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The final objective of smart cities is to optimize services and improve the quality of life of their citizens, who can play important roles due to the information they can provide. This information can be used in order to enhance many sectors involved in city activity such as transport, energy or health. Crowd-sourcing initiatives focus their efforts on making cities safer places that are adapted to the population size they host. In this way, citizens are able to report the issues they identify to the relevant body so that they can be fixed and, at the same time, they can provide useful information to other citizens. There are several projects aimed at reporting incidents in a smart city context. In this paper, we propose the use of model-driven engineering by designing a graphical domain-specific language to abstract and improve the incident-reporting process. With the use of a domain-specific language, we can obtain several benefits in our research for users and cities. For instance, we can shorten the time for reporting the events by users and, at the same time, we gain an expressive power compared to other methodologies for incident reporting. In addition, it can be reused and is centered in this specific domain after being studied. Furthermore, we have evaluated the DSL with different users, obtaining a high satisfaction percentage. [ABSTRACT FROM AUTHOR]

Online experimental platforms can be used as an alternative to, or complement, lab-based research. However, when conducting auditory experiments via online methods, the researcher has limited control over the participants' listening environment. We offer a new method to probe one aspect of that environment, headphone use. Headphones not only provide better control of sound presentation but can also "shield" the listener from background noise. Here we present a rapid (< 3 min) headphone screening test based on Huggins Pitch (HP), a perceptual phenomenon that can only be detected when stimuli are presented dichotically. We validate this test using a cohort of "Trusted" online participants who completed the test using both headphones and loudspeakers. The same participants were also used to test an existing headphone test (AP test; Woods et al., 2017, Attention Perception Psychophysics). We demonstrate that compared to the AP test, the HP test has a higher selectivity for headphone users, rendering it as a compelling alternative to existing methods. Overall, the new HP test correctly detects 80% of headphone users and has a false-positive rate of 20%. Moreover, we demonstrate that combining the HP test with an additional test–either the AP test or an alternative based on a beat test (BT)–can lower the false-positive rate to ~ 7%. This should be useful in situations where headphone use is particularly critical (e.g., dichotic or spatial manipulations). Code for implementing the new tests is publicly available in JavaScript and through Gorilla (gorilla.sc). [ABSTRACT FROM AUTHOR]

The history of the Banda Islands is revealed in material and immaterial heritage which can still be narrated, visited and experienced today. Using the technological tools available in the Digital Humanities, this paper proposes a project to create a virtual interactive platform in which documents and stories related to the colonial past can be gathered. Tools like crowd-sourcing and crowd-mapping can be used to establish this archive from the bottom-up, creating a platform allowing both the former colonizer and colonized to reflect on the past. Moreover, it will provide scholars with a source of information to revisit the history of the Banda Islands. This particular history is part of the current public debate in the Netherlands regarding the colonial past, moreover, it is central to the narrative concerning the ongoing conservation efforts to prepare the islands’ heritage to become an UNESCO World Heritage site for Indonesia.

Digital transformation of all spheres of society, changing values, modes of production and consumption caused the emergence of new types of labour relations. Gigonomics is a new social and economic model, which is characterized by spread of new types of non-standard employment based on part-time, remote work and short-term labour contracts. Today gigonomics develops very fast all over the world, as this model is appealing for employers in respect of cutting costs on personnel remuneration, providing safe working conditions and others. Russia announced the entry into the age of digital economy on 28 July 2017, when the program ‘Digital Economy of the Russian Federation’ was adopted, therefore the goal of the research is to identify prospects of developing freelancing and crowdsourcing as principle types of economic relations of gigonamics on Russian labour market. To reach the set goal the author analyzes the current development of digitalization in industrialized countries and in Russia and studies the development of gigonomics, in particular freelancing and crowd-sourcing.

Fingerprint-based indoor localization has become one of the most attractive and promising techniques, and however, one primary concern for this technology to be fully practical is to maintain the fingerprint database to combat harsh indoor environmental dynamics, especially for the large-scale and long-term deployment. In this paper, focusing on room localization, we first analyze the effect of different factors on indoor location accuracy, then propose an integrity check algorithm and a fuzzy map mechanism in response to network changes accurately and timely, and then design an accuracy check algorithm and a regional adaptive periodic update approach to update the fingerprint database effectively when an environment changes. Meanwhile, we design the active static data collecting mechanism and the active pin data collecting mechanism to ensure the accuracy and reliability of fingerprints newly captured and reduce the overhead on mobile devices significantly. The experimental results demonstrate that the proposed solution improves the performance of updating the fingerprint database in real time and robustness and maintains the location accuracy over 95% no matter how the indoor environmental changes; meanwhile, it also reduces energy efficiency imposed on the mobile phones by over 50%.

