Web Map Research Articles

Development of Spatial Uncertainty-Aware Visualization of Historical Traffic Queue Length

Traffic queue length at intersection is one of the most important measures of traffic signal performance and the traffic signal optimization. The spread of the ride-hailing services makes GPS data widely available with large area coverage, allowing us to quantify the queue length in a wide area without the need for additional installation cost like conventional sensors. Hence, queue length can be observed simultaneously and the relationship between intersections can be captured. Due to the current myriad of available queue length data, an analysis tool is needed to turn the data into useful knowledge to support traffic operations and management. However, the current queue length visualization only focuses on a real-time fashion, which cannot provide information about the change of the queue or its uncertainty. Such information is crucial for understanding the probability of the queue to be spillover, as well as determining the lengths of turning lanes to minimize blockage. In this research, we developed a spatial uncertainty-aware visualization by applying a conventional technique called box plot to visualize queue lengths on a map derived from taxi GPS data. A prototype of a web mapping application has been implemented and tested with users. The results indicated that participants with a statistics background have an advantage over those without a statistical background to understand the box plot. However, all of them were able to identify the uncertainty of the queue lengths as well as to compare the uncertainty between intersections and times.

Computational Cartographic Recognition: Identifying Maps, Geographic Regions, and Projections from Images Using Machine Learning

Map reading is a challenging task for computer programs. This article explores how artificial intelligence and machine learning methods can be used to understand maps, an area we broadly refer to as computational cartographic recognition. Specifically, we use machine learning methods to (1) identify whether an image is a map, (2) recognize the geographic region on the map, and (3) recognize the projection used on the map. Four machine learning models—support vector machine, multilayer perceptrons, convolutional neural networks (CNNs) developed from scratch using our own architecture (CNNS), and pretrained CNN models through transfer learning (CNNT)—are applied in these tasks. We use 2,200 online map images, 500 nonmap images, and 1,050 synthetic map images to train and evaluate the models. Results show that the CNNT models achieve the highest performance among all models, with an accuracy rate above 90 percent for the tasks. The CNNS models come in second. We also conduct a round of stress tests using 3,600 additional synthetic maps where the shape and layout are systematically distorted and test if the models can still identify the maps and recognize the region and projection on the maps. The results of the stress tests show that the models can reliably recognize some of the modified maps even when exhibiting performance inferior to even random models for other maps. This unpredictable nature of the methods when applied to maps that are not represented in the training data suggests both promises and limitations of the current machine learning approaches to cartographic recognition.

Amaka Okechukwu. To Fulfill These Rights: Political Struggle Over Affirmative Action and Open Admissions.

Universities are highly visible sites that have traditionally defined “diversity” in public discourse and, as Amaka Okechukwu argues, served as standard-bearers for national conversations, debates, and political decisions around racial discrimination. To Fulfill These Rights: Political Struggle Over Affirmative Action and Open Admissions offers a detailed and nuanced account of the actions behind key policy decisions that have shaped ethnic and racial diversity in US higher education in recent decades. A wave of protests swept across university campuses in 2014 and 2015. Student protestors, and sometimes faculty, on US campuses advocated for greater representation of marginalized groups in the student body, on faculty, and in higher levels of university administration. These protests stretched across the United States and beyond to the United Kingdom and South Africa under the banner #RhodesMustFall. As Angus Johnston documents in a detailed online time line and map of these events, this wave of protests entwined an array of student concerns over university mismanagement of sexual assault and harassment cases, anger over the killing of Michael Brown in Ferguson, fears around racist campus policing, and student protests against tuition hikes, among others. The protests raised questions about university authority and the intentions of those on the boards of trustees. This is where Okechukwu’s book begins. Through meticulous archival research, we gain a nuanced view of the actions that have thwarted fair access to higher education despite decades of mobilization.

