Spatio-temporal Distribution Of Population Research Articles

Spatiotemporal dynamics of prey-predator model incorporating Holling-type II functional response with fear and its carryover effects.

The recent focus in the fields of biology and ecology has centered on the significant attention given to the mathematical modeling and analyzing the spatiotemporal population distribution among species engaged in interactions. This paper explores the dynamics of the temporal and spatiotemporal delayed Bazykin-type prey-predator model, incorporating fear and its carryover effect. In our model, we incorporated a functional response of the Holling-type II. In the temporal model, a detailed dynamic analysis was carried out, investigating the positivity and boundedness of solutions, establishing the uniqueness and existence of positive interior equilibria, and examining both local and global stability. Additionally, we explored the presence of saddle-node, transcritical, and Hopf bifurcations varying attack rate parameter. The delayed system shows highly periodic behavior. Additionally, for the spatiotemporal model, we provide a complete analysis of local and global stability, and we derive the conditions for the existence of Turing instability for both self-diffusion and cross-diffusion, respectively. The two-dimensional diffusive model is further discussed, highlighting various Turing patterns, including holes, stripes, and hot and cold spots, along with their biological significance. Numerical simulations are executed to validate the analytical findings in both temporal and spatiotemporal models.

A spatial approach to assessing PM2.5 exposure level of a brickmaking community in South Africa

ABSTRACT Globally, particulate matter with an aerodynamic diameter of 2.5 µm or less poses a significant threat to human health. The first step in quantifying human health impacts caused by exposure to PM2.5 pollution is exposure assessment. Population-weighted exposure level (PWEL) estimation is one of the methods that provides a more precise exposure assessment since it incorporates the spatiotemporal distribution of population with the pollution concentration estimate. In this study, PM2.5 exposure levels in the local communities around brickmaking industries were investigated, using the population census data of the study area and 1-year data from nine PM2.5 monitoring stations installed in and around the brickmaking industries. The observed PM2.5 data was spatially interpolated using inverse distance weight (IDW). Data on PM2.5 levels across the study area were classified based on the World Health Organization interim target (IT) guidelines and the South African National ambient air quality standard (NAAQS). An annual PM2.5 population weighted exposure level of 27.6 µg/m3 was estimated for the study area. However, seasonal exposure levels of 28.9, 37.6, 26.5, and 20.7 µg/m3 were estimated for the autumn, winter, spring, and summer seasons, respectively. This implies that local communities around the brick kiln in the Vhembe District are exposed to high levels of PM2.5, especially in winter. The PM2.5 levels in the brickmaking industries as well as its other sources in the Vhembe District, therefore, need to be lowered. Findings from population exposure level to pollutants can provide valuable data for formulating policies and recommendations on exposure reduction and public health protection. Implications: PM2.5 concentration in any given environment has high spatial and temporal variability due to the presence of diffused sources in the environment. Using ambient air concentrations to directly estimate population exposure without taking into consideration the disproportionate spatial and temporal distribution of the pollutant and the population may not yield accurate results on human exposure levels. It is, therefore, important to assess the aggregated PM2.5 exposure of a populace within a given area. This study therefore examines the PM2.5 population-weighted-exposure level of the host communities of the brickmaking industry in Vhembe District, South Africa.

Dynamic Assessment of Spatiotemporal Population Distribution Based on Mobile Phone Data: A Case Study in Xining City, China

High-resolution, dynamic assessments of the spatiotemporal distributions of populations are critical for urban planning and disaster management. Mobile phone big data have real-time collection, wide coverage, and high resolution advantages and can thus be used to characterize human activities and population distributions at fine spatiotemporal scales. Based on six days of mobile phone user-location signal (MPLS) data, we assessed the dynamic spatiotemporal distribution of the population of Xining City, Qinghai Province, China. The results show that strong temporal regularity exists in the daily activities of local residents. The spatiotemporal distribution of the local population showed a significant downtown-suburban attenuation pattern. Factors such as land use types, holidays, and seasons significantly affect the spatiotemporal patterns of the local population. By combining other spatiotemporal trajectory data, high-resolution and dynamic real-time population distribution evaluations based on mobile phone location signals could be better developed and improved for use in urban management and disaster assessment research.

