Actual Map Research Articles

Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model

Urban growth and land-use change are a few of many puzzling factors affecting our future cities. Creating a precise simulation for future land change is a challenging process that requires temporal and spatial modeling. Many recent studies developed and trained models to predict urban expansion patterns using Artificial Intelligence (AI). This study aims to enhance the simulation capability of Cellular Automata Markov Chain (CA-MC) model in predicting changes in land-use. This study integrates the Artificial Neural Network (ANN) into CA-MC to incorporate several driving forces that highly impact land-use change. The research utilizes different socio-economic, spatial, and environmental variables (slope, distance to road, distance to urban centers, distance to commercial, density, elevation, and land fertility) to generate potential transition maps using ANN Data-driven model. The generated maps are fed to CA-MC as additional inputs. We calibrated the original CA-MC and our models for 2015 cross-comparing simulated maps and actual maps obtained for Irbid city, Jordan in 2015. Validation of our model was assessed and compared to the CA-MC model using Kappa indices including the agreement in terms of quantity and location. The results elucidated that our model with an accuracy of 90.04% substantially outperforms CA-MC (86.29%) model. The improvement we obtained from integrating ANN with CA-MC suggested that the influence imposed by the driving force was necessary to be taken into account for more accurate prediction. In addition to the improved model prediction, the predicted maps of Irbid for the years 2021 and 2027 will guide local authorities in the development of management strategies that balance urban expansion and protect agricultural regions. This will play a vital role in sustaining Jordan's food security.

Subpixel matching method for remote sensing image of ground features based on geographic information

AbstractIn order to solve the problem of large error of subpixel matching and poor filtering effect in traditional methods, a subpixel matching method based on geographical information is proposed. First, the image quality of the remote sensing image is enhanced by the image enhancement method based on light energy allocation. Then, the boundary geographic information is extracted by the improved thresholding segmentation algorithm based on histogram exponential convex hull for the enhanced remote sensing image of ground features. Based on the extracted geographic information, by matching the boundary image with the function measurement method, the center coordinates of the image block corresponding to the actual measurement map and the reference submap which achieve the best matching are obtained. According to the corresponding geometric transformation relationship between the measured image and the reference image, the subpixel matching of the measured remote sensing image and the reference image can be carried out under the least-square-error criterion. The experimental results show that the enhancement performance and noise filtering performance of the proposed method are better than those of the same type of method, the matching residual is very small, the matching accuracy is high, and the application value is significant.

Land Characteristics And Land Evaluation For Development On Other Use Area Rice Fertilizer Plants In District Salak Regency Pakpak Bharat

The opening of new areas should be investigated so that land resources can be used to determine the suitability of the land on certain uses so that APL land use (Other Area of Use) in Salak Sub-district needs to be supported with information on land suitability. The objectives of this study were to evaluate the characteristics of the APL land suitability level, to analyze improvement efforts to increase productivity and to create an actual land suitability map and potential land suitability for Wetland rice on APL land in Kecamatan Salak. The research method used the survey method. Unit of land-based on land map. Research data were analyzed using the matching method. Characteristics of the land in Salak District both physical and chemical properties have very diverse characteristics that can be inhibiting or supporting factors in the development of agricultural crops, especially the development of rice paddy. Temperature regime limiting factor (tc), rooting medium (rc), erosion hazard (eh). Improvement efforts that can be done are pelumpuran, giving of organic material, making of the terrace, and fertilizing. Temperature regime can not be repaired

Cell and nucleus refractive-index mapping by interferometric phase microscopy and rapid confocal fluorescence microscopy.

We present a multimodal technique for measuring the integral refractive index and the thickness of biological cells and their organelles by integrating interferometric phase microscopy (IPM) and rapid confocal fluorescence microscopy. First, the actual thickness maps of the cellular compartments are reconstructed using the confocal fluorescent sections, and then the optical path difference (OPD) map of the same cell is reconstructed using IPM. Based on the co-registered data, the integral refractive index maps of the cell and its organelles are calculated. This technique enables rapidly measuring refractive index of live, dynamic cells, where IPM provides quantitative imaging capabilities and confocal fluorescence microscopy provides molecular specificity of the cell organelles. We acquire human colorectal adenocarcinoma cells and show that the integral refractive index values are similar for the whole cell, the cytoplasm and the nucleus on the population level, but significantly different on the single cell level.

