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During an epidemic, decision-makers in public health need accurate predictions of the future case numbers, in order to control the spread of new cases and allow efficient resource planning for hospital needs and capacities. In particular, considering that infectious diseases are spread through human-human transmissions, the analysis of spatio-tempo...
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... This information is invaluable for designing targeted interventions, optimizing resource allocation, and enhancing the overall effectiveness of public health responses. For instance, during the COVID-19 pandemic, mobility data were essential in predicting outbreak locations and evaluating the impact of lockdown measures [50,51]. ...
Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin–destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales—10–100 m and 100–3000 m—to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems.
... In literature, classic density-based clustering algorithms are largely exploited to discover spatial hotspots [7][8][9][10][11]. However, due to the adoption of global parameters, they fail to identify multi-density hotspots (i.e., different regions having various densities [12,13]) unless the clusters (or hotspots) are clearly separated by sparse regions [14]. ...
The increasing pervasiveness of ICT technologies and sensor infrastructures is enabling police departments to gather and store increasing volumes of spatio-temporal crime data. This offers the opportunity to apply data analytics methodologies to extract useful crime predictive models, which can effectively detect spatial and temporal patterns of crime events, and can support police departments in implementing more effective strategies for crime prevention. The detection of crime hotspots from geo-referenced data is a crucial aspect of discovering effective predictive models and implementing efficient crime prevention decisions. In particular, since metropolitan cities are heavily characterized by variable spatial densities of crime events, multi-density clustering seems to be more effective than classic techniques for discovering crime hotspots. This paper presents the design and implementation of MD-CrimePredictor (Multi- Density Crime Predictor), an approach based on multi-density crime hotspots and regressive models to automatically detect high-risk crime areas in urban environments, and to reliably forecast crime trends in each area. The algorithm result is a spatio-temporal crime forecasting model, composed of a set of multi-density crime hotspots, their densities and a set of associated crime predictors, each one representing a predictive model to forecast the number of crimes that are estimated to happen in its specific hotspot. The experimental evaluation of the proposed approach has been performed by analyzing a large area of Chicago, involving more than two million crime events (over a period of 19 years). This evaluation shows that the proposed approach, based on multi-density clustering and regressive models, achieves good accuracy in spatial and temporal crime forecasting over rolling prediction horizons. It also presents a comparative analysis between SARIMA and LSTM models, showing higher accuracy of the first method with respect to the second one.
... In this paper, we propose an approach for occupancy prediction in Cognitive Buildings, whose main characteristics are: (i) a multi-layer hierarchy for Federated Learning [53], which combines models for presence forecasting at the different levels; (ii) the use of IoT devices at the Edge for data collection and actuation; (iii) long short-term memory neural network models [22,54]; (iv) the exploitation of Edge Computing for processing data and forecasting occupancy; (v) a design template that can be used to design and implement real distributed systems for occupancy prediction in Cognitive Buildings. The following subsections will describe the proposed approach. ...
... As another instance, in environmental analysis scientists typically divide a city into zones depending on environmental variables such as pollutant density or atmospheric conditions. In epidemic analysis, the detection of hotspots is useful to forecast spreading trends [15,16] in cities and countries. In crime analysis, police departments are interested in identifying locations with similar crime behaviors, so as to characterize the urban territory and forecast crime trends [17]. ...
... For example, the use of annual urban crime data can be used to identify a city's crime hotspots, which can help understand the causes of crime in the city and help relevant authorities develop more effective crime prevention measures with limited resources (Butt et al., 2020). Similarly, based on the disease infection data, we can obtain the hotspots of disease infection in a city, which can facilitate the investigation of the infection and transmission patterns of diseases in the region to effectively carry out epidemic prevention and control (Canino et al., 2022;Des Jarlais et al., 2018). ...