Optimization of COVID-19 prevention and control with low building energy consumption

COVID-19 is a global threat. Non-pharmaceutical interventions were commonly adopted for COVID-19 prevention and control. However, during stable periods of the pandemic, energy would be inevitably wasted if all interventions were implemented. The study aims to reduce the building energy consumption when meet the demands of epidemic prevention and control under the stable period of COVID-19. Based on the improved Wells-Riley model considering dynamic quanta generation and pulmonary ventilation rate, we established the infection risk - equivalent fresh air volume - energy consumption model to analyze the infection risk and building energy consumption during different seasons and optimized the urban building energy consumption according to the spatio-temporal population distribution. Shopping centers and restaurants contributed the most in urban energy consumption, and if they are closed during the pandemic, the total infection risk would be reduced by 25%–40% and 15%–25% respectively and the urban energy consumption would be reduced by 30%–40% and 13%–20% respectively. If people wore masks in all public indoor environments (exclude restaurants and KTV), the infection risk could be reduced by 60%–70% and the energy consumption could be reduced by 20%–60%. Gyms pose the highest risk for COVID-19 transmission. If the energy consumption kept the same with the current value, after the optimization, infection risk in winter, summer and the transition season could be reduced by 65%, 53% and 60%, respectively. After the optimization, under the condition of Rt < 1, the energy consumption in winter, summer, and the transition season could be reduced by 72%, 64%, and 68% respectively.

Intelligent Post-Disaster Networking by Exploiting Crowd Big Data

A major disaster would damage the communication infrastructure severely, resulting in further chaos and loss in the disaster stricken area. Rapid restoration of wireless/mobile communications is one of the most critical issues for disaster response. Wireless multihop networking by deploying low-cost relays is a promising solution to effectively extend network services to people in the disrupted areas after large-scale disasters have occurred. It is of great importance to accurately estimate the population distribution after a disaster and, based on that, judiciously place a limited number of relay nodes to maximize the population coverage ratio. In this article we present an intelligent post-disaster networking approach by exploiting crowd dynamics. First, we present a long short-term memory based neural network to predict the spatio-temporal population distribution after a disaster. The neural network is trained by using a real crowd dynamics dataset collected during the Kumamoto earthquake in 2016. Then, based on the fine-grained population estimation result, we present three simple algorithms for the budget-constrained population-aware relay placement problem. The proposed approach is evaluated in real-world scenarios. The results show that the estimation error for population distribution is reduced by 56~69 percent compared to the regressive models, and a large proportion of the population could be efficiently covered by a limited number of relays.

A big data-driven dynamic estimation model of relief supplies demand in urban flood disaster

The dynamic relief supplies estimation for urban flood disaster still remains an important and challenging topic in emergency response. Traditional relief supplies estimation mainly depends on static census data rather than dynamic spatio-temporal population distribution, which may easily lead to a serious imbalance between supply and demand for relief resources. The emergence of big data originating from web mapping service, social media, crowdsourcing system and other methods provides alternative data sources to understand the dynamic distribution of urban population, which can help accurately estimating the relief supplies demand when urban flood occurs. This paper proposes a big data-driven dynamic estimation model of relief supplies demand, which merges the dynamic population distribution from Baidu big data and Multilayer Perceptron (MLP) neural network so as to improve the accuracy and timeliness. Taking Wuhan as an example, a specific day in summer flooding period was chosen to dynamically predict three kinds of relief materials. The results indicate that the proposed model is more feasible to estimate the relief supplies because it is integrated with MLP neural network trained by national-wide historical flood disaster cases and Baidu big data. The utility of Baidu big data for flood-affected population statistics has been shown to be more comprehensive than that of the traditional demographic methods. Thorough analysis exhibits the superiority of our proposed model with respect to accuracy and dynamics. This allows our proposed model to be more widely applied in pre- and post-disaster operations activities carried out by government and humanitarian aid organizations.

