Search Results (14)

In 21st-century American cities, urban crime remains a critical public safety concern influenced by complex social, political, and environmental structures. Crime is not randomly distributed and built-environment characteristics, such as street network configuration, impact criminal activity through spatial dependence effects at multiple scales. This study investigates the cross-sectional, multi-scale spatial effects of street network configuration on property crime across neighborhoods in Charlotte, North Carolina. Specifically, we examine whether the fundamental characteristics of a neighborhood’s street network contribute to variations in its property crime. Using a novel and granular spatial approach, incorporating spatial econometric models (SAR, CAR, and GWR), several street network characteristics, including density, connectivity, and centrality, within five nested buffer bands are measured to capture both local and non-local influences. The results provide strong and consistent evidence that certain characteristics of the neighborhood street network, such as connectivity and accessibility, significantly influence the occurrence of property crime. Impacts are also found to be spatially heterogenous, manifesting themselves at the mid-range scale rather than hyper-locally. The integration of comprehensive measures of street network configuration into spatially explicit models offers new opportunities for advancement in environmental criminology literature. Such spatial dynamics further contribute to urban safety policy by informing decision-makers so that they can ensure a defensively built environment design. Full article

Despite the increasing time sensitivity of climate change, many cities worldwide still heavily rely on coal. The extraction, processing, transport, and usage of coal lead to deteriorated air quality, resulting in complex environmental and public health problems for the local communities. Mapping different pollution sources in coal-centric cities is not trivial due to the hyperlocal nature of air pollution and the often low-density network of air quality monitors. This study explores the air quality issues surrounding coal-centric cities using a combination of qualitative and quantitative data from reference-grade air quality monitors, low-cost sensors (LCSs) deployed on citizens’ vehicles, and community engagement activities. It explores how LCSs can be used to characterize air quality at a high spatio-temporal resolution and how this information can be used to decode people’s perceptions of air quality issues and elicit local knowledge. We evaluated our approach in Sparwood (Canada), and Oskemen (Kazakhstan) which are very different cities, but are both heavily dependent on coal. LCSs have been proven an efficient tool to identify pollution hotspots that traditional reference monitors miss, while workshop-based activities making use of data maps and coding tools have successfully elicited information about pollution sources from non-experts, helping collaborative sense-making and informing new LCS deployment strategies. Understanding air quality in coal-centric cities as a complex socio-technical phenomenon can enable the coal industry, city officials, and residents to engage in addressing air quality issues. Full article

Traffic congestion is a contemporary urban issue plaguing transportation planners, land developers, policy-makers, and citizens. While many studies have investigated the impact of built environments on traffic behavior in large metropolises on a regional scale, little attention has been paid to smaller urban areas, in China’s context, especially on a neighborhood level. This study investigates the spatial–temporal pattern of traffic congestion in a small-scale city, Xining, in China. By applying multivariate least-square regression analysis to social-sensing hyperlocal travel data, the results indicate that Xining is experiencing morning and evening traffic peaks on the weekdays and pre-weekends and only the evening peak during the weekends or holidays. The pre-weekend congestion is significantly worse than on a normal weekday, implying that stronger measures to consolidate traffic management should be implemented during this time. Educational land use and residential areas were found to contribute significantly to traffic congestion in Xining, and their combined effects tend to exacerbate the situation. The study furthers the understanding of traffic congestion in small urban areas, providing urban planners and policy-makers with new insights to formulate evidence-based strategies for mitigating traffic congestion. Full article

Here, we examine the extension of smart retailing from the indoor confines of stores, outward to high streets. We explore how several technologies at the union of retail intelligence and smart city monitoring could coalesce into retail high streets that are both smart and sentient. We examine the new vantages that smart and sentient retail high streets provide on the customer journey, and how they could transform retailers’ sway over customer experience with new reach to the public spaces around shops. In doing so, we pursue a three-way consideration of these issues, examining the technology that underpins smart retailing, new advances in artificial intelligence and machine learning that beget a level of street-side sentience, and opportunities for retailers to map the knowledge that those technologies provide to individual customer journeys in outdoor settings. Our exploration of these issues takes form as a review of the literature and the introduction of our own research to prototype smart and sentient retail systems for high streets. The topic of enhancing retailers’ acuity on high streets has significant currency, as many high street stores have recently been struggling to sustain custom. However, the production and application of smart and sentient technologies at hyper-local resolution of the streetscape conjures some sobering considerations about shoppers’ and pedestrians’ rights to privacy in public. Full article