Integrating cellular automata Markov model to simulate future land use change of a tropical basin.

Predicting land use change is an indispensable aspect in identifying the best development and management of land resources and their potential. This study used certified land-use maps of 1997, 2006, and 2015 combined with ancillary data such as road networks, water bodies and slopes, obtained from the Department of Agriculture and the Department of Surveying and Mapping in Malaysia, respectively. The prediction of future land use changes in the Selangor River basin in Malaysia was performed using the Cellular Automata Markov model. The transition probability matrices were computed using the land use conditions of the periods 1997-2006, 2006-2015, 1997-2015. The performance of the model was very good in its overall ability to simulate the actual land use map of 2015, with the index values of 0.92% and 0.97%, respectively for Kappa for no information and Kappa for grid-cell level location which indicated the reliability of the model to successfully simulate land use changes in 2024 and 2033. Based on the expected results, the future urban area will grow faster (33%) over the next two decades, leading to a decline in forest area that is expected to lose 8% of its total space during these periods. Agricultural land will increase to 4%, while water bodies will change slightly increasing to 1%, and other areas of land use will likely become reservoirs of water, topsoil or new green spaces shrinking at 30%. Given the importance of knowledge of future land use in addressing the problems of uncontrolled development on environmental quality, this study could be valuable for land use planners of the river basin largely covered by natural forest. The study however, suggests future research to integrate geospatial techniques with biophysical and socio-economic factors in simulating land use trends.

Actual and Potential Land Productivity of Some Soils of Sohag-Red Sea Road Sides, Eastern Desert, Egypt

Egyptian Government focuses on development projects especially in new lands such as Sohag-Red Sea road to improve tourism and agricultural activities. This study aims to assess the actual and potential land productivity. Seventeen soil profiles were chosen depending on the six mapping units of the study area. Soil profiles were drilled and soil samples were collected from each horizon. Nine land characteristics were measured/estimated viz. soil moisture content (H), drainage (D), depth (P), texture (T), soluble salts content (S), average nutrient content (N), organic matter content (O), cationic exchange capacity (A) and reserves weatherable minerals (M). Soil samples were analyzed for their mentioned parameters using the standard methods of soil analysis. Accordingly, land productivity (PI) and potentiality (P⸌I) indices were calculated for all studied soil profiles. The results revealed that actual land productivity of the studied area is extremely poor and can be enhanced 17 times by improving soil moisture content, texture, and organic matter content as the most important controlling-factors. Actual and potential land productivity maps were generated using Arc GIS 10.1 software. These results may help decision-makers for new lands reclamation planning and better agricultural production.

Maps Matter. The 10/40 Window and Missionary Geography

Abstract The 10/40 Window map is used by evangelical missionary societies to promote mission in Northern Africa, the Middle East and South East Asia. It has been widely popular among Christians worldwide, but has also suffered sustained criticism. The map itself, however, has received no scholarly attention. This article investigates the 10/40 Window map through the lens of the concept of territoriality. Using insights from the field of critical cartography, it argues that the map is pivotal in directing missionary zeal, but that in turn it has also reshaped missionary thinking. This is so because the actual map’s metageographical proportions, its cartographic language and the accompanying rhetoric communicate several novel key propositions about mission. The overall argument of this article is that maps are not innocuous illustrations, but indeed that maps matter a great deal and that missionary geography should be taken seriously.