Population distribution modelling at fine spatio-temporal scale based on mobile phone data

ABSTRACTPopulation distribution modelling can benefit many different domains, for example, transportation, urban planning, ecology or emergency management. Information about the location and number of people in an affected area is crucial for decision-makers during emergencies and crises. Mobile phone data represents relatively reliable and time accurate information on real-time population distribution, movement and behaviour. In this study, we evaluate the spatio-temporal distribution of population derived from phone data of the selected pilot area (City of Brno, Czech Republic). Analysis is based on the dataset describing the estimated human presence (EHP) with two values – visitors and transiting persons. The temporal change of data is first analysed and further processed using two methodological approaches. First, the dasymetric method is used where the building geometry and technical attributes served as a target layer. Second, the results of building level analysis are transformed into a regular grid zone of both visitors and the general EHP. Resulting spatio-temporal patterns are compared to the census data. Results demonstrate how the proposed building level dasymetric approach can improve the spatial granularity of EHP. Potential use of proposed methodology within selected emergency situations is further discussed.

Revealing the relationship between spatio-temporal distribution of population and urban function with social media data

This research examines the spatio-temporal distribution of population in an urban environment and its relationship with urban functions using an unprecedented high-resolution and broad-coverage-crowd LBS dataset from Tencent, one of the biggest Internet companies in China. By examining the distribution of the population during different time periods, different urban morphologies are observed. The analysis of the spatio-temporal population distribution based on temporal entropy indicates that population distributions of employment, commercial, and scenic areas have larger temporal fluctuations than those in residential and mixed-use areas. As for the Spearman correlation coefficient between urban functions and temporal population distribution based on 300 × 300-m grids, it is then measured to uncover the underlying reason for this spatio-temporal distribution of population. The result demonstrates that as urban functions become more mixed, the temporal distribution of the population becomes more even. At a local scale, temporal population distribution in key areas shows that the location of people in a certain place is in accordance with human behavior. The population in employment-dominated areas shows large fluctuations on weekdays but is relatively evenly distributed on weekends. The population in commercial areas only peaks for several hours on both weekdays and weekends. Comparatively, mixed areas and large-scale residential communities accommodate a stable number of people at all times.

On the Spatiotemporal Traffic Variation in Vehicle Mobility Modeling

Several studies have shown the importance of realistic micromobility and macromobility modeling in vehicular ad hoc networks (VANETs). At the macroscopic level, most researchers focus on a detailed and accurate description of road topology. However, a key factor often overlooked is a spatiotemporal configuration of vehicular traffic. This factor greatly influences network topology and topology variations. Indeed, vehicle distribution has high spatial and temporal diversity that depends on the time of the day and place attraction. This diversity impacts the quality of radio links and, thus, network topology. In this paper, we propose a new mobility model for vehicular networks in urban and suburban environments. To reproduce realistic network topology and topological changes, the model uses real static and dynamic data on the environment. The data concern particularly the topographic and socioeconomic characteristics of infrastructures and the spatiotemporal population distribution. We validate our model by comparing the simulation results with real data derived from individual displacement survey. We also present statistics on network topology, which show the interest of taking into account the spatiotemporal mobility variation.

Impact of Temporal Population Distribution on Earthquake Loss Estimation: A Case Study on Sylhet, Bangladesh

To estimate human loss in an earthquake-prone area, it is necessary to analyze the role played by the spatiotemporal distribution of the area’s resident population. In order to evaluate earthquake impact, this article focuses on the spatiotemporal distribution of population and five scenario earthquakes that form the basis for loss estimation in the city of Sylhet, Bangladesh. Four temporal contexts (weekday, weekly holiday, the 30 days of Ramadan, and strike days) expand the more typical daytime and nighttime settings in which to examine hazard risk. The population distribution for every 2 hour interval in a day is developed for each type of day. A relationship between the occupancy classes and average space (persons per 100 m2) is used to distribute people in each building regardless of building locations. A total daytime and nighttime population is obtained for each building and the estimated nighttime population is used to model the population for four temporal scenarios in a year based on different factors and weights. The resulting data are employed to estimate population loss for each of the temporal and earthquake scenarios. This study used building-specific human vulnerability curves developed by the Central American Probabilistic Risk Assessment (CAPRA) to obtain possible loss of life estimates. The results reveal that there is a high positive correlation between the spatiotemporal distribution of population and the potential number of casualties.