Abstract Crowd‐sourced observations (CSO) offer great potential for numerical weather prediction (NWP). This paper offers a synthesis of progress, challenges and opportunities in this area based on a special session of the EWGLAM Meeting in 2019, concentrating on high‐resolution limited‐area models (LAMs). Two main application areas of CSO are described: data assimilation and verification. One part of data assimilation developments concentrates on smartphone pressure observations, which represent a large volume of data. However, special care has to be taken about data protection and the quality of observations. In this paper, two examples are presented: the SMAPS experiment from Denmark and the uWx experiment from the United States. Another data assimilation topic is citizen observations with low‐cost weather sensors; here an example from Norway is presented using Netatmo stations. The other application area is the use of CSO for model verification. One novel method developed in the United Kingdom is applying social media data to detect severe weather events. This approach is especially important because one future application area of LAM NWP models is impact‐oriented warnings.

We trained a computer vision algorithm to identify 45 species of snakes from photos and compared its performance to that of humans. Both human and algorithm performance is substantially better than randomly guessing (null probability of guessing correctly given 45 classes = 2.2%). Some species (e.g., Boa constrictor) are routinely identified with ease by both algorithm and humans, whereas other groups of species (e.g., uniform green snakes, blotched brown snakes) are routinely confused. A species complex with largely molecular species delimitation (North American ratsnakes) was the most challenging for computer vision. Humans had an edge at identifying images of poor quality or with visual artifacts. With future improvement, computer vision could play a larger role in snakebite epidemiology, particularly when combined with information about geographic location and input from human experts.

Crowd‐sourced observations (CSO) offer great potential for numerical weather prediction (NWP). This paper offers a synthesis of progress, challenges and opportunities in this area based on a special session of the EWGLAM Meeting in 2019, concentrating on high‐resolution limited‐area models (LAMs). Two main application areas of CSO are described: data assimilation and verification. One part of data assimilation developments concentrates on smartphone pressure observations, which represent a large volume of data. However, special care has to be taken about data protection and the quality of observations. In this paper, two examples are presented: the SMAPS experiment from Denmark and the uWx experiment from the United States. Another data assimilation topic is citizen observations with low‐cost weather sensors; here an example from Norway is presented using Netatmo stations. The other application area is the use of CSO for model verification. One novel method developed in the United Kingdom is applying social media data to detect severe weather events. This approach is especially important because one future application area of LAM NWP models is impact‐oriented warnings. [ABSTRACT FROM AUTHOR]

This paper reports on a study of crowd-sourcing 'study aid' web platforms. Students are sharing completed academic coursework through a growing network of 'study aid' web platforms like CourseHero.com. These websites facilitate the crowd-sourced exchange of coursework, and effectively support plagiarism. However, virtually no data exists concerning the scope or extent of coursework being shared through these platforms. This paper reports on two experiments to monitor the frequency of coursework from a sample university uploaded onto CourseHero.com. Ultimately, both experiments failed to produce a clear or meaningful measurement of coursework upload frequency. The apparently widespread use of these crowd-sourcing 'study aid' websites and the failure of these experiments demonstrates the need for further investigation into how much coursework is being shared through such platforms, how frequently it is shared, and what kind of coursework is being shared. Addressing these issues is an important step into measuring the impact of these wellsprings of academically dishonest behavior. [ABSTRACT FROM AUTHOR]

