Distance Cost Research Articles

Construction and optimization of green space ecological networks in urban fringe areas: A case study with the urban fringe area of Tongzhou district in Beijing

Rapid urbanization has engendered increased fragmentation of urban green spaces combined with declining green space connectivity. This situation is significant in urban fringe areas wherein rural areas transition to urban spaces. Therefore, it is important to understand how to scientifically and rationally construct green space ecological networks, connect fragmented habitats, and protect biodiversity and regional ecological security. Here, we used geographic information system and remote sensing technology to address the ecological sources via morphological spatial pattern analysis and ecological connectivity index analysis methods. The minimum cost distance method was used to generate a potential corridor. The green space ecological network system of the study area was constructed based on its green space landscape pattern. Qualitative and quantitative analysis provided targeted recommendations for green space ecological network optimization in the study area. The results showed that in the study area, the level of the fragmentation was high. Although the number of urban green space patches was significant, the area of the patches was small. Despite the potential corridor not covering the study area evenly, the optimized green space corridor increased the secondary ecological source area. The network system for green space corridors comprised water corridors, road corridors, and ecological restoration sites.

A Two-Stage DSS to Evaluate Optimal Locations for Bioenergy Facilities

Research Highlights: A set of 128 potential bioenergy facility locations is established and evaluated based on the transport cost to select optimal locations. Background and Objectives: The identification of optimal facility locations to process recovered forest biomass is an important decision in designing a bioenergy supply chain at the strategic planning level. The result of this analysis can affect supply chain costs and the overall efficiency of the network, due to the low density and dispersed nature of forest biomass and the high costs associated with its logistics operations. In this study, we develop a two-stage decision support system to identify the optimal site locations for forest biomass conversion based on biomass availability, transport distance and cost. Materials and Methods: In the first stage, a GIS-based analysis is designed to identify strategic locations of potential bioenergy sites. The second stage evaluates the most cost-effective locations individually using a transportation cost model, based on the results from stage one. The sensitivity of inputs, such as maximum allowable transport cost, the distance of transport and their relations to the profit balance, and changes in fuel price are tested. The method is applied to a real case study in the state of Queensland, Australia. Results and Conclusions: The GIS analysis resulted in 128 strategic candidate locations being suggested for bioenergy conversion sites. The logistics analysis estimated the optimal cost and transportation distance of each one of the locations and ranked them according to the overall performance between capacities of 5 and 100 MW.

A stochastic prepositioning model for distribution of disaster supplies considering lateral transshipment

This paper focuses on addressing uncertainties in disasters when considering lateral transshipment opportunities for pre-positioning relief supplies. To deal with uncertain demands the problem is formulated as a two-stage stochastic programming model, which decides simultaneously on the locations of relief facilities and the allocations of relief supplies to demand nodes. Meanwhile, different damage levels caused by disasters are considered and reflected by a survival rate of usable stocked relief items. Multiple types of supplies with various priorities, values and spaces are explored. A real-world case study based on the Gulf Coast region of the United States is conducted to illustrate the application of the developed model. By comparison with the direct shipment solution, the lateral transshipment solution is demonstrated to be more cost-effective and flexible. The sensitivity analysis of out-of-stock penalty cost and maximum travel distance provides managerial insights for relief agencies.

LoRa-Based Smart IoT Application for Smart City: An Example of Human Posture Detection

Scientists have explored the human body for hundreds of years, and yet more relationships between the behaviors and health are still to be discovered. With the development of data mining, artificial intelligence technology, and human posture detection, it is much more possible to figure out how behaviors and movements influence people’s health and life and how to adjust the relationship between work and rest, which is needed urgently for modern people against this high-speed lifestyle. Using smart technology and daily behaviors to supervise or predict people’s health is a key part of a smart city. In a smart city, these applications involve large groups and high-frequency use, so the system must have low energy consumption, a portable system, and a low cost for long-term detection. To meet these requirements, this paper proposes a posture recognition method based on multisensor and using LoRa technology to build a long-term posture detection system. LoRa WAN technology has the advantages of low cost and long transmission distances. Combining the LoRa transmitting module and sensors, this paper designs wearable clothing to make people comfortable in any given posture. Aiming at LoRa’s low transmitting frequency and small size of data transmission, this paper proposes a multiprocessing method, including data denoising, data enlarging based on sliding windows, feature extraction, and feature selection using Random Forest, to make 4 values retain the most information about 125 data from 9 axes of sensors. The result shows an accuracy of 99.38% of extracted features and 95.06% of selected features with the training of 3239 groups of datasets. To verify the performance of the proposed algorithm, three testers created 500 groups of datasets and the results showed good performance. Hence, due to the energy sustainability of LoRa and the accuracy of recognition, this proposed posture recognition using multisensor and LoRa can work well when facing long-term detection and LoRa fits smart city well when facing long-distance transmission.