Evaluating the VGI Users' Level of Expertise

Currently, online maps are of the most innovative and significant sources of information in people's daily life. However, quality assessment of volunteered geographic information (VGI) data raises some challenges. This research aims to analyze the VGI participants' level of expertise through evaluation of their background information. Towards this goal, an android application was developed to test users' knowledge and cognition about some selected regions of city as well as their background information. In order to evaluate the quality of information expressed by participants, some changes were made in Tehran's online map, and users were asked to identify the changes and to guess the vanished attributes. Statistical and ANN approaches were applied for analysis. The results demonstrated that the ANN was able to predict the percentage of correct answers of a new volunteer with mean squared error of 0.2. This research suggests that users' age and familiarity with the specific region in the city play more significant roles in their expertise in using online maps and in probable participating in VGI.

Building an interactive web mapping tool to support distributed energy resource planning using public participation GIS

Community solar projects involve the installation of large PV systems whose net outputs are then virtually allocated to customers at other locations. They represent a relatively new model of distributed energy resource (DER) implementation that could help overcome some historical barriers to low-income, disadvantaged community, renter household participation in the ongoing renewable energy transition. Siting community solar assets is a thick and inherently geographic problem however, one which relates to historical patterns of energy infrastructure investment, local urban development, and community socio-demographic change. Creating a more equitable energy system will require planning tools which better coordinate the DER adoption decisions of individual property owners with the grid operators. Interactive decision support tools based on web maps are well suited to addressing this need for improved information sharing and coordination. However, such tools need to be designed and implemented through a process of public participation with stakeholders who explicitly focus on social and environmental justice. Here we describe the process by which one such tool was developed. We also report on several important findings derived from its use relating to current imbalances between local DER supply potential and the grid integration capacity limits which exist within Southern California's disadvantaged communities.

Evaluating the accuracy and usefulness of commercially-available proximal soil mapping services for grassland nutrient management planning and soil health monitoring

Grasslands play an important role in global food security. However, there are increasing pressures to improve the sustainability of ruminant farming. Precision nutrient management tools (e.g., proximal soil sensors for soil mapping) offer opportunities to improve nutrient use efficiency through spatially-variable nutrient application rate maps. Despite little research validating these technologies on grasslands, commercial companies promote these technologies to grassland farmers. In this study, the accuracy of commercial companies offering these services was evaluated by comparing soil pH, P, K, Mg and SOM measurements derived from conventional soil sampling and laboratory analyses to measurements derived from the commercial operators, across a range of soils that are typical found in UK grasslands. Results showed that soil mapping services utilising gamma-ray spectroscopy (GRS) were not sufficiently accurate to predict soil pH, P, K and Mg on grasslands, and subsequently inappropriate for nutrient management planning for variable rate lime and nutrient application. Conversely, both GRS and visible-near infrared spectroscopy (Vis–NIR) accurately predicted between-field SOM variations in grassland soils but not within-field variation. This study emphasises the need for further research to explore the limitations of, and opportunities for, the universal application of these technologies across different soil types and/or land uses before their commercial application. It is therefore highly recommended that commercially-available soil mapping services are subject to certification, similar to centralised soil testing laboratories, to ensure data are accurate for soil management interpretation. The lack of reliability of such systems risks farmers’ confidence in the value of soil mapping, which could severely hinder future adoption of potentially valuable technologies.

Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree

Spatial clustering is dependent on spatial scales. With the widespread use of web maps, a fast clustering method for multi-scale spatial elements has become a new requirement. Therefore, to cluster and display elements rapidly at different spatial scales, we propose a method called Multi-Scale Massive Points Fast Clustering based on Hierarchical Density Spanning Tree. This study refers to the basic principle of Clustering by Fast Search and Find of Density Peaks aggregation algorithm and introduces the concept of a hierarchical density-based spanning tree, combining the spatial scale with the tree links of elements to propose the corresponding pruning strategy, and finally realizes the fast multi-scale clustering of elements. The first experiment proved the time efficiency of the method in obtaining clustering results by the distance-scale adjustment of parameters. Accurate clustering results were also achieved. The second experiment demonstrated the feasibility of the method at the aggregation point element and showed its visual effect. This provides a further explanation for the application of tree-link structures.