A Method to Improve the Flood Maps Forecasted by On-Line Use of 1D Model

Forecasting floods in urban areas during a heavy rainfall is the aim of every early warning system. 2D-models produce the most accurate flood maps, but they are practically useless as quasi real-time tools, because their run times are comparable to times of propagation of floods. Run times of 1D-model are of tens of seconds, but their predictions lack accuracy and many useful indicators of flood severity. Our aim is the identification of the 2D-model map that is more similar to the actual map, chosen among those simulated off-line. To this aim, we produce a rough flood map of the occurring event, through a quasi real-time simulation of the rainfall-runoff using a 1D-model. Then we apply an original method, named “ranking approach”, to perform the best matching. This method is applied to the Corace torrent (Calabria, Southern Italy), using 17 synthetic hyetographs to simulate the same number of rainfall-runoff events, using 1D (SWMM) and 2D (MIKE) models. The method proves to be effective in 65% of the cases, while in 82% of cases (i.e., for 14 cases out 17), the event produced by the same ietograph falls within the third rank.

Focusing of Tsunami Energy at Imwon Port

Lee, K.-J. and Cho, Y.-S., 2020. Focusing of tsunami energy at Imwon Port. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 247–251. Coconut Creek (Florida), ISSN 0749-0208.In the last few decades, several catastrophic tsunamis triggered by undersea earthquakes have occurred and struck not only neighboring but also distant coastal communities around the world. These tsunamis have caused a large number of human casualties and severe property damage. In this study, a geographical feature of the Imwon Port located at eastern coast of the Korean Peninsula, is investigated by studying a group of historical and virtual tsunamis caused by 3 historical and 11 virtual undersea earthquakes suggested by KEDO (1999). In addition, the effectiveness of the wave ray model is then examined by estimating tsunami heights at specific positions. Finally, the location of wave energy focusing is predicted by estimating tsunami heights and wave ray tracing, and whether the location is right is reconfirmed by the actual particle velocity vector map. The results of this study shall be used by a civil defense authority to mitigate the tsunami hazards in the case of a real tsunami attack.

Selection Method of Dendritic River Networks Based on Hybrid Coding for Topographic Map Generalization

As the coding of a dendritic river system can be used to represent the stream order and spatial-structure of a river network, it is always used in river selection, which is a key step in topographic map generalization. There are two categories of conventional hydrological coding systems, one is the top-down approach, and the other is the bottom-up approach. However, the former does not accurately reflect the hierarchies of a dendritic river network, which is produced by catchment relationships, and it is not appropriate for the stream selection of river networks with uniform distributions of tributaries. The latter cannot directly indicate the subtree depth of a stream, and it is not favorable to stream selection of river systems that have topologically deep structures. Therefore, a selection method for dendritic river networks based on hybrid coding is proposed in this paper. First, the dendritic river network is coded through classical top-down Horton coding. Second, directed topology trees are constructed to organize the river network data, and stroke connections are calculated to code the river network in the bottom-up approach. Third, the river network is marked through hybrid usage of the top-down approach and bottom-up approach, and based on the spatial characteristics of the river network, the river network is classified into three kinds of subtrees: deep branch, shallow branch and modest branch. Then, appropriate coding is assigned automatically to different subtrees to achieve river selection. Finally, actual topographic map data of a river system in a region of Hubei Province are used to comparatively validate the hybrid coding system against two existing isolated coding systems. The experimental results demonstrate that the hybrid coding method is very effective for river network selection, not only in highlighting hierarchies formed by catchment relationships but also in the uniform distribution of tributaries.

Prediction of Fuel Maps in Variable Valve Timing Spark Ignited Gasoline Engines Using Kriging Metamodels