Discrete, ephemeral natural phenomena with low spatial or temporal predictability are incredibly challenging to study systematically. In ecology, species interactions, which constitute the functional backbone of ecological communities, can be notoriously difficult to characterise especially when taxa are inconspicuous and the interactions of interest (e.g., trophic events) occur infrequently, rapidly, or variably in space and time. Overcoming such issues has historically required significant time and resource investment to collect sufficient data, precluding the answering of many ecological and evolutionary questions. Here, we show the utility of social media for rapidly collecting observations of ephemeral ecological phenomena with low spatial and temporal predictability by using a Facebook group dedicated to collecting predation events involving reptiles and amphibians in sub-Saharan Africa. We collected over 1900 independent feeding observations using Facebook from 2015 to 2019 involving 83 families of predators and 129 families of prey. Feeding events by snakes were particularly well-represented with close to 1,100 feeding observations recorded. Relative to an extensive literature review spanning 226 sources and 138 years, we found that social media has provided snake dietary records faster than ever before in history with prey being identified to a finer taxonomic resolution and showing only modest concordance with the literature due to the number of novel interactions that were detected. Finally, we demonstrate that social media can outperform other citizen science image-based approaches (iNaturalist and Google Images) highlighting the versatility of social media and its ability to function as a citizen science platform.

Recent advances in citizen weather station (CWS) networks, with data accessible via crowd-sourcing, provide relevant climatic information to urban scientists and decision makers. In particular, CWS can provide long-term measurements of urban heat and valuable information on spatio-temporal heterogeneity related to horizontal heat advection. In this study, we make the first compilation of a quasi-climatologic dataset covering six years (2015–2020) of hourly near-surface air temperature measurements obtained via 1560 suitable CWS in a domain covering south-east England and Greater London. We investigated the spatio-temporal distribution of urban heat and the influences of local environments on climate, captured by CWS through the scope of Local Climate Zones (LCZ)—a land-use land-cover classification specifically designed for urban climate studies. We further calculate, for the first time, the amount of advected heat captured by CWS located in Greater London and the wider south east England region. We find that London is on average warmer by about 1.0 ^∘ C–1.5 ^∘ C than the rest of south-east England. Characteristics of the southern coastal climate are also captured in the analysis. We find that on average, urban heat advection (UHA) contributes to 0.22 ± 0.96 ^∘ C of the total urban heat in Greater London. Certain areas, mostly in the centre of London are deprived of urban heat through advection since heat is transferred more to downwind suburban areas. UHA can positively contribute to urban heat by up to 1.57 ^∘ C, on average and negatively by down to −1.21 ^∘ C. Our results also show an important degree of inter- and intra-LCZ variability in UHA, calling for more research in the future. Nevertheless, we already find that UHA can impact green areas and reduce their cooling benefit. Such outcomes show the added value of CWS when considering future urban design.

Abstract Background Influenza causes an estimated 3000 to 50,000 deaths per year in the United States of America (US). Timely and representative data can help local, state, and national public health officials monitor and respond to outbreaks of seasonal influenza. Data from cloud-based electronic health records (EHR) and crowd-sourced influenza surveillance systems have the potential to provide complementary, near real-time estimates of influenza activity. The objectives of this paper are to compare two novel influenza-tracking systems with three traditional healthcare-based influenza surveillance systems at four spatial resolutions: national, regional, state, and city, and to determine the minimum number of participants in these systems required to produce influenza activity estimates that resemble the historical trends recorded by traditional surveillance systems. Methods We compared influenza activity estimates from five influenza surveillance systems: 1) patient visits for influenza-like illness (ILI) from the US Outpatient ILI Surveillance Network (ILINet), 2) virologic data from World Health Organization (WHO) Collaborating and National Respiratory and Enteric Virus Surveillance System (NREVSS) Laboratories, 3) Emergency Department (ED) syndromic surveillance from Boston, Massachusetts, 4) patient visits for ILI from EHR, and 5) reports of ILI from the crowd-sourced system, Flu Near You (FNY), by calculating correlations between these systems across four influenza seasons, 2012–16, at four different spatial resolutions in the US. For the crowd-sourced system, we also used a bootstrapping statistical approach to estimate the minimum number of reports necessary to produce a meaningful signal at a given spatial resolution. Results In general, as the spatial resolution increased, correlation values between all influenza surveillance systems decreased. Influenza-like Illness rates in geographic areas with more than 250 crowd-sourced participants or with more than 20,000 visit counts for EHR tracked government-lead estimates of influenza activity. Conclusions With a sufficient number of reports, data from novel influenza surveillance systems can complement traditional healthcare-based systems at multiple spatial resolutions.