LiDAR Road-Atlas: An Efficient Map Representation for General 3D Urban Environment

In this work, we propose the LiDAR Road-Atlas, a compact and efficient 3D map representation, for autonomous robot or vehicle navigation in a general urban environment. The LiDAR Road-Atlas can be generated by an online mapping framework which incrementally merges local 2D occupancy grid maps (2D-OGMs). Specifically, the contributions of our method are threefold. First, we solve the challenging problem of creating local 2D-OGMs in nonstructured urban scenes based on a real-time delimitation of traversable and curb regions in a LiDAR point cloud. Second, we achieve accurate 3D mapping in multiple-layer urban road scenarios by a probabilistic fusion scheme. Third, we achieve a very efficient 3D map representation of a general environment thanks to the automatic local-OGM-induced traversable-region labeling and a sparse probabilistic local point-cloud encoding. Given the LiDAR Road-Atlas, one can achieve accurate vehicle localization, path planning, and some other tasks. Our map representation is insensitive to dynamic objects which can be filtered out in the resulting map based on a probabilistic fusion. Empirically, we compare our map representation with a couple of popular map representations in robotics society, and our map representation is more favorable in terms of efficiency, scalability, and compactness. Additionally, we also evaluate localization performance given the LiDAR Road-Atlas representations on two public datasets. With a 16-channel LiDAR sensor, our method achieves an average global localization error of 0.26 m (translation) and 1.07 (rotation) on the Apollo dataset, and 0.89 m (translation) and 1.29 (rotation) on the MulRan dataset, respectively, at 10 Hz, which validates its promising performance. The code for this work is open-sourced at https://github.com/IMRL/Lidar-road-atlas.

Ethnic segregation on linguistic landscapes

This work presents a novel approach to studying ethnic segregation from the perspective of linguistic landscapes. Numerous street-level images accumulated over the last two decades have enabled the exploration of linguistic landscapes at a larger scale than ever before. Since the prevalence of a specific language in a public space implies the linguistic group inhabiting the area, its careful evaluation can reveal the degree of segregation between linguistically different ethnic groups. To demonstrate the effectiveness of the proposed approach, we applied it to the linguistic landscape of Seoul, South Korea. Using a large set of street-level images collected from an online map platform, we measured the levels of segregation between Korean and Chinese signs from 2010 to 2018. The levels of segregation on the street-level images were different to a certain extent from those of residential segregation. While residential segregation gradually increased between 2010 and 2018, except for 2011, more fluctuations were observed in linguistic segregation. This finding is likely because a linguistic landscape is shaped mainly by advertising signs, banners, and billboards in commercial areas, and such commodified urban spaces change more dynamically to attract inhabitants and visitors. These results suggest that the proposed approach can offer an alternative way of understanding the complex socio-demographic phenomenon from a new perspective, as with other emerging data sources in the era of big data.

Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System

Abstract. Operational global-scale hydrological forecasting systems are used to help manage hydrological extremes such as floods and droughts. The vast amounts of raw data that underpin forecast systems and the ability to generate information on forecast skill have, until now, not been publicly available. As part of the Global Flood Awareness System (GloFAS; https://www.globalfloods.eu/, last access: 3 December 2022) service evolution, in this paper daily ensemble river discharge reforecasts and real-time forecast datasets are made free and openly available through the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). They include real-time forecast data starting on 1 January 2020 updated operationally every day and a 20-year set of reforecasts and associated metadata. This paper describes the model components and configuration used to generate the real-time river discharge forecasts and the reforecasts. An evaluation of ensemble forecast skill using the continuous ranked probability skill score (CRPSS) was also undertaken for river points around the globe. Results show that GloFAS is skilful in over 93 % of catchments in the short (1 to 3 d) and medium range (5 to 15 d) against a persistence benchmark forecast and skilful in over 80 % of catchments out to the extended range (16 to 30 d) against a climatological benchmark forecast. However, the strength of skill varies considerably by location with GloFAS found to have no or negative skill at longer lead times in broad hydroclimatic regions in tropical Africa, western coast of South America, and catchments dominated by snow and ice in high northern latitudes. Forecast skill is summarised as a new headline skill score available as a new layer on the GloFAS forecast Web Map Viewer to aid user interpretation and understanding of forecast quality.