Obstacle avoidance path planning of unmanned submarine vehicle in ocean current environment based on improved firework-ant colony algorithm

In order to solve the unmanned underwater vehicle two-dimensional autonomous path planning problem in the environment affected by ocean current and obstacles, this paper applies the improved Fireworks-Ant Colony Hybrid Algorithm to solve it. Firstly, a two-dimensional Lamb vortex ocean current environment model with randomly distributed obstacles is established, and the circular obstacle is equivalent to a square grid. Then, the mathematical model of path planning is established considering the energy consumption cost, navigation time cost and navigation distance cost. Finally, the improved fireworks-ant colony hybrid algorithm is applied to solve the nonlinear optimization problem, and this algorithm is compared with the basic ant colony algorithm for simulation experiments in the four different marine environments. The experimental results show that this algorithm can quickly find the global optimal solution, and the more complex the environment, the more obvious its advantages. The algorithm proposed in this paper provides a new way for autonomous path planning of underwater vehicles.

Research on the Optimization Method of Regional Logistics Distribution Integration Based on Intelligent Information Processing

At present, the integration method of logistics distribution does not exchange regional logistics distribution information, the related logistics distribution information is not timely understood, and the integration effect of regional logistics distribution is poor, resulting in long distribution time, high distribution cost and small distribution scope. Therefore, the intelligent information processing technology is introduced to optimize the regional logistics distribution integration method. This paper established a regional logistics distribution integration mechanism which is able to process logistics distribution integration information through hierarchical authorization, transform the business data between the upstream and downstream enterprises in the supply chain of the regional logistics canter and integrate vehicle resources and human resources to optimize regional logistics distribution based on intelligent information processing. Taking the distribution time, distribution cost and distribution distance as experimental indicators, the experiment is carried out. The results show that the logistics distribution time, cost and distance of the proposed integrated optimization method are better than those of the traditional methods.

Occurrence of apomictic conspecifics and ecological preferences rather than colonization history govern the geographic distribution of sexual Potentilla puberula.

The geographic distribution of sexual‐apomictic taxa (i.e., comprising individuals usually reproducing either sexually or asexually via seeds) is traditionally thought to be driven by their ecological preferences and colonization histories. Where sexuals and apomicts get into contact with each other, competitive and reproductive interactions can interfere with these factors, an aspect which hitherto received little attention in biogeographic studies. We disentangled and quantified the relative effects of the three factors on the distribution of tetraploid sexuals in Potentilla puberula in a latitudinal transect through the Eastern European Alps, in which they are codistributed with penta‐, hepta‐, and octoploid apomictic conspecifics. Effects were explored by means of binomial generalized linear regression models combining a single with a multiple predictor approach. Postglacial colonization history was inferred from population genetic variation (AFLPs and cpDNA) and quantified using a cost distance metric. The study was based on 235 populations, which were purely sexual, purely apomictic, or of mixed reproductive mode. The occurrence of apomicts explained most of the variation in the distribution of sexuals (31%). Specifically, the presence of sexual tetraploids was negatively related to the presence of each of the three apomictic cytotypes. Effects of ecological preferences were substantial too (7% and 12% of the total variation explained by ecological preferences alone, or jointly with apomicts’ occurrence, respectively). In contrast, colonization history had negligible effects on the occurrence of sexuals. Taken together, our results highlight the potentially high impact of reproductive interactions on the geographic distribution of sexual and apomictic conspecifics and that resultant mutual exclusion interrelates to ecological differentiation, a situation potentially promoting their local coexistence.