Advancing tsunami risk assessment by improving spatio-temporal population exposure and evacuation modeling

Tsunamis are among the most destructive and lethal of coastal hazards. These are time-specific events, and despite directly affecting a narrow strip of coastline, a single occurrence can have devastating effects and cause massive loss of life, especially in urbanized coastal areas. In this work, in order to consider the time dependence of population exposure to tsunami threat, the variation of spatio-temporal population distribution in the daily cycle is mapped and analyzed in the Lisbon Metropolitan Area. High-resolution daytime and nighttime population distribution maps are developed using ‘intelligent dasymetric mapping,’ that is, applying areal interpolation to combine best-available census data and statistics with land use and land cover data. Workplace information and mobility statistics are considered for mapping daytime distribution. In combination with a tsunami hazard map, information on infrastructure, land use and terrain slope, the modeled population distribution is used to assess people’s evacuation speed, applying a geospatial evacuation modeling approach to the city of Lisbon. The detailed dynamic population exposure assessment allows producing both daytime and nighttime evacuation time maps, which provide valuable input for evacuation planning and management. Results show that a significant amount of population is at risk, and its numbers increase dramatically from nighttime to daytime, especially in the zones of high tsunami flooding susceptibility. Also, full evacuation can be problematic in the daytime period, even if initiated immediately after a major tsunami-triggering earthquake. The presented approach greatly improves tsunami risk assessment and can benefit all phases of the disaster management process.

SPATIO-TEMPORAL DISTRIBUTION OF POPULATION ON WEEKDAYS AND HOLIDAYS FOR ESTIMATING HUMAN DAMAGES FROM DEVASTATING EARTHQUAKE

Understanding characteristics of spatio-temporal distribution of population, which varies according to weekdays or holidays, is one of the most important issues in the field of urban disaster prevention planning. However, the existing person trip data based on weekdays cannot be used for estimating the spatio-temporal distribution of population on holidays. In this study we propose a method to convert the existing person trip data from weekday-base to holiday-base, and examine the differences in the spatio-temporal distribution of population. We also attempt to compare the number of people who might lose their lives under collapsed buildings on weekdays and holidays. Some new findings are demonstrated by numerical examples using the data about an established city area in Tokyo Metropolitan area.

SPATIO-TEMPORAL DISTRIBUTION OF RAILROAD USERS FOR DISASTER PREVENTION PLANNING

Considering provisions for human damage due to a large earthquake, it is important to understand the characteristics of the spatio-temporal distribution of population in urban area. In this paper, we construct a model which simulates, on geographic information systems, the route choice behavior and transfer behavior of railroad users. The spatiotemporal distribution of users is estimated by applying the model to the Tokyo metropolitan area, using the data taken from a person-trip survey. Some numerical examples using the proposed model are shown for detailed disaster prevention planning.

SPATIO-TEMPORAL DISTRIBUTION OF AUTOMOBILE USERS FOR DISASTER PREVENTION PLANNING

In the field of urban disaster prevention planning, it is important to grasp the characteristics of spatio-temporal distribution of population for considering provisions for human damage by a great earthquake. In this paper, we construct a model that simulates route choice behavior of automobile users on geographic information systems by using the data taken from a person trip survey and a road traffic census. We estimate spatio-temporal distribution of automobile users in Tokyo, and demonstrate some analyses on urban disaster prevention planning.

ESTIMATION OF SPATIO-TEMPORAL DISTRIBUTION OF POPULATION USING DETAILED DIGITAL MAP

In order to precisely estimate human damage due to a large earthquake, it is important to grasp the spatio-temporal distribution of population in an urban area. This paper aims to construct models that describe the spatio-temporal distribution of population. The unknown parameters of the models are estimated using the data taken from a person trip survey and GIS data of Tokyo Metropolitan area. The characteristics of the spatio-temporal distribution of population are discussed and the accuracy of the models is statistically examined. Some numerical examples demonstrate that the proposed models can be applied to various researches in the field of disaster prevention planning.

Spatiotemporal population distributions and their implications for species coexistence in a variable environment

A population experiences environmental variation both directly, through effects on life history parameters such as fecundity, and indirectly, through effects on the population distributions of competitors and thus on the distribution of competition. Which spatial and temporal scales of environmental variation most influence the coexistence of two species thus depends in part on the degree to which the resident population responds to different scales of variation. In this paper, I calculate an approximation for a spatiotemporal population distribution as the result of a filter function convolved with the environmental variation. I find that there is no straightforward connection between spatial or temporal scales inherent to an organism's life history, such as mean lifetime or dispersal distance, and the population's sensitivity to variation at different scales. Rather, life history traits interact sensitively with the way environmental variation affects the organism. I comment on the implications for variation-mediated coexistence.