Disability and Discourses of Mobility and Movement

Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors and mobility patterns using the useful information that mobile phone data provide. Specifically, the digital traces left by the large number of mobile devices provide important information that facilitates a deeper understanding of human behavior and mobility configurations for researchers in various fields, such as criminology, urban sensing, transportation planning, and healthcare. Mobile phone data record significant spatiotemporal (i.e., geospatial and time-related data) and communication (i.e., call) information. These can be used to achieve different research objectives and form the basis of various practical applications, including human mobility models based on spatiotemporal interactions, real-time identification of criminal activities, inference of friendship interactions, and density distribution estimation. The present research primarily reviews studies that have employed mobile phone data to investigate, assess, and predict human communication and mobility patterns in the context of crime prevention. These investigations have sought, for example, to detect suspicious activities, identify criminal networks, and predict crime, as well as understand human communication and mobility patterns in urban sensing applications. To achieve this, a systematic literature review was conducted on crime research studies that were published between 2014 and 2022 and listed in eight electronic databases. In this review, we evaluated the most advanced methods and techniques used in recent criminology applications based on mobile phone data and the benefits of using this information to predict crime and detect suspected criminals. The results of this literature review contribute to improving the existing understanding of where and how populations live and socialize and how to classify individuals based on their mobility patterns. The results show extraordinary growth in studies that utilized mobile phone data to study human mobility and movement patterns compared to studies that used the data to infer communication behaviors. This observation can be attributed to privacy concerns related to acquiring call detail records (CDRs). Additionally, most of the studies used census and survey data for data validation. The results show that social network analysis tools and techniques have been widely employed to detect criminal networks and urban communities. In addition, correlation analysis has been used to investigate spatial–temporal patterns of crime, and ambient population measures have a significant impact on crime rates.

Context-awareness has empowered advanced multimedia computing technology to provide user-oriented services and environment. This research aims at achieving a seamless video delivery service concentrating on the user’s mobility patterns during a multimedia streaming service session. Mobility supporting technology providing seamless services are categorized into host mobility and user mobility. The former corresponds to host-level handoff while the latter refers to user-level handoff. In host-level handoff, the influential parameters affecting the quality of video consumption are the total distance between the hosts, the distance for streaming to resume while user is in mobility mode and the screen size of the end host. The interdependency amongst the parameters is evaluated by a user subjective assessment and a befitting video quality model is developed, accordingly. Additionally, the optimal video delivery switching point to enable user mobility based video service is studied based on a quality analysis of host mobility services at varying distances among hosts. This quality model supporting a complete mobilization has a high correlation with the assessed quality and enables an adequate seamless mobility for multimedia service delivery.KeywordsSession mobilitySeamless mobilityMultimedia handoff

Wild pigs are the most prolific, very intelligent and secretive species that over the recent past has been in a lot of conflict terms with the humans.These animals are adaptive generalists and survive in wide range of habitats worldwide.The reproductive traits of wild pigs are extraordinary showing high prolificacy so it becomes impossible to control their population.These animals have adapted to become nocturnal proving it difficult to know about their behavior.However in order in spite of these challenges the main objectives of this study was to understand the behavior and ecology of this particular species to come up the control strategies.Thus basis for documenting and understanding the wild pig behavior has dramatically evolved, contributing significantly to our understanding of these animals.In general, however, the lack of intensive field studies was attributed to the fact that wild pigs were regarded everywhere as a pest.The wild pig groups usually known as sounder's were tracked and all findings were documented.The study period was about a year and the wild pigs were tracked and observed at the adjoining regions of the Eastern Ghats (Sathyamangalam region) and the Western Ghats (Mudumalai tiger reserve, Anaimalai tiger reserve) and of Tamil Nadu state during November, 2012 to October, 2014.A variety of behaviors were documented such as social unit organization, Habitat utilization, Daily activity patterns, Movement patterns, Modes of mobility, Home range, Male-male competition, Maternal behavior, Resting/loafing beds, Mannerism and attitudes, Vigilance behavior, Vocalizations, Wallowing, Rubbing, Symbiotic grooming behavior, Scent marking, Senses.At the end of the study we were able to precisely outlay the above the entire behavior attributes of wild pigs.These findings are necessary to understand the wild pigs and their vermin nature to combat crop raiding, eventually cutting down Human-Wild pig conflict, that will a more scientific method of conservation to avoid conflicts ending in tragic outcomes.