In this paper, recent comparison tests are performed on smartphone magnetometer “apps”with a view toward their suitability for detecting and measuring magnetic storm events. Although the iPhone and Samsung smartphone magnetometer sensors in this study have a nominal sensitivity of ±150 nT, this “digitization”noise level is not a Gaussian process and cannot be further reduced by co-adding more measurements to obtain √N improvements. Moreover, there are a variety of systematic effects including apparently uncontrollable glitches and “dc”baseline changes that have amplitudes of ±2000 nT during long-term measurement operations. These systematic variations appear to mask the detection of all but the strongest geomagnetic storms with K > 8. It is possible that other existing smartphones outside this study have improved magnetic sensors, and that future improvements in sensor design may change these conclusions, so a continued study of this issue is warranted.

The "Railway Work, Life & Death" project explores accidents and ill-health amongst British and Irish railway workers from the late nineteenth century to 1939. Drawing from state, railway company and trade union records, the project is making details of the working lives and accidents of railway employees more easily accessible. This note describes the collaborative impetus behind the project, and the crowd-sourcing methodology used, including the importance of working with volunteers. It shows that focusing on individual cases, at scale, is extremely revealing about the nature of work and the dangers of one of the largest employers of its time. It aims to encourage others to engage with crowd-sourcing and co-creation, as well as to make use of the resources being produced. [ABSTRACT FROM AUTHOR]

Big-data acquisition and processing is important in developing value driven machine learning applications. This is challenging in compute resource-constrained scenarios. Compute resource-constrained scenarios arise due to low capacity of installed cloud infrastructure and low availability of high speed internet links. These factors limit the ability to process crowd-sourced data to develop machine learning applications. The volunteer computing paradigm is found to be suitable for addressing these challenges. Volunteer computing paradigm makes use of computing nodes provided by users distributed over a geographical area. It leverages on the availability of volunteers with low cost computing entities. This paper proposes the fractionated computing system (FCS) to address the challenges described above. FCS incorporates intelligent compute node selection and uses high performance end-user computing nodes (laptops) to process the crowd-sourced data. The performance of FCS is investigated against the existing method of using cloud servers. Results show that FCS reduces acquisition costs and power consumption by 35% and up to 56.5% on average, respectively. The watt per bit expended in processing crowd-sourced data is also enhanced by up to 98% on average. In addition, the use of FCS enhances memory resources accessible for data processing. Simulations show that increasing memory in modular computing entities by up to 58.7% enhances memory available across network of modular volunteer computing nodes by 0.5 EB. The use of end-user nodes with modular communication subsystems instead of end-user computing nodes with non-modular communication sub-systems enhances channel capacity by 37.5% on average. [ABSTRACT FROM AUTHOR]

Breeding ground food availability is critical to the survival and productivity of adult birds. The common cuckoo Cuculus canorus is a brood‐parasitic Afro‐Palearctic migrant bird exhibiting long‐term (breeding) population declines in many European countries. Variation in population trend between regions and habitats suggests breeding ground drivers such as adult food supply. However, cuckoo diet has not been studied in detail since before the most significant population declines in Europe began in the mid‐1980s. 20th century studies of cuckoo diet largely comprised field observations likely to carry bias towards larger prey taxa. Here we demonstrate the potential value of 1) using high‐throughput DNA sequencing of invertebrate prey in faeces to determine cuckoo diet with minimal bias towards large prey taxa, and 2) using crowd‐sourced digital photographs from across Britain to identify lepidopteran cuckoo prey taxa during recent years post‐decline (2005–2016). DNA analysis found a high frequency of Lepidoptera, including moths of family Lasiocampidae, prominent within the past literature, but also grasshoppers (Orthoptera) and flies (Diptera) that may be overlooked by field observation methodologies. The range of larval lepidopteran prey identified from photographs largely agreed with those previously documented, with potential signs of reduced diversity, and identities of key adult prey taxa were supported by molecular results. Notably, many identified cuckoo prey taxa have shown severe declines due to agricultural intensification, suggesting this has driven spatial patterns of cuckoo loss. Landscape‐scale, lowland rewilding interventions provide opportunities to understand the scale of reversal of previous agricultural intensification that may be necessary to restore prey populations sufficiently to permit recolonization by cuckoos. [ABSTRACT FROM AUTHOR]

