Mapping Tool Research Articles

Rediscovering publicly available single-cell data with the DISCO platform.

Single-cell RNA sequencing (scRNA-seq) has emerged as the key technique for studying transcriptomics at the single-cell level. In our previous work, we presented the DISCO database (https://www.immunesinglecell.org/) that integrates publicly available human scRNA-seq data. We now introduce an enhanced version of DISCO, which has expanded fourfold to include >100 million cells from >17 thousand samples. It provides uniformly realigned read count tables, curated metadata, integrated tissue and phenotype specific atlases, and harmonized cell type annotations. It also hosts a single-cell enhanced knowledgebase of cell type ontology and gene signatures relating to cell types and phenotypes. Lastly, it offers a suite of tools for data retrieval, integration, annotation, and mapping, allowing users to construct customized atlases and perform integrated analysis with their own data. These tools are also available in a standalone R package for offline analysis.

Enhancing environmental modeling and 3D point cloud construction for unmanned vehicles using laser SLAM and stereo vision with Convolutional Neural Networks

Abstract. This research presents an innovative SLAM algorithm that integrates Convolutional Neural Networks (CNNs) with LIDAR and stereo vision to significantly enhance the accuracy of environmental modeling and the construction of dense 3D point cloud maps in complex and dynamic surroundings. By processing pre-recorded video data and employing advanced image segmentation techniques, this study achieves a deep fusion of visual and geometric data, resulting in highly detailed and precise 3D representations of the environment. The experimental results demonstrate that this approach effectively detects and excludes dynamic objects, thereby significantly improving the overall quality, robustness, and reliability of the constructed maps. This work represents a substantial advancement in SLAM technology, particularly in its capability to model and capture intricate environmental details under varying conditions, making it a powerful tool for precise environmental mapping.

Monitoring mangroves with multi-sensor Earth Observation data sets: from one-shot historical cartography to online and near-real-time monitoring tools

Abstract. This study outlines advancements in mangrove monitoring using Earth Observation (EO) technologies, highlighting a transition from historical cartography to modern, near-real-time monitoring systems. Mangroves, critical for biodiversity, climate regulation, and coastal protection, face threats from climate change and human activities. The need for accurate and recurrent mapping tools is emphasized, addressing the limitations of current global datasets like the Global Mangrove Watch (GMW) by incorporating high-resolution data and field measurements. The French National Research Institute for Sustainable Development (IRD) has pioneered mangrove monitoring since the 1990s, with key methodologies including the use of Sentinel-2 and Pleiades satellite imagery for high-resolution mapping, texture-based analysis, and the incorporation of LiDAR data for accurate biomass estimation. Historical cartography is contrasted with contemporary monitoring efforts, focusing on the integration of multi-source data and the importance of localized ground-truthing. This approach enhances the capabilities of earth observation for carbon stock assessments and supports informed decision-making in conservation and climate policy. The work also highlights the need for further integration of local knowledge and advanced EO sensor data to refine carbon sequestration models and improve mangrove management strategies.

Ocean literacy research community: co-identifying gaps and priorities to advance the UN Ocean Decade

IntroductionThe overarching goal of the UN Ocean Decade is to “change humanity's relationship with the ocean.” While this may be a challenge, it is, at the same time, a once in a generation opportunity. How can 8 billion people, including those who don't live near coastal areas, be inspired to value and care for the ocean? This is the essence of ocean literacy, and the driver of ocean literacy research (OLR).MethodsIn 2021, we began a research initiative to co-create a global OLR agenda by the developing OLR community, to better understand existing research themes, gaps, future priorities, actions, and impacts of ocean literacy initiatives. To deliver this, a series of virtual workshops – with the first taking place as part of the UN Ocean Decade Laboratories – was complemented by a participatory methodology using digital survey and mapping tools for crowdsourced collaboration.Results and discussionThrough this process, four initial OLR priorities were identified, including measuring ocean literacy, the role of ocean literacy as a policy mechanism, and alignment of OLR with climate change and the blue economy agendas. Finally, a working definition of OLR was developed to further guide OLR priorities for the UN Ocean Decade and beyond.

Optical Calibration of a Multi-Color Ellipsometric Mapping Tool Fabricated Using Cheap Parts

We developed and applied a new calibration method to make more accurate measurements with our multi-color ellipsometric mapping tool made from cheap parts. Ellipsometry is an optical technique that measures the relative change in the polarization state of the measurement beam induced by reflection from or transmission through a sample. During conventional ellipsometric measurement, the data collection is relatively slow and measures one spot at a time, so mapping needs a long time compared with our new optical mapping equipment made by an ordinary color LED monitor and a polarization-sensitive camera. The angle of incidence and the incident polarization state is varied point by point, so a special optical calibration method is needed. Three SiO2 samples with different thicknesses were used for the point-by-point determination of the angle of incidence and rho (ρ) corrections. After the calibration, another SiO2 sample was measured and analyzed using the calibrated corrections; further, this sample was independently measured using a conventional spectroscopic ellipsometer. The difference between the two measured thickness maps is less than 1 nm. Our optical mapping tool made from cheap parts is faster and covers wider area samples relative to conventional ellipsometers, and these correction enhancements further demonstrate its performance.

