We revisit the longstanding question of how physical structures in urban landscapes influence crime. Leveraging machine learning-based matching techniques to control for demographic composition, we estimate the effects of several types of urban structures on the incidence of violent crime in New York City and Chicago. We additionally contribute to a growing body of literature documenting the relationship between perception of crime and actual crime rates by separately analyzing how the physical urban landscape shapes subjective feelings of safety. Our results are twofold. First, in consensus with prior work, we demonstrate a "broken windows" effect in which abandoned buildings, a sign of social disorder, are associated with both greater incidence of crime and a heightened perception of danger. This is also true of types of urban structures that draw foot traffic such as public transportation infrastructure. Second, these effects are not uniform within or across cities. The criminogenic effects of the same structure types across two cities differ in magnitude, degree of spatial localization, and heterogeneity across subgroups, while within the same city, the effects of different structure types are confounded by different demographic variables. Taken together, these results emphasize that one-size-fits-all approaches to crime reduction are untenable and policy interventions must be specifically tailored to their targets.

Robot vacuums have become a staple in many homes, offering a convenient way to keep floors clean with minimal effort. But getting the most out of your robot vacuum isn't as simple as pressing "start" and walking away. A little strategy is needed to enhance its performance, extend its lifespan, and ensure your floors are as clean as possible. Whether you're a new owner or a seasoned user, these practical tips will help you maximize your robot vacuum's effectiveness and make it a trusted household helper. It might seem like a small detail, but the location of your robot vacuum's charging dock can make a huge difference in terms of battery life and cleaning efficiency.

The edge computing market is expected to grow from $40.84 million in 2022 to $132.11 million by 2028. This is a compound annual growth rate of 21.8% percent. The use cases for the edge are limitless. Use cases can range from remote field offices operating drone fleets for utility and mining operations to employees working from home and automated manufacturing assembly lines. As this movement to edge computing has unfolded, more non-IT professionals are being asked to manage the technology that is located at the edges that they occupy.

One of the biggest challenges to deploying and scaling artificial intelligence is ensuring the data being used to train AI algorithms is accurate and timely. Using bad data hampers an organization's ability to predict future trends and make meaningful business decisions. Only when high-quality datasets drive AI algorithms can businesses gain valuable information and insight that can help them make critical decisions that could improve customer experience, brand loyalty, supply chain flow and more. At its core, an accurate dataset provides a representative sample of the population. Given that 97% of the population owns some type of mobile device and 85% have smartphones, location data is well-positioned to provide an accurate accounting of the activities of a given population, especially when used to complement other data sources.

On the side of a dusty Jakarta road, nestled in the corner of a gas station, might be the future of coffee. Driving home, you might preorder a cup as you plan to refill the car, even before pulling up to the gas station. Open the Kopi Kenangan app, click on "preorder," and choose one -- perhaps a mellow coffee with just a hint of acidity, creamy with milk. By the time you arrive at the station, the iced coffee, made by a human, will be sitting on the countertop, sweating in a plastic cup in the Jakarta humidity. Indonesian coffee chain Kopi Kenangan -- "coffee memories" in Bahasa Indonesia -- did not set out to be a tech-powered coffee chain.

Placer.ai, a location analytics platform that serves companies with data around consumer foot traffic, has raised $100 million in a series C round of funding, valuing the company at $1 billion. The location intelligence industry was pegged as a $12 billion market last year, a figure that's predicted to more than double in the coming years as businesses leverage big data insights to improve their bottom line. For example, businesses can glean accurate foot traffic counts and "dwell time," allowing them to filter by time and day as well as customer segments. This can be useful for understanding how special promotions, events, or holidays impact trade. Or they can discover what Placer.ai

Advances in cloud computing, AI and sensors are combining to offer insurers new, better variables to characterize occupancy risk in buildings. Commercial general liability insurers traditionally estimate business risk exposure of similar businesses based on variables like floor area and revenue. Advances in cloud computing and artificial intelligence are combining to offer insurers new, better variables to characterize risk. Insurers generally understand that liability risk correlates to human presence and movement. A hair salon with twice the foot traffic should present twice the slip-and-fall risk.

Agreed, the headline is slightly presumptuous, in light of the third wave looming large and governments' globally gearing up for the same. That said, with vaccination drives going on in full swing, mask-mandates being lifted, travels resuming, and offices reopening, people are actually heaving a sigh of relief – a virus of good feeling is rippling across the cities. But, please don't take my word for it! America's leading retail brands have gone on to report that their foot traffic has rebounded earlier than expected, so much so that the numbers might exceed their 2019 sales performance. The point is consumer behavior is expected to upend big-time post-pandemic, partly because some customers might want to pursue their pre-pandemic routines, and mostly because some customers might wish to continue with new customer engagement models launched during the pandemic.

Everything was going well for innovative entrepreneur and restaurateur Haruki Odajima -- or so it seemed until the coronavirus pandemic swirled across the country earlier this year. His artificial intelligence business, Ebilab, which helps restaurants predict how many customers they will get with more than 95 percent accuracy, was flourishing. That success was driven by his long-established restaurant Ebiya in Ise, Mie Prefecture. He had been using the Japanese restaurant to demonstrate how his system can more than triple productivity, grow profits fivefold and reduce food waste by more than 70 percent. With businesses asked to close and people told to stay in, the restaurant industry has chalked up unprecedented losses as bankruptcies sweep the country.

Using publicly available traffic camera data in New York City, we quantify time-dependent patterns in aggregate pedestrian foot traffic. These patterns exhibit repeatable diurnal behaviors that differ for weekdays and weekends but are broadly consistent across neighborhoods in the borough of Manhattan. Weekday patterns contain a characteristic 3-peak structure with increased foot traffic around 9:00am, 12:00-1:00pm, and 5:00pm aligned with the "9-to-5" work day in which pedestrians are on the street during their morning commute, during lunch hour, and then during their evening commute. Weekend days do not show a peaked structure, but rather increase steadily until sunset. Our study period of June 28, 2017 to September 11, 2017 contains two holidays, the 4th of July and Labor Day, and their foot traffic patterns are quantitatively similar to weekend days despite the fact that they fell on weekdays. Projecting all days in our study period onto the weekday/weekend phase space (by regressing against the average weekday and weekend day) we find that Friday foot traffic can be represented as a mixture of both the 3-peak weekday structure and non-peaked weekend structure. We also show that anomalies in the foot traffic patterns can be used for detection of events and network-level disruptions. Finally, we show that clustering of foot traffic time series generates associations between cameras that are spatially aligned with Manhattan neighborhood boundaries indicating that foot traffic dynamics encode information about neighborhood character.