Discrete, ephemeral natural phenomena with low spatial or temporal predictability are incredibly challenging to study systematically. In ecology, species interactions, which constitute the functional backbone of ecological communities, can be notoriously difficult to characterise especially when taxa are inconspicuous and the interactions of interest (e.g., trophic events) occur infrequently, rapidly, or variably in space and time. Overcoming such issues has historically required significant time and resource investment to collect sufficient data, precluding the answering of many ecological and evolutionary questions. Here, we show the utility of social media for rapidly collecting observations of ephemeral ecological phenomena with low spatial and temporal predictability by using a Facebook group dedicated to collecting predation events involving reptiles and amphibians in sub-Saharan Africa. We collected over 1900 independent feeding observations using Facebook from 2015 to 2019 involving 83 families of predators and 129 families of prey. Feeding events by snakes were particularly well-represented with close to 1,100 feeding observations recorded. Relative to an extensive literature review spanning 226 sources and 138 years, we found that social media has provided snake dietary records faster than ever before in history with prey being identified to a finer taxonomic resolution and showing only modest concordance with the literature due to the number of novel interactions that were detected. Finally, we demonstrate that social media can outperform other citizen science image-based approaches (iNaturalist and Google Images) highlighting the versatility of social media and its ability to function as a citizen science platform. [ABSTRACT FROM AUTHOR]

This article interrogates digital "story banking," a storytelling practice that has become increasingly popular among U.S. grassroots advocacy organizations. Through the examination of LinkedIn data and in-depth interviews with story banking professionals, this technique emerges as the centerpiece of the growing institutionalization, professionalization, and datafication of storytelling in progressive advocacy. Following the 2016 election, political crisis and an increasing awareness of changing information consumption patterns promoted story banking diffusion. Story banking ushers in the era of stories as data and political story on demand. Yet, political constraints currently limit story banking to a reactive approach based on news monitoring, algorithmic shortlisting of stories, and audience testing. Furthermore, an unresolved tension has emerged between the growing centralization of storytelling functions and the participatory potential of crowd-sourced story banks. The implications of these trends for progressive advocacy organizations and the groups they aim to represent are considered. [ABSTRACT FROM AUTHOR]

The present paper is devoted to the moderator’s intellectual work on the international research project “Encyclopedia of flat curves”. A process of creating the so called paper-matrix is described. One unexpected circumstance is brought to light: mainly, the content of a paper-matrix is determined by the project general functions, in spite of its reflection on the moderator’s personal views. [ABSTRACT FROM AUTHOR]

Systems within IoT domains such as ITS, Smart City, Smart Grid and other, often rely on real-time information and communication. These types of systems often include geographically distributed nodes which are connected via cellular or other wireless networks. This means great variability and uncertainty in network connection performance, effectively increasing the expected minimum system response time. Having information about network connection performance means that it is possible to predict the performance of the system in terms of sensor access delay or application response time. We obtain the performance information, in terms of signal strength and transport layer round trip time, using crowd sourcing and consumer devices which causes the measurements to be heterogeneously distributed. From these measurements we want to create a network performance map but in areas with sparse measurements the reliability of the map values will be low. To solve this problem we include neighboring measurements and evaluate the impact of doing so. We show that generally there is a benefit from including neighboring measurements, and that transport layer round trip times are less sensitive to bias when increasing the size of the extended area to include measurements from. [ABSTRACT FROM AUTHOR]

The final objective of smart cities is to optimize services and improve the quality of life of their citizens, who can play important roles due to the information they can provide. This information can be used in order to enhance many sectors involved in city activity such as transport, energy or health. Crowd-sourcing initiatives focus their efforts on making cities safer places that are adapted to the population size they host. In this way, citizens are able to report the issues they identify to the relevant body so that they can be fixed and, at the same time, they can provide useful information to other citizens. There are several projects aimed at reporting incidents in a smart city context. In this paper, we propose the use of model-driven engineering by designing a graphical domain-specific language to abstract and improve the incident-reporting process. With the use of a domain-specific language, we can obtain several benefits in our research for users and cities. For instance, we can shorten the time for reporting the events by users and, at the same time, we gain an expressive power compared to other methodologies for incident reporting. In addition, it can be reused and is centered in this specific domain after being studied. Furthermore, we have evaluated the DSL with different users, obtaining a high satisfaction percentage.

