SOM method and then on the actual untrained data in order to evaluate its competence. This two-step approach of data training prior to clustering provides a solid foundation for knowledge discovery and data mining, otherwise unclaimed by clustering methods alone. The benefits of this method are that it produces clusters similar to the original k-means method at a much faster rate as shown by runtime comparison data; and it provides efficient analysis of large geospatial data with implications for disease mechanism discovery. From a disease mechanism discovery perspective, it is hypothesized that the linear-like pattern of elevated blood lead levels discovered in the city of Chicago may be spatially linked to the city's water service lines. [ABSTRACT FROM AUTHOR]