Аналіз рішень та підходів кластеризації геопросторових даних для оптимізації продуктивності веб-карти та взаємодії користувача

In the contemporary epoch, the management and visualization of geospatial information in web browsers have gained substantial importance. Web maps are indispensable tools across various tourism, goods delivery, and ecology sectors. Furthermore, the extensive support of web browsers on diverse devices enhances the accessibility of geospatial data on the web for various users. However, the incessant increment of geospatial information poses new challenges in efficiently displaying data and navigation through these data on web maps. Therefore, the clustering of geospatial data is crucial in dealing with them. Different clustering methods may affect the performance or visual clarity of web maps. To improve the user experience and optimize the use of computing resources, geodata clustering becomes a necessary tool for processing large volumes of markers on the map. Despite significant progress in the development of geodata clustering solutions in web maps, there are some challenges that developers and users may encounter. In this article, challenges with scaling, dynamic cluster data, and heterogeneity of data are described. Existing problems in geodata clustering in web maps require additional research and development. Understanding these issues will help developers and researchers improve existing solutions and create new methods and approaches for efficient clustering of geodata in web maps. The urgency of solving the problem lies in the search for effective clustering solutions that provide an opportunity to ensure convenient interactivity and fast processing of geodata in web maps. This study provides a comprehensive review of data types and clustering methods. Tools and libraries for geodata clustering in web maps are analyzed. Different types of geodata and approaches to working with them were also studied. Concepts such as semi-static data and their positions alongside static and dynamic data types are elucidated. Through the analysis, optimal scenarios for applying specific clustering methods or the utilization of server-side clustering approaches have been identified. Conclusions have also been drawn on the preferred approach when handling extensive volumes of static or semi-static geospatial data, particularly advocating for the application of server-side clustering with caching. In conclusion, various clustering approaches in web maps, both client-side and server-side, have been scrutinized. The advantages and disadvantages of both approaches, along with recommendations on when to apply each method, have been delineated. A noticeable absence of explicit approaches in clustering vast geospatial data for web map representation underpins the relevance and necessity of research in this direction.

Semi-MapGen: Translation of Remote Sensing Image Into Map via Semisupervised Adversarial Learning

Online maps play an essential role in modern life. The convenience of acquiring remote sensing images provides reliable geographic information sources for the compilation of online maps. Some existing works have used the idea of domain mapping to translate remote sensing images into maps directly, which is of great prospect for application. However, many of the current remote sensing image-to-map translation works are performed in an unsupervised manner that would lead to problems such as distortion and local detail inaccuracy. Although the fully-supervised method is effective, it requires plenty of paired as well as matched data for training. Paired remote sensing images and maps with consistent spatial locations can be easily accessed through online map services, whereas many pairs of samples in which some geographic element information is not accurately and completely matched. Supervised learning-based translation models are often confused by these unmatched data. Accurate and complete matched data has to be selected deliberately by humans, and the manual selection process is time-consuming and laborious, which brings new challenges. Therefore, we propose a novel remote sensing image-to-map translation model named Semi-MapGen based on semi-supervised generative adversarial networks (GAN), which requires only a small set of accurate and complete matched data and plenty of unpaired data. In this model, we apply a knowledge extension-based learning strategy that can improve the accuracy of translated maps. In addition, we design Expansion loss and Channel-wise loss to learn the information from massive unpaired data in an unsupervised manner. Qualitative and quantitative experiment results on three datasets demonstrate that the proposed model outperforms state-of-the-art semi-supervised and supervised methods.

Zasięg oraz trendy w użytkowaniu serwisu mapowego miejscowego planu zagospodarowania przestrzennego gminy Tomice