Effects of spatial distance, physical barriers, and habitat on a stream fish metacommunity

Metacommunity structure depends on environmental and spatial factors. Stream fishes are constrained to disperse within dendritic networks and waterfalls and other barriers add resistance to species dispersal. We evaluated the importance of environmental and spatial effects on fish metacommunity composition, adopting different models of spatial distance: overland, watercourse, resistance (physical barriers), cost distance (watercourse resistance), and asymmetrical dispersal (considering flow direction). Fishes were sampled in 31 riffle sites of a small subtropical basin of southern Brazil (~ 250 km2 effective study area). Using redundancy analysis (RDA) and variance partitioning, we found the best model was the one including flow direction weighted by watercourse distance plus environmental variables (overall adj-R2 = 0.42). However, the explanation of fish composition was greatly improved by weighting spatial distance by stream channel sinuosity or height of barriers. Reach slope and width were the two significant environmental variables. Our study supports that dispersal, in addition to environmental factors, affects stream fish metacommunity even in a watershed of small spatial extent. Detecting spatial effects in a small watershed depends on considering realistic watershed features that affect fish dispersal in drainage networks such as waterfalls and other physical barriers, and distance along the watercourse and flow direction.

Viticulture in the Laetanian Region (Spain) during the Roman Period: Predictive Modelling and Geomatic Analysis

Geographic information system (GIS)-based predictive modelling is widely used in archaeology to identify suitable zones for ancient settlement locations and determine underlying factors of their distribution. In this study, we developed predictive models on Roman viticulture in the Laetanian Region (Hispania Citerior-Tarraconensis), using the location of 82 ancient wine-pressing facilities or torcularia as response variables and 15 topographical and 6 socio-economic cost distance datasets as predictor variables. Several predictor variable subsets were selected either by expert knowledge of similar studies or by using a semi-automatization algorithm based on statistical distribution metrics of the input data. The latter aims at simplifying modelling and minimizing the necessity of a priori knowledge. Both approaches predicted the distribution of archeological sites sufficiently well. However, the best prediction performance was obtained by an expert knowledge model utilizing a predictor variable combination based on recommendations on viticulture by Lucius Junius Moderatus Columella, the prominent ancient Roman agronomist. The results indicate that the accessibility of a location and its connectivity to trade routes and distribution centres, determined by terrain steepness, was decisive for the settlement of viticultural facilities. With the knowledge gained, the ancient cultivated area and number of wine-pressing facilities needed for processing the vineyard yields were extrapolated for the entire study region.

Accurate and Efficient Calculation of Three-Dimensional Cost Distance

Cost distance is one of the fundamental functions in geographical information systems (GISs). 3D cost distance function makes the analysis of movement in 3D frictions possible. In this paper, we propose an algorithm and efficient data structures to accurately calculate the cost distance in discrete 3D space. Specifically, Dijkstra’s algorithm is used to calculate the least cost between initial voxels and all the other voxels in 3D space. During the calculation, unnecessary bends along the travel path are constantly corrected to retain the accurate least cost. Our results show that the proposed algorithm can generate true Euclidean distance in homogeneous frictions and can provide more accurate least cost in heterogeneous frictions than that provided by several existing methods. Furthermore, the proposed data structures, i.e., a heap combined with a hash table, significantly improve the algorithm’s efficiency. The algorithm and data structures have been verified via several applications including planning the shortest drone delivery path in an urban environment, generating volumetric viewshed, and calculating the minimum hydraulic resistance.

Impact of cost distance and habitat fragmentation on the daily path length of Rhinopithecus bieti.