<div class="section abstract"><div class="htmlview paragraph">Creating a fuel map for simulation of an engine with Variable Valve Actuation (VVA) can be computationally demanding. Design of Experiments (DOE) and metamodeling is one way to address this issue. In this paper, we introduce a sequential process to generate an engine fuel map using Kriging metamodels which account for different engine characteristics such as load and fuel consumption at different operating conditions. The generated map predicts engine output parameters such as fuel rate and load. We first create metamodels to accurately predict the Brake Mean Effective Pressure (BMEP), fuel rate, Residual Gas Fraction (RGF) and CA50 (Crank Angle for 50% Heat Release after top dead center). The last two quantities are used to ensure acceptable combustion. The metamodels are created sequentially to ensure acceptable accuracy is achieved with a small number of simulations. Two optimization problems are then solved using the developed metamodels, for full load and part load conditions, respectively. We demonstrate that the estimated fuel map is of high accuracy compared to the actual map. The map is obtained with about one tenth of the number of engine simulations for a full factorial design, leading to much faster predictions.</div></div>

Modeling the impact of out-of-phase ventilation on normal lung tissue response to radiation dose.

To create a dose-response model that predicts lung ventilation change following radiation therapy, and examine the effects of out-of-phase ventilation. The dose-response model was built using 27 human subjects who underwent radiation therapy (RT) from an IRB-approved trial. For each four-dimensional computed tomography, two ventilation maps were created by calculating the N-phase local expansion ratio (LERN ) using most or all breathing phases and the 2-phase LER (LER2 ) using only the end inspiration and end expiration breathing phases. A polynomial regression model was created using the LERN ventilation maps pre-RT and post-RT and dose distributions for each subject, and crossvalidated with a leave-one-out method. Further validation of the model was performed using 15 additional human subjects using common statistical operating characteristics and gamma pass rates. For voxels receiving 20Gy or greater, there was a significant increase from 52% to 59% (P=0.03) in the gamma pass rates of the LERN model predicted post-RT Jacobian maps to the actual post-RT Jacobian maps, relative to the LER2 model. Additionally, accuracy significantly increased (P=0.03) from 68% to 75% using the LERN model, relative to the LER2 model. The LERN model was significantly more accurate than the LER2 model at predicting post-RT ventilation maps. More accurate post-RT ventilation maps will aid in producing a higher quality functional avoidance treatment plan, allowing for potentially better normal tissue sparing.

Ormanlarda iklim değişikliğinin etkilerinin belirlenmesinde bazı önemli parametreler: Cerle planlama birimi örneği, Antalya, Türkiye

Climate change is one of the most detrimental issues of the earth in this century. It is evident that a change in climate parameters would affect considerably the entire global ecosystem’s environment. On the other hand, different forest related parameters such as natural disaster occurrence or land cover change of forest ecosystems could also help to understand the effects of climate change besides climate parameters such as temperature, precipitation or relative humidity. Therefore, in this study, selected parameters describing climate change were tried to display in a Mediterranean forest planning unit called “Cerle” located in Antalya. Firstly, trend analysis was carried out using climate parameters in the selected study area to detect the amount of climate change. Then, different natural disaster occurrences such as forest fire, windstorm, insects and pest attack evolution were displayed over a period of 30 to 50 years. Spatiotemporal changes for the same period were also tried to be demonstrated, based on historical and actual maps to identify the impact of climate change on the structure and composition of forest ecosystems. In brief, Mann-Kendall test results showed significant increasing trends in annual mean temperature and precipitation. Moreover, the natural development of crimean pine as a result of land use change analysis, as well as the increasing frequency of wildfires and windstorm leading to salvage cutting have been interpreted as the impact of climate change. It is concluded that changes in selected climate parameters are very important to display the climate change digitally. The outputs of this study could help in future forest management planning decisions and determination of silvicultural prescriptions. Furthermore, the obtained results might be the basis for the integration of climate change to forest management planning related to future climate projections and scenarios.