Users are primarily interested in the comfort of use and the range of map service in terms of its functionalities. Publishers, in turn, monitor usage statistics in order to optimize the operation of their websites based on the analysis of these statistics. This also applies to municipal map apps. The present work’s objective is to analyse the reach and trends in the use of the Internet application presenting the local spatial development plan (eMPZP). The monitoring covered the map service of the Tomice municipality – in the Małopolska Region (Województwo Małopolskie), Wadowice district (Powiat Wadowicki), Poland – which is available on the municipality’s server at the Internet address https://www.tomice.pl/mpzp/. The data was collected in the period from January 1, 2013 to May 1, 2023. Selected website usage indicators were analysed. In a period of just over 10 years, the eMPZP application was displayed 31,713 times, of which 25,009 (78.86%) were unique views. The largest number of users was recorded in Poland, i.e. 13,044 (83.52%); followed by the United States, i.e. 565 (3.62%) and the United Kingdom, i.e. 434 (2.78%). Most users found the eMPZP application using search engines and direct links. In conclusion, it was demonstrated that map service usage statistics could be useful for municipal authorities, as they help to understand how the service is used, by whom it is used, and what the users’ preferences are. In addition, it was shown that eMPZP still fulfils a number of selected functions, despite the fact that in 2019 it was replaced by a geoportal (http://mpzp.tomice.pl). Accordingly, it is recommended that the so-called automatic redirection be set, sending the users to the current geoportal using specific HTML tags.

Topographic data in the cartographic support of geological exploration: current state and prospects

The purpose of the current study is an attempt to characterize the scientific and practical issues of modern topographic data preparation and its use in geological exploration. The study is based on the long-term author’s experience in cartographic support of field and cameral geological and geophysical works and on the analysis of modern research in topographical mapping. The specifics of providing access to modern topographic data in the Russian Federation, the Republic of India and the Republic of Colombia, where the main experimental work has been carried out, are briefly described. The content features of the topographic map sheets of these countries are shortly summarized. Practical results of solving such exploration tasks as survey network design, creation of terrain sketch, processing and interpretation of geological and geophysical data, creation of digital terrain models, and preparation of base maps for web services are presented. The prospective directions for further research in improving the use of topographic data in geological exploration are given. It has been found that topographic maps and data are increasingly being used as base layers in the creation of the modern web map services. A balanced and critical approach to the use of global topographic data resources is need for geological exploration as a state’s strategic activity. The industry needs publicly available national multiscale topographic maps as well as theoretical and methodological foundations for the use of web mapping advances in mineral prospecting and exploration.

Describing the administrative border of Koshtepa district on an electronic digital map and creating a web map

In this article, the administrative border of the district is described on an electronic digital map and a web map is created, on the basis of the obtained results, in the development of target state programs for the establishment of digital maps connected to coordinate systems in the determination of the boundaries of villages and towns of the Koshtepa district of Fergana region and the determination of the boundaries of administrative-territorial units, and the design and determination of administrative boundaries data is provided.

Understanding uncertainty in forest resources maps

Maps of forest resources and other ecosystem services are needed for decision making at different levels. However, such maps are typically presented without addressing the uncertainties. Thus, the users of the maps have vague or no understanding of the uncertainties and can easily make wrong conclusions. Attempts to visualize the uncertainties are also rare, even though the visualization would be highly likely to improve understanding. One complication is that it has been difficult to address the predictions and their uncertainties simultaneously. In this article, the methods for addressing the map uncertainty and visualize them are first reviewed. Then, the methods are tested using laser scanning data with simulated response variable values to illustrate their possibilities. Analytical kriging approach captured the uncertainty of predictions at pixel level in our test case, where the estimated models had similar log-linear shape than the true model. Ensemble modelling with random forest led to slight underestimation of the uncertainties. Simulation is needed when uncertainty estimates are required for landscape level features more complicated than small areas.

High-technology based startup in agribusiness sector: mapping linkages, products and services

Abstract: The agribusiness sector has experienced a significant pace of technological evolution. New resources, such as the use of drones, the internet of things, and satellite communication have been fundamental in the emergence of AgTechs, technological companies that have emerged from agribusiness. This article aims to map São Paulo AgTechs and the products and services they offer. This article contributes to the advancement of knowledge about AgTechs and their transformation of the Brazilian agribusiness field. AgTechs have expanded and strengthened the agribusiness sector, which represented approximately 21.4% of Brazilian GDP in 2019, and 26.6% in 2020, despite the political-health crisis and its challenge, it has continued the growth and investment trajectory in Rand and Development, while seeking to increase efficiency in interactions. This qualitative study is exploratory and has a bibliographic design. The data were collected from the Brazilian Association of Startups, Global Entrepreneurship Monitor reports, websites of AgTechs in São Paulo state, and Web of Science. The main results indicate the importance of the São Paulo ecosystem, in which are located 26% of Brazilian AgTechs, the largest concentration in the country. The products and services generated at AgTechs are high tech. In the state of São Paulo, there are three communities that interact with AgTechs and form an entrepreneur-innovative ecosystem.