An understanding of primate movement patterns in response to natural and anthropogenically induced changes in habitat heterogeneity, food availability, and plant species distribution is essential for developing effective management and conservation programs. Therefore, from July 2013 to June 2014, we examined the effects of landscape configuration on the ranging behavior (daily path length, DPL) of the Endangered Yunnan snub-nosed monkey (Rhinopithecus bieti) in the Baimaxueshan National Nature Reserve (27°34′N, 99°17′E) in Gehuaqing, China. Given the extreme difficulties in following the study group across high altitude mountainous terrain across an elevation of 2,500–4,000 m, we were only able to collect DPL using 3-4 GPS points per day on 21 individual days. We found that R. bieti traveled the shortest DPL in winter (1,141.31 m), followed by spring (2,034.06 m) and autumn (2,131.19 m). The cost distance, a statistical tool designed to estimate the difficulty of a species moving across its distributional range, was lowest in autumn (205.47), followed by spring (225.93) and winter (432.59) (one-way ANOVA: F = 3.852, P = 0.026, df = 2). The habitat fragmentation index (HFI), which measures the density of forest patches, indicated areas visited in the winter were more fragmented (HFI = 2.16) compared to spring (HFI = 1.83) or autumn (HFI = 1.3). Although our results should be considered preliminary, they suggest that both the availability of suitable travel routes and habitat fragmentation, driven by high-intensity human disturbance, constrain the movement of R. bieti. We found that undisturbed areas of the bands’ range contained a high density of lichens, which represent a nutritious and abundant and year-round food source for Yunnan snub-nosed monkeys. In order to protect this Endangered species, we recommend that researchers construct detailed maps of landscape heterogeneity, particularly habitat connectivity, forest fragmentation, and seasonal variation in the location of major food patches in order to better understand and mitigate the effects of seasonal habitat change on patterns of R. bieti habitat utilization and population viability.

Probabilistic Fire Risk Framework for Optimizing Construction Site Layout

Purpose: This paper aims to develop an efficient model able to reduce catastrophic consequences and the significant number of victims resulting from fires at construction sites. The paper proposes probabilistic modeling aimed to minimize the probability of failure of a construction site. Methodology: The developed model in this paper consists of modeling fire hazards, the vulnerability of the potential targets, and the risk within construction sites. The optimization algorithm called “differential evolution” is used in order to determine the optimal site layout, which is characterized by having the smallest overall probability of failure. A numerical simulation is performed to delineate an appropriate probability density function of the failure of the site. In addition, a geographic information system (GIS) is used to display the spatial variability of fire risk on a construction site. Findings: The paper provides an efficient model to enhance site layout planning and assign locations for supporting temporary facilities at appropriate positions within a construction site. The model is examined through applying it on a simple case study containing numerous facilities. All these facilities are considered vulnerable targets and some of them are potential fire hazards, with different intensity values. Value: Most of the previous research focuses on travel cost distance in developing site layout planning models. This paper fulfills the development of a valuable model able to generate an optimized construction site layout by minimizing the probability of failure of the whole site. It will assist the decision makers and the risk managers in identifying the riskiest zones on a construction site.

Smart home autonomous water management system for agricultural applications

PurposeIn recent days, there is a huge loss in the income of farmers due to the reasons such as low water lever and increased pesticide attack. Therefore, the purpose of this paper is to establish an efficient reliable low-cost information gathering Reliable Low-Cost Information Gathering Protocol (RLCIG) protocol for agricultural water irrigation using optimal clustering and path selection technique where the RCIG protocol wrinkles the expedient statistics about the moisture and temperature of the soil and it will be installed few inches below the pipeline. Thereafter, the congregated data will augment the irrigation of water by using a decision-making algorithm.Design/methodology/approachThe projected model has been inscribed mathematically by underlying the wireless sensor networks (WSN) framework with deliberation of contemporary challenges. Furthermore, the energy, cost and expanse optimization framework in the WSN framework is presented. The projected technique has been tested using network simulator and the results are also integrated MATLAB.FindingsRecently, for efficacious management in the field of agriculture, the WSN has been successfully assimilated. This instigation accomplishes the irrigation management in terms of energy, cost and communication distance. The simulation result shows that the proposed model yields better results in terms of both the transmission range and cost with efficient lifetime improvement in comparisons with existing techniques.Originality/valueAgriculture is the need of the time whatever invention happens in the scientific world without food production no lives survive on the earth, hence, the scientific invention should also focus on agriculture, in this contrast, the authors have proposed an efficient low-cost information gathering (RCIG) protocol for agricultural water irrigation using optimal clustering and path selection technique.