Indoor WLAN Intelligent Target Intrusion Sensing Using Ray-Aided Generative Adversarial Network

An indoor target intrusion sensing technique has been used in many fields, such as smart home management, security monitoring, counter-terrorism, and disaster relief. At the same time, with the wide deployment of wireless local area network (WLAN) and general support of the IEEE 802.11 protocol by various mobile devices, the target intrusion sensing can be realized based on the existing WLAN infrastructure without requiring the target to carry any special device. However, the existing indoor WLAN target intrusion sensing approaches usually depend on the radio map construction with huge labor and time cost, which is the major barrier of current systems. In response to this compelling problem, we propose the new ray-aided generative adversarial model (RaGAM) to automatically construct the radio map, which is used for the indoor WLAN intelligent target intrusion sensing and localization. To achieve the low labor and time cost, the RaGAM uses the adaptive-depth ray tree based the quasi three-dimensional ray-tracing model to depict the difference of WLAN signals between the silence and intrusion environments with the purpose of constructing the synthetic radio map. Considering the gap between the synthetic and actual radio maps, we modify the conventional generative adversarial network by the joint synthetic and unsupervised learning (or called S+U learning) from the actual unlabeled received signal strength (RSS) data to improve the precision of the proposed ray-tracing model, and consequently obtain the refined radio map. After that, the statistical characteristics of the refined radio map are utilized to construct the training set for the probabilistic neural network (PNN), and then by using the well-trained PNN to classify the newly collected RSS data, the target intrusion sensing, and localization are achieved. The experimental results show that the proposed approach cannot only perform well in terms of computation cost and the ray-tracing accuracy, but also sense the target intrusion states accurately.

Understanding Current and Future Fragmentation Dynamics of Urban Forest Cover in the Nanjing Laoshan Region of Jiangsu, China

Accurate acquisition of the spatiotemporal distribution of urban forests and fragmentation (e.g., interior and intact regions) is of great significance to contributing to the mitigation of climate change and the conservation of habitat biodiversity. However, the spatiotemporal pattern of urban forest cover changes related with the dynamics of interior and intact forests from the present to the future have rarely been characterized. We investigated fragmentation of urban forest cover using satellite observations and simulation models in the Nanjing Laoshan Region of Jiangbei New Area, Jiangsu, China, during 2002–2023. Object-oriented classification-based land cover maps were created to simulate land cover changes using the cellular automation-Markov chain (CA-Markov) model and the state transition simulation modeling. We then quantified the forest cover change by the morphological change detection algorithm and estimated the forest area density-based fragmentation patterns. Their relationships were built through the spatial analysis and statistical methods. Results showed that the overall accuracies of actual land cover maps were approximately 83.75–92.25% (2012–2017). The usefulness of a CA-Markov model for simulating land cover maps was demonstrated. The greatest proportion of forest with a low level of fragmentation was captured along with the decreasing percentage of fragmented area from 81.1% to 64.1% based on high spatial resolution data with the window size of 27 pixels × 27 pixels. The greatest increase in fragmentation (3% from 2016 to 2023) among the changes between intact and fragmented forest was reported. However, intact forest was modeled to have recovered in 2023 and restored to 2002 fragmentation levels. Moreover, we found 58.07 km2 and 0.35 km2 of interior and intact forests have been removed from forest area losses and added from forest area gains. The loss rate of forest interior and intact area exceeded the rate of total forest area loss. However, their approximate ratio (1) implying the loss of forest interior and intact area would have slight fragmentation effects on the remaining forests. This analysis illustrates the achievement of protecting and restoring forest interior; more importantly, excessive human activities in the surrounding area had been avoided. This study provides strategies for future forest conservation and management in large urban regions.

The Task of the Cleric: Cartography, Translation, and Economics in Thirteenth-Century Iberia by Simone Pinet