A GIS-based interactive map enabling data-driven decision-making in Nigeria's food supply chain

The accessibility of open-source data on fresh food supply chains provides key stakeholders from the public and private sectors with insights for better decision-making to drive food loss reduction. Nigeria has a fair amount of open-source agricultural and climate-related data. However, most of these datasets are not readily accessible. This paper presents a detailed method used to develop an interactive web Geographic Information System (GIS) tool that collates and visualizes available open-source datasets on Nigeria's Agricultural Sector with particular focus on the fresh produce supply chains. The following steps were used to create such an interactive map.•Open-source data were acquired in various forms, including tabular, vector, and rasters, processed and uploaded as layers on the interactive web map.•Most of the data needed some processing on open-source geographic information system applications and web-based computing platforms to transform them into sources of actionable insights•These final processed layers were then uploaded to a consolidated interactive web map built on the Google Earth Engine platform.The gathered open-source data includes crop production data, market prices, weather, road network, market locations, mobile coverage, water access, water scarcity, and food insecurity. The method described here also allows the reproduction of such maps for other countries.

Географический атлас «Территории традиционного природопользования Республики Саха (Якутия)»: Структура и информационное наполнение

The article discusses the current state of issues of atlas mapping of the territories of traditional nature use (TTPs) of the Republic of Sakha (Yakutia). It is shown that currently there are no such atlases in Russia, but there is a need to create them based on modern web mapping technologies, which will ensure accessibility for a wide range of users. The necessity of creating a geographical atlas “Territories of traditional nature use of the Republic of Sakha (Yakutia)”, which systematizes a knowledge about the peculiarities of residence and economic activity of indigenous peoples of the North in Yakutia in the form of a complex of maps, graphic materials and analytical texts, is substantiated. The structure and information content of the developed electronic atlas is presented, the content of sections is described, including various information resources used in the creation of maps. The main features of the thematic content of the atlas sections are highlighted. Examples of maps are given: “Population density of the Republic of Sakha (Yakutia)”, “Settlement of indigenous peoples of the North in the territories of traditional nature use of the Republic of Sakha (Yakutia)”. It is assumed that the atlas would be interesting to specialists from other Arctic regions of the Russian Federation, as it is planned to prepare an atlas in the form of a geoportal with the possibility of further replenishment of the database. The proposed solutions on the structure of the atlas, its information content, methods of cartographic display and functional capabilities can be subsequently implemented when creating similar cartographic models for other regions of residence and traditional nature use of indigenous peoples of the North, Siberia and the Russian Far East.

Interpretable machine learning models for detecting fine-grained transport modes by multi-source data

<abstract> <p>Analysis of transport mode choice is crucial in transportation planning and optimization. Traditionally, the transport mode of individuals is detected by discrete choice models (DCMs), which rely on data regarding individual and household attributes. Using these attribute data raises privacy concerns and limits the applicability of the model. Meanwhile, the detection results of DCMs may be biased, despite providing insight into the impact of variables. The machine learning models are more effective for mode detection, but most models need more interpretability. In this study, an interpretable machine learning model is developed to detect the transport modes of individuals. The mobility features of individuals, which introduce the velocity and acceleration of the center of mass (COM) are innovatively considered in the detection model. These mobility features are combined with multi-source data, including land use mix, GDP, population and online map service data as detection features. Using the travel survey data from Nanjing, China in 2015, the effects of different machine learning models on fine-grained detection performance are investigated. The results indicate that the deep forest model presents the best detection performance and achieves an accuracy of 0.82 in the test dataset, demonstrating the effectiveness of the proposed detection model. Furthermore, t-distributed stochastic neighbor embedding (t-SNE) and ablation experiments are conducted to overcome the non-interpretability issue of the machine learning models. The results show that the mobility features of individuals are the most critical features for improving detection performance. This study is essential for improving the structure of transport modes and maintaining low-carbon and sustainable development in urban traffic systems.</p> </abstract>