Hotspot detection from geo-referenced urban data is critical for smart city research, such as traffic management and policy making. However, the classical clustering or classification approach for hotspot detection mainly aims at identifying “hotspot areas” rather than specific points, and the setting of global parameters such as search bandwidth can lead to inaccurate results when processing multi-density urban data. In this article, a data-driven adaptive hotspot detection (AHD) approach based on kernel density analysis is proposed and applied to various spatial objects. The adaptive search bandwidth is automatically calculated depending on the local density. Window detection is used to extract the specific hotspots in AHD, thus realizing a small-scale characterization of urban hotspots. Through the trajectory data of Harbin City taxis and New York City crime data, Geo-information Tupu is used to analyze the obtained specific hotspots and verify the effectiveness of AHD, providing new ideas for further research.
... The interactions between human mobility and epidemic spread have been studied unprecedentedly during the COVID-19 pandemic [1][2][3][4][5][6][7][8]. With these efforts, nonpharmaceutical interventions (such as national lockdowns) have been evaluated for their effectiveness and socioeconomic impact on different groups [9][10][11], models have been developed to predict disease spatial diffusion [12,13], and scenarios have been modeled to assess their outcomes [14][15][16][17]. Studies have demonstrated that mobility data are a meaningful proxy measure of social distancing [18], affect viral spreading [19,20], and are useful for predicting the spread of COVID-19 [21][22][23]. ...
In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefits of publicly available mobility data, a key question remains unanswered: are models using mobility data performing equitably across demographic groups? We hypothesize that bias in the mobility data used to train the predictive models might lead to unfairly less accurate predictions for certain demographic groups. To test our hypothesis, we applied two mobility-based COVID infection prediction models at the county level in the United States using SafeGraph data, and correlated model performance with sociodemographic traits. Findings revealed that there is a systematic bias in models’ performance toward certain demographic characteristics. Specifically, the models tend to favor large, highly educated, wealthy, young, and urban counties. We hypothesize that the mobility data currently used by many predictive models tends to capture less information about older, poorer, less educated and people from rural regions, which in turn negatively impacts the accuracy of the COVID-19 prediction in these areas. Ultimately, this study points to the need of improved data collection and sampling approaches that allow for an accurate representation of the mobility patterns across demographic groups.
... A predictive approach was presented by Canino et al. (2022) that utilizes spatial analysis and regressive models to discover spatiotemporal epidemic patterns from mobility and infection data. An important and timely research problem is also addressed in our study by providing comprehensive data on the impact of the COVID-19 pandemic on power usage patterns, which is crucial given the significant societal and economic impacts of the pandemic. ...
Improving load forecasting is becoming increasingly crucial for power system management and operational research. Disruptive influences can seriously impact both the supply and demand sides of power. This work examines the impact of the coronavirus on power usage in two US states from January 2020 to December 2020. A wide range of machine learning (ML) algorithms and ensemble learning are employed to conduct the analysis. The findings showed a surprising increase in monthly power use changes in Florida and Texas during the COVID-19 pandemic, in contrast to New York, where usage decreased over the same period. In Texas, the quantity of power usage rises from 2% to 6% practically every month, except for September, when it decreased by around 1%. For Florida, except for May, which showed a fall of roughly 2.5%, the growth varied from 2.5% to 7.5%. This indicates the need for more extensive research into such systems and the applicability of adopting groups of algorithms in learning the trends of electric power demand during uncertain events. Such learning will be helpful in forecasting future power demand changes due to especially public health-related scenarios.
... The third one is an approach to discover spatio-temporal predictive epidemic patterns from mobility and infection data, whose experimental evaluation has been carried out on real-world COVID-19 data. (Canino et al., 2022a). The presented real-world cases are aimed to show three example where data analytics models can provide effectively valuable support for city managers in tackling smart city challenges, to improve urban applications and citizens' lives. ...
... An epidemic predictive approach based on spatial analysis, mobility and regressive models has been presented in Canino et al. (2022a). From movement and infection data, the approach is utilized to identify spatio-temporal predicted epidemic trends. ...
... Epidemic hotspots are detected during this step. Specifically, an epidemic hotspot is defined as "an infection hotspots whose spatial overlap with a mobility hotspot is greater than a given threshold" (Canino et al., 2022a). The spatial overlap is calculated as the percentage of the overlapping area between the identified infection and mobility hotspots. ...