Reviewed by: The Task of the Cleric: Cartography, Translation, and Economics in Thirteenth-Century Iberia by Simone Pinet Mary Jane Kelley KEYWORDS Mester de Clerecía, Libro de Alexandre, Libro De Apolonio, Libro De Fernán González, Cartography, Translation, Economics, Maps, Medieval Spain, Rhetoric, Medieval Literature simone pinet. The Task of the Cleric: Cartography, Translation, and Economics in Thirteenth-Century Iberia. U of Toronto P, 2016, 191 pp. Each of the three components in the subtitle of Simone Pinet’s new book on the Spanish mester de clerecía corresponds to one of three interrelated chapters that together define the task of the cleric. Pinet reads outward from the text to the scriptorium library to the medieval Iberian world in order to identify the intellectual, economic, and political contexts that shaped the Libro de Alexandre, Libro de Fernán González, and Libro de Apolonio. The result is a remarkably fresh conception of clerical narrative fiction from thirteenth-century Spain. The book opens with an intriguing thirty pages of manuscript images including medieval maps and other organizing diagrams, such as horseshoe arches and rotae. As they progress through the book, readers learn to interpret these drawings through a newly acquired literacy in which Pinet schools us. One measure of the book’s success is the satisfying familiarity with which we approach the images our second time through. The same can be said for the introduction, in which Pinet suggestively anticipates showing “how cartography served as a source . . . for the rhetorical invention of fiction” in chapter 1 (8), how the Tower of Babel provides “a point of entry . . . to the political potential in the figure of the cleric as translator” in chapter 2 (11), and how “contemporary practices and linguistic registers belonging to the sphere of economics are translated into a rhetoric that can negotiate increasingly complicated courtly politics” in chapter 3 (12). All of this becomes clear in the pages that follow. Chapter 1, “The Cleric’s Compass,” argues that the medieval discipline of cartography informs various scenes in the Libro de Alexandre, in which the poet maps with words. Readers of Pinet’s Archipelagoes (2011) will recognize and once again appreciate her ability to synthesize cartography with other medieval disciplines and bring it all to bear in insightful textual analysis in a specific literary context. In order to make her point through a detailed reading of the Alexandre in the second half of the chapter, Pinet first offers a history of mapping and its function in manuscript culture. A chronological study of maps appearing in manuscripts from the ninth to the thirteenth centuries reveals an evolution of function from [End Page 131] simple illustration of the three divisions of the world in T/O maps to a highly rhetorical language in mappamundi that parallels learned prose in its use of tropes such as abbreviatio, amplificatio, and digressio. When Alexander lands in Asia, the poet launches into a cartographic digression based in part on Gautier de Chȃtillon’s Alexandreis, but, Pinet suggests, an additional source for the material of unknown origin may have been an actual map, whose graphic components would have triggered associations in the clerical poet’s memory and inspired him to supplement further his source text. Later in the poem, in the ekphrasis on Alexander’s shield, the poet includes a detailed description of a mappamundi that both reviews and anticipates themes and action in his story. Based on that passage, Pinet builds an eloquent reflection on the interplay of visual and verbal representation, as well as the role of the artist/cartographer/author and the interpreter/observer/reader, both within the text and without. Map-making and authorship of narrative fiction are parallel endeavors, especially in the context of thirteenth-century Spain. Chapter 2 turns to translation as another way to describe clerical authorship and which, similar to mapping, involves more than meets the uninitiated, twenty-first-century reader’s eye. The chapter title, “Bricks and Mortar,” refers to the rhetorical materials that clerical authors had at their disposal for constructing, or translating, texts in the late twelfth and early thirteenth centuries. The first half of this, the most challenging of the book’s...

Optimization of Ride Sharing Systems Using Event-driven Receding Horizon Control

Abstract We develop an event-driven Receding Horizon Control (RHC) scheme for a Ride Sharing System (RSS) where vehicles are shared to pick up and drop off passengers so as to minimize a weighted sum of passenger waiting and traveling times. The RSS is modeled as a discrete event system and the event-driven nature of the controller significantly reduces the complexity of the vehicle assignment problem, thus enabling its real-time implementation. Simulation results using actual city maps and real traffic data illustrate the effectiveness of the RH controller in terms of online implementation and performance relative to known heuristics.