This paper describes an on-road air quality monitoring and control approach by proposing an agent-based system for modeling the urban road network infrastructure, establishing the real-time and predicted air pollution indexes in different road segments and generating recommendations and regulation proposals for road users. This can help by reducing vehicle emissions in the most polluted road sections, optimizing the pollution levels while maximizing the vehicle flow. For this, we use data sets gathered from a set of air quality monitoring stations, embedded low-cost e-participatory pollution sensors, contextual data, and the road network available data. These data are used in the air quality indexes calculation and then the generation of a dynamic traffic network. This network is represented by a weighted graph in which the edges weights evolve according to the pollution indexes. In this paper, we propose to combine the benefits of agent technology with both machine learning and big data tools. An artificial neural networks model and the Dijkstra algorithm are used for air quality prediction and the least polluted path finding in the road network. All data processing tasks are performed over a Hadoop-based framework: HBase and MapReduce.

A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? Answers to this question will lead to designing and deploying more effective crowd-sourced financial platforms and to minimizing issues stemming from risk such as implied volatility. To investigate this trade-off, we conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&P 500, WTI Oil and Gold prices). Using the data collected, we modeled the belief update process of participants using models inspired by Bayesian models of cognition. We show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning. We also observe that social learning led to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote.

Mining GPS traces is proposed, as an alternative to surveying and satellite image processing, for constructing road and pedestrian networks, trading out the constructed network’s perfectness for substantially reduced cost and time. To construct a road or pedestrian network, the proposed algorithm requires only GPS traces, which can be collected with low or no cost using crowd-sourced repositories such as open street map. Network construction algorithms in the literature, mostly do not provide enough details about different steps of constructing a network. Besides, their evaluations are mostly visual, qualitative, and limited to one dataset. This paper not only describes all the steps involved in constructing a network but also quantitatively evaluates the algorithm (via three metrics: precision, completeness, and topology correctness) using numerous datasets from different sources and discusses its time complexity. The proposed algorithm’s distinguishing strengths can be summarized as: 1) fixing the constructed network’s topological errors while many existing algorithms overlook the constructed network’s topological integrity; 2) imposing no restrictions on the shape of GPS traces while many existing algorithms include such implicit or explicit constraints, e.g. GPS traces cannot include U turns; 3) being fully automatic and involving no subjective judgments or human interventions for filtering GPS points unlike many existing algorithms; and 4) achieving high accuracies in practice. The proposed algorithm includes six hyperparameters, all related to GPS traces’ geometrical traits and optimized during evaluation. [ABSTRACT FROM AUTHOR]

Today, most information sources provide factual, objective knowledge, but they fail to capture personalized contextual knowledge which could be used to enrich the available factual data and contribute to their interpretation, in the context of the knowledge of the user who queries the system. This would require a knowledge framework which can accommodate both objective data and semantic enrichments that capture user provided knowledge associated to the factual data in the database. Unfortunately, most conventional DBMSs lack the flexibilities necessary (a) to prevent the data and metadata, evolve quickly with changing application requirements and (b) to capture user-provided and/or crowdsourced data and knowledge for more effective decision support. In this paper, we present CrowdSourced Semantic Enrichment (CroSSE) knowledge framework which allows traditional databases and semantic enrichment modules to coexist. CroSSE provides a novel Semantically Enriched SQL (SESQL) language to enrich SQL queries with information from a knowledge base containing semantic annotations. We describe CroSSE and SESQL with examples taken from our SmartGround EU project. [ABSTRACT FROM AUTHOR]

Smart cities (SCs) are becoming highly sophisticated ecosystems at which innovative solutions and smart services are being deployed. These ecosystems consider SCs as data production and sharing engines, setting new challenges for building effective SC architectures and novel services. The aim of this article is to "connect the pieces" among Data Science and SC domains, with a systematic literature review which identifies the core topics, services, and methods applied in SC data monitoring. The survey focuses on data harvesting and data mining processes over repeated SC data cycles. A survey protocol is followed to reach both quantitative and semantically important entities. The review results generate useful taxonomies for data scientists in the SC context, which offers clear guidelines for corresponding future works. In particular, a taxonomy is proposed for each of the main SC data entities, namely, the "D Taxonomy" for the data production, the "M Taxonomy" for data analytics methods, and the "S Taxonomy" for smart services. Each of these taxonomies clearly places entities in a classification which is beneficial for multiple stakeholders and for multiple domains in urban smartness targeting. Such indicative scenarios are outlined and conclusions are quite promising for systemizing. [ABSTRACT FROM AUTHOR]