Infected individuals often display mobility patterns that differ significantly from those of healthy individuals—traveling less frequently, covering shorter distances, visiting fewer destinations, and altering their timing and modes of movement. In this paper, to explore the influence of changes in travel frequency and destination on the spatial spread of infectious diseases, we propose a susceptible–infectious–susceptible patch model in which susceptible and infected populations have different dispersal rates and connectivity matrices. We first establish the threshold dynamics in terms of the basic reproduction number and show the existence and uniqueness of endemic equilibrium (EE) when . Then we examine the asymptotic profiles of the EE under small dispersal rate of the susceptible or infected population. In particular, we prove that as susceptible mobility tends to zero, the EE converges a disease‐free equilibrium in the most general case. We find that asymmetric dispersal provides a new approach to eliminate infections than small susceptible mobility. Furthermore, we analyze both local and global disease prevalence to identify strategies for lowering endemic level. Variations in connectivity matrix can lead to high prevalence in low‐risk patch, a failure of the order‐preserving property on local prevalence. Numerical simulations are conducted to further reveal the role of heterogeneous mobility patterns. Overall, this study offers new insights into how human movement shapes the distribution of disease and generalizes many results in the literature.

Owing to the self organizing, self configuring and self healing capabilities, Wireless Mesh Networks (WMN) have emerged as most powerful architectures in recent times. WMN operate with limited battery resources. To save energy, ieee 802.11s has introduced a mechanism called power save mode(PSM) which switches STA mode from active to light sleep or deep sleep mode when STA is not involved in transmission. Several models have been proposed by researchers to improve the energy saving mechanisms in 802.11s. Most of the researchers have considered node position as static while proposing their model. In WMN nodes are subjected to change their position over a period of time depending on the mobility mode and pattern. Under mobility conditions there could be higher energy consumption at STA compared to static cases. Hence in this work, we propose an energy saving model that involves STA mobility and trigger power save modes based on the remaining energy at each STA. The experiments have been conducted using RandomWayPoint mobility model in Network Simulator3. The behaviour of the model is observed by keeping STA position dynamic. The results show that energy consumption rate of STA is high under mobility conditions compared to static. By Implementing PSM under mobility situations, we are able save substantial energy thus increasing the life of the batteries associated with each STA in a WMN.

This paper discusses the changing spatiality and movement modes of the 'Henro' pilgrimage on Shikoku Island from 1920-1930. The pilgrimage is one of principal topics in the geography of religion, and geographers have analysed its spatial structure using historical, quantitative and humanistic approaches. For those researching the spatiality of the pilgrimage, it is necessary to clarify the contested processes by which spatiality was produced and the manner in which discourses concerned with modes of movement were produced in order to control the flow of pilgrims. Though pilgrims mainly traveled on foot for religious training, there were no statements concerning the pilgrimage route in guidebooks published at Edo era.Since the modern Japanese government created a transport system to create homogeneous space in Japan, several transport systems-train, bus and ship-were available in Shikoku until the middle of 1930s. For pilgrims who used these transport systems, movement patterns during the Henro pilgrimage became diversified. However, since the end of the Taisho era, 'the intellectual class' who had hardly previously participated, became interested in the Henro pilgrimage. As a result of this change, the Henro pilgrimage became involved in domestic tourism as alternative form of tourism at the end of the 1920s, and pilgrims using the new transport system and taking casual pleasure in were referred to as 'Modern Henro'.On the other hand, in 1929, 'Henro-Dogyokai', which aimed to organize pilgrims and provide several activities concerned with the Henro pilgrimage, was founded at Tokyo. Its goal was to enlighten people along national policy and to criticize the Modern Henro because they regarded its style as religiously regressive. Through providing information about the Henro pilgrimage in their monthly journal Henro and their activities, they emphasised the authentic style or way of the Henro pilgrimage, and at the same time they emphasized that pilgrims should journey on foot.In short, from the end of 1920s to the middle of 1930s, while Japanese tourism or 'Modern Henro' represented the space of the Henro pilgrimage as tourist space, 'Henro-Dogyokai' represented it as religious training space. However, both agents reconstituted the network in the space of the Henro pilgrimage; indeed the space of the Henro pilgrimage was a contested one in this period. Of course, traditional pilgrims-those who had not been admitted to live their village community and could do nothing but carry on the pilgrimage with begging -also existed. In this context, the reconstituted Henro pilgrimage was appropriated within Japanese fascist policy through its articulation with hiking in the middle of 1930s, and Modern Henro' and 'Henro-Dogyokai' were placed within national ideology. The Japanese government coerced people into walking to make proper bodies and to pray for victory in World War II. In this policy, adopting various modes of movement in pilgrimage was unacceptable since pilgrims were compelled to walk. However, despite this policy, some pilgrims refused to comply and some of them preferred to use transportation.