Urban environments continuously generate larger and larger volumes of data, whose analysis can provide descriptive and predictive models as valuable support to inspire and develop data-driven Smart City applications. To this aim, Big data analysis and machine learning algorithms can play a fundamental role to bring improvements in city policies and urban issues. This paper introduces how Big Data analysis can be exploited to design and develop data-driven smart city services, and provides an overview on the most important Smart City applications, grouped in several categories. Then, it presents three real-case studies showing how data analysis methodologies can provide innovative solutions to deal with smart city issues. The first one is an approach for spatio-temporal crime forecasting (tested on Chicago crime data), the second one is methodology to discover mobility hotsposts and trajectory patterns from GPS data (tested on Beijing taxi traces), the third one is an approach to discover predictive epidemic patterns from mobility and infection data (tested on real COVID-19 data). The presented real-world cases prove that data analytics models can effectively support city managers in tackling smart city challenges and improving urban applications.
... A predictive approach based on spatial analysis and regressive models is proposed in [13], aiming at discovering spatio-temporal predictive epidemic patterns from infection and mobility data. The algorithm is composed of several steps, starting from the detection of epidemic hotspots (urban areas where infection events occur more densely with respect to others) and mobility hotspots (urban regions more densely visited by mobility traces), to the discovery of epidemic patterns among epidemic hotspots. ...
Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such an amount of information is analyzed to discover data-driven models, which can be exploited to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, crime forecasting, traffic flows, etc. In particular, the detection of city hotspots is de facto a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are a valuable support for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable for discovering hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. In fact, a proper threshold setting is very difficult when clusters in different regions have considerably different densities, or clusters with different density levels are nested. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate for discovering city hotspots. Indeed, such algorithms rely on multiple minimum threshold values and are able to detect multiple pattern distributions of different densities, aiming at distinguishing between several density regions, which may or may not be nested and are generally of a non-convex shape. This paper discusses the research issues and challenges for analyzing urban data, aimed at discovering multi-density hotspots in urban areas. In particular, the study compares the four approaches (DBSCAN, OPTICS-xi, HDBSCAN, and CHD) proposed in the literature for clustering urban data and analyzes their performance on both state-of-the-art and real-world datasets. Experimental results show that multi-density clustering algorithms generally achieve better results on urban data than classic density-based algorithms.
... This human-in-the-loop qualitative analysis help derives in-depth interpretations from identifying public discourse [26] better. Sentiment analysis is a natural language processing approach that attempts to analyze the sentiment and emotions of people expressed in unstructured text [27][28] [29]. This technique has been widely used in social science research for analyzing the general public's sentiment towards products, services, and any social phenomenon [30][31] [32]. 2 "I homeschooled my kids for many years so I didn't have get mine vaccinated at all. ...
Objectives: This study employs text mining and natural language processing approaches for analyzing and unearthing public discourse and sentiment towards the recent spiking Measles outbreaks reported across the globe.
Study design: A detailed qualitative study was designed using text mining and natural language processing on the user-generated comments from Reddit, a social news aggregation and discussion website.
Methods: A detailed analysis was conducted using topic modeling and sentiment analysis on Reddit comments (n=87203) posted between October 1 and December 15, 2022. Topic modeling was used to leverage major themes related to the Measles health emergency and public discourse; the sentiment analysis was performed to check how the general public responded to different aspects of the outbreak.
Results: Our results revealed several intriguing and helpful themes, including parental concerns, anti-vaxxer discussions, and measles symptoms from the user-generated content. The results further confirm that even though there have been administrative interventions to promote vaccinations that affirm the parents' concerns to a greater extent, the anti-vaccination or vaccine hesitancy prevalent in the general public reduces the effect of such intercessions.
Conclusions: A proactive analysis of public discourse and sentiments during health emergencies and disease outbreaks is vital. This study effectively explored public perceptions and sentiments to assist health policy researchers and stakeholders in making informed and data-driven decisions.}