Mapping of Tsunami disaster evacuation pathways based on Tsunami altitude scenario using Network Analyst Method (case study: Palu City, Central Sulawesi)

Central Sulawesi's Palu city is one of the regions in Indonesia that is vulnerable to tsunami disasters. A tsunami disaster is a disaster that can cause many victims of both casualties and materials. One of the disaster mitigation to reduce the victim's fall is by evacuating the community to a safer place, to evacuate the population required evacuation routes that can direct people to evacuation sites. The purpose of this research is to make a tsunami runoff using cost distance and to make tsunami evacuation and use a Network analyst on Arcgis 10.4 software. The most affected regional analysis is the Northern Palu sub-district with a total area of 8.643528 km2, and there are 93 evacuation routes with 92 evacuation points. The longest evacuation route is 4,297 M with a travel time of 27.6 minutes with running and the shortest evacuation route of 96 m with a travel time of 0.6 minutes by running.

Modeling pedestrian evacuation for near-field tsunamis fusing ALCD and agent-based approaches: A case study of Rincón, PR

Phenomena like tsunamis cannot be predicted, and simulation models are continuously being used in an attempt to understand how individuals will react in an emergency evacuation event. More specifically, pedestrian evacuation simulation can be used to better understand how pedestrians will escape an at-risk area. This work presents a pedestrian evacuation model (PEM) for the municipality of Rincón, Puerto Rico. This PEM provides insight on the vulnerability of Rincón to tsunamis using an iterative, optimization-based algorithm that fuses anisotropic least cost distance (ALCD) and agent-based (AB) approaches. This work advances the ALCD literature by providing a computationally-feasible PEM implementation capable of: (1) strategically initializing the location of pedestrians based on infrastructure and land use considerations, (2) managing individual as well as group-based evacuation, (3) assigning evacuation speeds and responses using probability and machine learning approaches, and (4) penalizing evacuation times using fatigue and reaction delays. The results of this PEM will provide emergency managers with a more realistic depiction of the time to reach safety using a systematic framework. Results, based on the worst-case scenario, indicate that 32.44% of the population in the tsunami evacuation zone (TEZ) would reach safety under 5 min, 41.96% between 5 and 15 min, and 25.60% would require more than 15 min. Unless effective public policy and mitigation strategies take place, the aftermath of a tsunami that inundates the Rincón coastline in less than 5 min could amount to up to 3312 casualties or 68% of the population in the TEZ.

Identifying Urban Road Black Spots with a Novel Method Based on the Firefly Clustering Algorithm and a Geographic Information System

With the rapid development of urban road traffic, there are a certain number of black spots in an urban road network. Therefore, it is important to create a method to effectively identify the urban road black spots in order to quickly and accurately ensure the safety of residents and maintain the sustainable development of a city. In this study, a GIS (geographic information system) and the Firefly Clustering Algorithm are combined. On the one hand, a GIS can accurately extract the distance between accident points through its spatial analysis function, overcoming the disadvantage of the accident data not usually including the specific location data. On the other hand, the Firefly Clustering Algorithm can be used to comprehensively extract the characteristics of accident points, which is particularly suitable for the identification of black spots. In order to verify the feasibility of the proposed method, this research compares the identification effect between the OD (origin–destination) cost distance calculated by GIS and the Euclidean distance. The results show that the Euclidean distance is smaller than the OD cost distance and that the accident search method based on the Euclidean distance can overestimate the number of black spots, especially for intersections. Therefore, the proposed method based on the Firefly Clustering Algorithm and GIS can not only contribute to identifying urban road black spots but also plays an auxiliary role in reducing urban road crashes and maintaining sustainable urban development.

Site Suitability Analysis of Solar Energy Plants in Stony Wasteland Area: A Case Study of Trans-Yamuna Upland Region, Allahabad District, India