This book critically unpacks the why and how around everyday rhetorics and slogans promoting global LGBTQ equality. Examining the means by which particular discourses of progress and hope are circulated globally, it offers unique insights into how LGBTQ livelihoods, relationships, and social movements are legitimated and valued in contemporary society. Adopting an innovative critical discourse-ethnographic approach, Comer draws on scholarship from the sociolinguistics of global mobility, queer linguistics, and digital media studies, offering in-depth analyses of representations of LGBTQ identity across a range of domains. The volume examines semiotic linkages between: LGBTQ tourism marketing; Cape Town, South Africa, as a locus for contemporary ideologies of global mobility and equality; diversity management practices framing LGBTQ equality as a business imperative; and, humanitarian discourses within transnational LGBTQ advocacy. Autoethnographic vignettes and principles from within queer theory are incorporated by Comer's critical discourse-ethnographic approach, giving voice to personal experience in order to sharpen scholarly understanding of the relationships between everyday social voices', globalized neoliberal political economy, and the media. Taken together, the volume expansively (if queerly) maps what Comer refers to as the mediatization of equality', and will be of interest to graduate students and scholars in critical discourse studies, sociolinguistics, and linguistic anthropology, as well as those working across such fields as media studies, queer studies, and sociology

Mining people's semantic trajectory behaviours from geotagged photographs

ABSTRACTThe modelling of human mobility and migration patterns has received much attention due to its substantial importance. Despite long-term efforts, we still lack a modelling framework that captures mobility patterns and further obtains a prospective view of movement trends with regards to diverse impacting factors. Here, we propose a proportional odds model of human mobility and migration (POM-HM) that takes a probabilistic approach to model human movements. Our model is based on the migration probability with a log-logistic distribution under the proportional odds assumption. Explanatory variables are introduced into the model by re-parameterizing the probability distribution function. The two resultant functions, namely, the migration strength and cumulative hazard, are used to estimate regional differences among travel fluxes and their tendencies. The performance of the POM-HM in terms of its validity and accuracy is examined and compared with the gravity model and the radiation model. The probability-based modelling framework enables us to investigate regional variations in migrant fluxes consequently further predict potential future patterns. In short, our modelling approach captures the probabilistic nature of human mobility and migration and furthers our understanding of both the spatiotemporal patterns of population movements and the impacts of various driving forces.

Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.

During the COVID-19 pandemic, governments have attempted to control infections within their territories by implementing border controls and lockdowns. While large-scale quarantine has been the most successful short-term policy, the enormous costs exerted by lockdowns over long periods are unsustainable. As such, developing more flexible policies that limit transmission without requiring large-scale quarantine is an urgent priority. Here, the dynamics of dismantled community mobility structures within US society during the COVID-19 outbreak are analysed by applying the Louvain method with modularity optimization to weekly datasets of mobile device locations. Our networks are built based on individuals' movements from February to May 2020. In a multi-scale community detection process using the locations of confirmed cases, natural break points from mobility patterns as well as high risk areas for contagion are identified at three scales. Deviations from administrative boundaries were observed in detected communities, indicating that policies informed by assumptions of disease containment within administrative boundaries do not account for high risk patterns of movement across and through these boundaries. We have designed a multi-level quarantine process that takes these deviations into account based on the heterogeneity in mobility patterns. For communities with high numbers of confirmed cases, contact tracing and associated quarantine policies informed by underlying dismantled community mobility structures is of increasing importance.

Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study

Mobility modeling is the mathematical modeling of mobile users' (cars, cell phone users) movement patterns. The resulting model not only provides us with an understanding of past mobile user movements, but also enables us to predict how a mobile user might move in the future. It has been found useful in both infrastructure-based wireless network and ad hoc network for protocol evaluation, resource planning, etc. A nonparametric hierarchical Bayesian approach is proposed in this paper for extracting hierarchical mobility patterns from mobile user traces. Experiment results show that the proposed method is able to generate a hierarchical mobility model that better reflect the mobility pattern structure in many scenarios. It also has better future movement prediction compared to the hidden Markov model.

The formation of lithic assemblages

SUMMARYTarget tracking in wireless sensor networks is a well‐known application. In real life scenario, target mobility can be predicted using well‐known filters. In this paper, we explain an approach to model the pattern of movement of a target on the basis of target data available. This method utilizes filter techniques to predict the target and a curve‐fitting algorithm to model the mobility of a target in both linear and non‐linear motion patterns. Two alternate strategies to achieve mobility approximation have been proposed and compared. The efficacy of the algorithm is, further, adjudged by comparing its mobility prediction vis‐a‐vis the Kalman filter. Simulation results show that with sufficient data, the mobility pattern of the target can be fairly calculated even if the target moves unpredictably. Copyright © 2013 John Wiley & Sons, Ltd.