The GB1900 project transcribed almost all text on 1:10,650 mapping covering Great Britain, published circa 1900: 2.6 million geo-referenced text strings, so possibly the largest specifically historical gazetteer. Nearly 1200 volunteers made 5.5 million transcriptions, including "confirmations." This paper describes the project's interaction with online volunteers and then presents their experience, as recorded through the online system itself, six in-depth interviews and 162 responses to an online questionnaire. We find that, unlike volunteers in physical science "citizen science" projects, they were motivated by personal interest in the maps, in places that held meaning for them, and in how places had changed. These conclusions enable us to offer suggestions for volunteer recruitment and retention in similar future projects. [ABSTRACT FROM AUTHOR]

Fingerprint-based indoor localization has become one of the most attractive and promising techniques; however, one primary concern for this technology to be fully practical is to maintain the fingerprint database to combat harsh indoor environmental dynamics, especially in the large-scale and long-term deployment. In this article, focusing on three key problems now existing in fingerprint database updating approaches such as the mechanism for triggering updates, the collection of new fingerprints and determination of fingerprints’ location information, we propose a fuzzy map mechanism and decision methods of neighbours’ fingerprints in response to all kinds of changes in indoor environments. Meanwhile, we design a static data collecting mechanism to filter reliable information from numerous users’ inputs and propose a neighbours’ fingerprint-assisted technique to calculate the location of fingerprints. Experimental results demonstrate that the proposed solution not only improves the performance of updating the fingerprint database in real time and robustness by 40% and 50%, respectively, but also reduces the update frequency and improves mean location accuracy by over 40%. [ABSTRACT FROM AUTHOR]

Abstract The safety hazard and the additional costs on transportation due to road accidents invite the necessity to minimize their impact. In this paper, we study the spatial-clustering behavior and hazard vulnerability of car accidents that occurred in Lebanon between 2015 and 2018. Assessment of spatial clustering of accidents and hot spots densities were examined using the Global G method of spatial autocorrelation and Getis–Ord G i ∗ statistics. A novel Road Hazard Index (H i) was proposed to assess hazard vulnerability of road networks and to develop a road hazard prediction model. Highlights • Analysis of accidents' types distribution is provided. • Spatial autocorrelation in the Lebanese accidents dataset and high clustering accidents areas is detected. • Hot spots variation between the summer and winter seasons is inspected. • Road hazard index is proposed to measure road segments risk analysis. [ABSTRACT FROM AUTHOR]

Structure-from-motion (SfM) photogrammetry techniques are now widely available to generate digital terrain models (DTMs) from optical imagery, providing an alternative to costlier options such as LiDAR or satellite surveys. SfM could be a useful tool in hazard studies because its minimal cost makes it accessible even in developing regions and its speed of use can provide updated data rapidly in hazard-prone regions. Our study is designed to assess whether crowd-sourced SfM data is comparable to an industry standard LiDAR dataset, demonstrating potential real-world use of SfM if employed for disaster risk reduction purposes. Three groups with variable SfM knowledge utilized 16 different camera models, including four camera phones, to collect 1001 total photos in one hour of data collection. Datasets collected by each group were processed using VisualSFM, and the point densities, accuracies and distributions of points in the resultant point clouds (DTM skeletons) were compared. Our results show that the point clouds are resilient to inconsistency in users' SfM knowledge: crowd-sourced data collected by a moderately informed general public yields topography results comparable in data density and accuracy to those produced with data collected by highly-informed SfM users or experts using LiDAR. This means that in a real-world scenario involving participants with a diverse range of expertise, topography models could be produced from crowd-sourced data quite rapidly and to a very high standard. This could be beneficial to disaster risk reduction as a relatively quick, simple and low-cost method to attain rapidly updated knowledge of terrain attributes, useful for the prediction and mitigation of many natural hazards. [ABSTRACT FROM AUTHOR]