Installation of solar power plant units in wasteland areas is not only helpful for land resource management but also expected to create some job opportunities for the local population, infrastructure development, water resource conservation and energy revenue generation. In the present study, the suitable region for installation of the solar PV power plant is identified through the AHP method in combination with cost distance function by considering existing infrastructure in the Trans-Yamuna upland region of Allahabad District. Research finding indicates that it has an insolation value of 1194–1859 h of the sunshine and 498.16–703.73 kW h/m2/day of solar radiation. Thus, the total capacity of the study region is 6263.71 MW (126.74 km2) if 1 km2 area has installed capacity of 49.42 MW, which is further validated with Jalaun Solar Power Plant (Uttar Pradesh) and Bhadla Power Plant (Rajasthan) having 50 MW and 1365 MW solar capacity with an area of 1.012 km2 and 40 km2, respectively. The actual project cost ranges from ₹ 0.04 to 0.1 billion, and the range of difference between the actual and estimated cost is ₹ ± 0.03 billion, while the estimated project cost is ₹ 0.06 billion. Thus, it shows a good comparison agreement between the actual and estimated project cost, and further the present methodology could be useful to install the solar PV power plant in other regions as well.

Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt

The probabilistic mapping of landslide occurrence at a high spatial resolution and over a large geographic extent is explored using random forests (RF) machine learning; light detection and ranging (LiDAR)-derived terrain variables; additional variables relating to lithology, soils, distance to roads and streams and cost distance to roads and streams; and training data interpreted from high spatial resolution LiDAR-derivatives. Using a large training set and all predictor variables, an area under the receiver operating characteristic (ROC) curve (AUC) of 0.946 is obtained. Our findings highlight the value of a large training dataset, the incorporation of a variety of terrain variables and the use of variable window sizes to characterize the landscape at different spatial scales. We also document important variables for mapping slope failures. Our results suggest that feature selection is not required to improve the RF modeling results and that incorporating multiple models using different pseudo absence samples is not necessary. From our findings and based on a review of prior studies, we make recommendations for high spatial resolution, large-area slope failure probabilistic mapping.

Identifying and evaluating poverty using multisource remote sensing and point of interest (POI) data: A case study of Chongqing, China

Poverty is a chronic worldwide dilemma that can seriously hamper human sustainable development, which is closely related to economic growth, environmental protection, ecological restoration, and sustainable utilization of resources. Accurately and effectively identifying and evaluating poverty has become an important prerequisite for allowing Chinese governments to make reasonable poverty reduction and alleviation policies. Thus, using Chongqing as a study area, the purpose of this study was to analyze poverty from multiple viewpoints based on multiple data sources. First, a comprehensive poverty index (CPI) was developed by combining nighttime light data, the digital elevation model (DEM), the normalized differential vegetation index (NDVI), and point of interest (POI) data to map poverty at a 500-m spatial resolution. Then, the performance of the CPI was validated with poverty-stricken villages, Google Earth images, and the multidimensional poverty index (MPI). Finally, spatial autocorrelation analysis was used to explore the spatial distribution of poverty across county and town levels. The results revealed that the CPI could provide an effective way of identifying the spatial distribution of poverty when compared with three validated indexes. Most of the rich counties were in the center of Chongqing, whereas the poor counties were located in the northeast and southeast of Chongqing. The Global Moran’s I index showed that there were significantly positive spatial autocorrelations of poverty, and that the spatial autocorrelation of poverty was more significant at the town level compared to the county level. Among the selected factors, the POI cost distance was the most import factor for assessing poverty. Our study will be valuable for providing scientific references for the government to implement precise poverty alleviation methods with differentiated policies in China.

Effect of spatial distribution and number of raw material collection locations on the transportation costs of biomass thermal power plants

Biomass is disadvantageous as it has low energy density and poor fluidity. High transportation costs lead to bottlenecks in the development of biomass. This paper aims to find a spatial distribution model of biomass collection points to effectively control the transportation cost of raw materials. Considering a variety of influencing factors to assess the potential of forestry, agricultural biomass, and domestic waste; and to establish transport cost models with the help of ArcGIS software, the most economical location of biomass thermal power plant is determined and the shortest path and transportation cost from resource point to biomass thermal power plant are obtained. The analysis results show that the transportation cost for a single vehicle has a positive linear relationship with the transportation distance. The transportation cost rapidly increases when the transportation distance reaches 90 km. The total transportation cost and transportation distance within the distance range are positively correlated and follow an S curve. Setting a transportation distance of 22 km as the threshold for the first level raw material collection locations and 63 km as the second level for raw material collection locations. effectively lowered the transportation costs for biomass.