SyntheticNET: A 3GPP Compliant Simulator for AI Enabled 5G and Beyond

The rapid evolution of cellular system design towards 5G and beyond gives rise to a need for investigation of the new features, design proposals and solutions in realistic settings for various deployments and use case scenarios. While many system level simulators for 4G and 5G exist today, there is particularly a dire need for a 3GPP compliant system level holistic and realistic simulator that can support evaluation of the plethora of AI based network automation solutions being proposed in literature. In this paper we present such a simulator developed at AI4networks Lab, called SyntheticNET. To the best of authors' knowledge, SyntheticNET is the very first python-based simulator that fully conforms to 3GPP 5G standard release 15 and is upgradable to future releases. The key distinguishing features of SyntheticNET compared to existing simulators include: 1) a modular structure to facilitate cross validation and upgrading to future releases; 2) flexible propagation modeling using measurement based, ray tracing based or AI based propagation modeling; 3) ability to import data sheet based on measurement based realistic vendor specific base station features such as antenna and energy consumption pattern; 4) support for 5G standard based adaptive numerology; 5) realistic and user-specific mobility patterns that are yielded from actual geographical maps; 6) detailed handover (HO) process implementation; and 7) incorporation of database aided edge computing. Another key feature of the SyntheticNET is the ease with which it can be used to test AI based network automation solutions. Being the first python based 5G simulator, this ease, in part stems for SyntheticNET's built-in capability to process and analyze large data sets and integrated access to Machine Learning libraries. Thus, SyntheticNET simulator offers a powerful platform for academia and industry alike to investigate not only new solutions for optimally designing, deploying and operating existing and emerging cellular networks but also for enabling AI empowered deep automation in the future.

Electric Vehicle Charging Warning and Path Planning Method Based on Spark

Electric vehicles (EVs) are increasing popular as clean transportation. However electric vehicle (EV) users often encounter a shortage of electricity in the driving process. Especially when vehicle charging path planning system request reaches peak hour, it will increase the system calculation pressure. To promote penetration and popularity of electric vehicles, it is of critical importance to determine location and size of charging stations more scientifically and improve the computational power of path planning. In this paper, a method is developed for charge warning and path planning of insufficient energy EVs. The method utilizes the appropriate energy consumption factor to construct the electricity early warning model, analyzes the consumption of electricity in the vehicle in real time, and promptly warns the users when the vehicle has insufficient electric energy. Meanwhile, it combines the actual traffic information and map information to construct a network topology based on time weight, queuing mechanism, and charging calculation model to obtain path network model data, charging station queuing times and charging times as parameters. EV path planning problem is solved to minimize the total times of travel using the Dijkstra algorithm with the input path network model data, charging station queuing times and charging times as parameters. At the same time, the Spark computing framework is joined innovatively to improve the computing speed and solve the problem of path planning peak period. The Spark framework is utilized to parallelize the optimized Dijkstra algorithm. The experiment shows that the electric vehicle charging warning and path planning method provides an effective way for drivers of electric vehicles to charge.

Predicting land use changes in northern China using logistic regression, cellular automata, and a Markov model

Land use changes are complex processes affected by both natural and human-induced driving factors. This research is focused on simulating land use changes in southern Shenyang in northern China using an integration of logistic regression, cellular automata, and a Markov model and the use of fine resolution land use data to assess potential environmental impacts and provide a scientific basis for environmental management. A set of environmental and socio-economic driving factors was used to generate transition potential maps for land use change simulations in 2010 and 2020 using logistic regression. An average relative operating characteristic value of 0.824 was obtained, indicating the validity of the logistic regression model. The logistic–cellular automata (CA)–Markov model was calibrated by comparing the actual and simulated land use maps of 2010. A match of 83.7% was achieved between the predicted and actual maps of 2010, which represented a satisfactory calibration. This indicated that the integration of the logistic regression, CA, and Markov model has a high potential for simulating land use change in northern China. The calibrated hybrid model was implemented to obtain a land use map for 2020. The results showed a new wave of suburban development in the southwestern, west, and northwestern parts of the study area during 2010–2020. In addition, urban expansion has been accelerating, which is very likely to exacerbate the extensive environmental pollution currently existing in this area. Moreover, rapid urban expansion has resulted in significant decreases in forest areas and agricultural lands.