Bottom-up initiatives of active citizens are increasingly demonstrating sustainable practices within local ecosystems. Local urban farming, sustainable agri-food systems, circular supply chains, and community fablabs are exemplary ways of tackling global challenges on a local level. Although promising in accelerating towards future-proof systems, these hyper-localized, bottom-up initiatives often struggle to take root in new contexts due to embedded socio-cultural challenges. With the premise that transformative capacity can be co-created to overcome such scaling challenges, the current work addresses the identified gap in scaling bottom-up initiatives into locally embedded ecosystems. While how to diffuse such practices across contexts is not straightforward, we introduce a three-phased approach enabling knowledge exchange and easing collaboration across cultures and ecosystems. The results allowed us to define common scalability criteria and to unfold scaling as a multi-step learning process to bridge identified cognitive and context gaps. The current article contributes to a broader activation of impact-driven scaling strategies and value creation processes that are transferable across contexts and deemed relevant for local ecosystems that are willing to co-create resilient socio-economic systems. Full article

The normalized difference vegetation index (NDVI) is a key parameter in precision agriculture. It has been used globally since the 1970s as a proxy to monitor crop growth and correlates to the crop coefficient (Kc), leaf area index (LAI), crop cover, and more. Yet, it is susceptible to clouds and other atmospheric conditions that might alter the crop’s real NDVI value. Synthetic Aperture Radar (SAR), on the other hand, can penetrate clouds and is hardly affected by atmospheric conditions, but it is sensitive to the physical structure of the crop and therefore does not give a direct indication of the NDVI. Several SAR indices and methods have been suggested to estimate NDVIs via SAR; however, they tend to work for local spatial and temporal conditions and do not work well globally. This is because they are not flexible enough to capture the changing NDVI–SAR relationship throughout the crop-growing season. This study suggests a new method for converting Sentinel-1 to NDVIs for Agricultural Fields (SNAF) by utilizing a hyperlocal machine learning approach. This method generates multiple on-the-fly disposal field- and time-specific models for every available Sentinel-1 image across 2021. Each model learns the field-specific NDVI (from Sentinel-2 and Landsat-8) –SAR (Sentinel-1) relationship based on recent NDVI and SAR time series and consequently estimates the optimal NDVI value from the current SAR image. The SNAF was tested on 548 commercial fields from 18 countries with 28 crop types and, based on 6880 paired NDVI–SAR images, achieved an RMSE, bias, and R² of 0.06, 0.00, and 0.92, respectively. The outcome of this study aspires to a persistent seamless stream of NDVI values, regardless of the atmospheric conditions, illumination, or local conditions, which can assist in agricultural decision making. Full article

In this paper, a finite control set model-free predictive current control (FCS-MFPCC) of a permanent magnet synchronous motor is presented. The control scheme addresses the problems of large current fluctuation and decline of the motor system performance during parameter perturbation for the traditional finite control set model predictive current control (FCS-MPCC). Firstly, the mathematical model of the motor is analyzed and derived during parameter perturbation, and a new hyperlocal model of the motor is established based on this mathematical model. Secondly, a finite control set model-free predictive current controller is designed based on the new hyperlocal model, and a current error correction factor is introduced to correct the prediction error. Meanwhile, the stability of the observer is demonstrated via the Lyapunov theory. The simulation results show that the proposed control strategy reduces current fluctuation compared with the FCS-MPCC strategy, and the system is robust during parameter perturbation. Full article

The rapid urbanization process brings consequences to urban environments, such poor air quality and the urban heat island issues. Due to these effects, environmental monitoring is gaining attention with the aim of identifying local risks and improving cities’ liveability and resilience. However, these environments are very heterogeneous, and high-spatial-resolution data are needed to identify the intra-urban variations of physical parameters. Recently, wearable sensing techniques have been used to perform microscale monitoring, but they usually focus on one environmental physics domain. This paper presents a new wearable system developed to monitor key multidomain parameters related to the air quality, thermal, and visual domains, on a hyperlocal scale from a pedestrian’s perspective. The system consisted of a set of sensors connected to a control unit settled on a backpack and could be connected via Wi-Fi to any portable equipment. The device was prototyped to guarantee the easy sensors maintenance, and a user-friendly dashboard facilitated a real-time monitoring overview. Several tests were conducted to confirm the reliability of the sensors. The new device will allow comprehensive environmental monitoring and multidomain comfort investigations to be carried out, which can support urban planners to face the negative effects of urbanization and to crowd data sourcing in smart cities. Full article

Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality. Full article

The article considers the task and a new approach to monitoring of soil and groundwater contamination by harmful emissions of nitrogen dioxide from motor vehicles. The approach is based on combination of measurement procedure of the harmful emissions concentrations in the surface atmospheric layer and mathematical modeling of the impact of these emissions on soil and groundwater contamination. The scheme of this harmful emission concentrations measurement using a mobile complex Sniffer4D Hyper-local Air Quality Analyzer is given. Based on these results, a mathematical model of diffusion of this harmful substance in the upper layers of soil and water resources is proposed. As a result of the computations, the distribution of concentrations of soil and water contamination in the neighborhood of separate observation points has been built. Also, the field of nitrogen dioxide concentrations at various depths for observation points has been modeled. For a waterbody, the concentration of nitric acid, which is formed due to the interaction of nitrogen dioxide with water, is higher in water than in the surface layer of the soil. This concentration can be significantly increased during prolonged downpours when acidic solutions drain from surrounding soil areas into the waterbodies. The obtained research results are fully consistent with the practical and theoretical results on the diffusion of gases into soils and water resources. It means that the proposed approach can be used instead of the existing very expensive approach to analysis of soil and groundwater contamination in the laboratory. Full article

Building robust age–depth models to understand climatic and geologic histories from coastal sedimentary archives often requires composite chronologies consisting of multi-proxy age markers. Pollen chronohorizons derived from a known change in vegetation are important for age–depth models, especially those with other sparse or imprecise age markers. However, the accuracy of pollen chronohorizons compared to other age markers and the impact of pollen chronohorizons on the precision of age–depth models, particularly in salt marsh environments, is poorly understood. Here, we combine new and published pollen data from eight coastal wetlands (salt marshes and mangroves) along the Atlantic Coast of the United States (U.S.) from Florida to Connecticut to define the age and uncertainty of 17 pollen chronohorizons. We found that 13 out of 17 pollen chronohorizons were consistent when compared to other age markers (radiocarbon, radionuclide ¹³⁷Cs and pollution markers). Inconsistencies were likely related to the hyperlocality of pollen chronohorizons, mixing of salt marsh sediment, reworking of pollen from nearby tidal flats, misidentification of pollen signals, and inaccuracies in or misinterpretation of other age markers. Additionally, in a total of 24 models, including one or more pollen chronohorizons, increased precision (up to 41 years) or no change was found in 18 models. Full article

This paper introduces a novel protocol for managing low altitude 3D aeronautical chart data to address the unique navigational challenges and collision risks associated with populated urban environments. Based on the Open Geospatial Consortium (OGC) 3D Tiles standard for geospatial data delivery, the proposed extension, called 3D Tiles Nav., uses a navigation-centric packet structure which automatically decomposes the navigable regions of space into hyperlocal navigation cells and encodes environmental surfaces that are potentially visible from each cell. The developed method is sensor agnostic and provides the ability to quickly and conservatively encode visibility directly from a region by enabling an expanded approach to viewshed analysis. In this approach, the navigation cells themselves are used to represent the intrinsic positional uncertainty often needed for navigation. Furthermore, we present in detail this new data format and its unique features as well as a candidate framework illustrating how an Unmanned Traffic Management (UTM) system could support trajectory-based operations and performance-based navigation in the urban canyon. Our results, experiments, and simulations conclude that this data reorganization enables 3D map streaming using less bandwidth and efficient 3D map-matching systems with limited on-board compute, storage, and sensor resources. Full article

Universities have become more engaged or entrepreneurial, forging deeper relations with society beyond the economic sphere. To foster, structure, and institutionalize a broader spectrum of engagement, new types of intermediary organizations are created, going beyond the “standard” technology transfer offices, incubators, and science parks. This paper conceptualizes the role of such new-style intermediaries as facilitator, enabler, and co-shaper of university–society interaction, making a distinction between the roles of facilitation, configuration, and brokering. As a case study, the paper presents the Knowledge Mile in Amsterdam as a novel form of hyper-local engagement of a university with its urban surroundings that connects the challenges of companies and organisations in the street to a broad range of educational and research activities of the university, as well as to rebrand the street. Full article

This article focuses on user experiences on reading location-aware news on the mobile platform and aims to explore what experiences this kind of locative journalism generates and how such experiences change the users’ social interaction with news. We produced a specially designed mobile application and tailored news stories specific to this project called LocaNews in order to explore participants’ relation to the content in this journalistic format. The result is generated through a field study and a questionnaire of 32 people to find out how they experience the news presented in this format. The user participants’ responses are analyzed based on their news experiences, contextualizing places and their social interaction with the news within this form of journalism. Results showed that the local, semi-local and non-local user approaches the locative news in a different manner, but that the average user found this kind of news more interesting and more informative than ordinary news. The participants also have a problem identifying this as journalism, rather than an information service. Full article