Deploying a user-friendly GIS mapping tool in post-earthquake Turkey and Syria

Background The February 2023 earthquakes in Turkey and Syria exposed significant gaps in healthcare information systems, which severely hampered communication, coordination, and the overall public health response. These disruptions were primarily due to the lack of real-time, accessible data on healthcare facilities. Methods In response, an interactive Geographic Information System (GIS) tool, coupled with an integrated WhatsApp chatbot, was developed under the platform Afetsaglikharitasi.org. This system enabled real-time mapping and updates on the status of healthcare services, even in areas with limited internet connectivity, facilitating more efficient resource coordination during the disaster response. Results By March 30, 2023, the disaster health map included data from 537 health centers and had reached over 800,000 individuals via social media platforms such as Instagram, Facebook, and Twitter. The platform attracted over 3,500 unique users, with most accessing it via mobile devices to locate healthcare services. Additionally, shortcomings in the Turkey National Disaster Response Plan (Türkiye Afet Müdahale Planı – TAMP) were identified, particularly the lack of integration of digital health technologies, which hindered resource allocation during the earthquake response and underscored the need for their inclusion in disaster management strategies. Conclusions This study demonstrates the effectiveness of digital health tools in enhancing disaster response and underscores the importance of integrating user-centered, deployable technologies to improve healthcare delivery in emergency settings.

A Comparative Crash-Test of Manual and Semi-Automated Methods for Detecting Complex Submarine Morphologies

Multibeam echosounders provide ideal data for the semi-automated seabed feature extraction and accurate morphometric measurements. In this study, bathymetric and raw backscatter data were initially used to manually delimit the reef morphologies found in an insular semi-enclosed gulf in the northern Aegean Sea (Gera Gulf, Lesvos Island, Greece). The complexity of this environment makes it an ideal area to “crash test” (test to the limit) and compare the results of the delineation methods. A large number of (more than 7000) small but prominent reefs were detected, which made manual mapping extremely time-consuming. Three semi-automated tools were also employed to map the reefs: the Benthic Terrain Modeler (BTM), Confined Morphologies Mapping (CoMMa), and eCognition Multiresolution Segmentation. BTM did not function properly with irregular reef footprints, but by modifying both the bathymetry and slope, the outcome was improved, producing accurate results that appeared to exceed the accuracy of manual mapping. CoMMa, a new GIS morphometric toolbox, was a “one-stop shop” that, besides generating satisfactory reef delineation results (i.e., detecting the same total reef area as the manual method), was also used to extract the morphometric characteristics of the polygons resulting from all the methods. Lastly, the Multiresolution Segmentation also gave satisfactory results with the highest precision. To compare the final maps with the distribution of the reefs, mapcurves were created to estimate the goodness-of-fit (GOF) with the Precision, Recall, and F1 Scores producing values higher than 0.78, suggesting a good detection accuracy for the semi-automated methods. The analysis reveals that the semi-automated methods provided more efficient results in comparison with the time-consuming manual mapping. Overall, for this case study, the modification of the bathymetry and slope enabled the results’ accuracy to be further enhanced. This study asserts that the use of semi-automated mapping is an effective method for delineating the geomorphometry of intricate relief and serves as a powerful tool for habitat mapping and decision-making.

Vibration vulnerability assessment of heritage structures in Lahore: a step towards sustainable conservation

Lahore is among the most important historical cities in Pakistan, and many cultural heritage structures, such as Shalamar Gardens and Lahore Fort, which are recognised as World Heritage Sites by UNESCO, are located there. The rapid increase in transit operations and infrastructure development in Lahore can increase the vulnerability of nearby heritage structures to ground-borne vibrations. In this study, a vibration vulnerability assessment of the heritage structures present in Lahore was performed with the help of the framework provided by the Federal Transit Administration (FTA), USA. A remote survey was carried out to gather valuable information regarding the distance between the heritage structures and potential vibrational sources, as well as the latitude and longitude of the heritage structures, via the web-based mapping tool Google Earth. A vibration vulnerability map of heritage structures was developed on the basis of a vibration vulnerability assessment to make it convenient for policymakers to visualise the present state of heritage structures with respect to vibration vulnerability. The performed vibration vulnerability assessment was validated by experimentally measuring vibrations at the Buddha’s Tomb (Buddha Ka Awa) heritage structure. The study revealed that 17% of the heritage structures present in Lahore are vulnerable to vibrations at present, with potential vibrational sources such as road traffic, metro trains, and rail traffic. The vibration vulnerability assessment revealed that 72% of the vibrationally vulnerable heritage structures in Lahore are most influenced by vibrations from road traffic. The results of this study can aid policymakers in creating intervention packages for vibrationally vulnerable heritage structures in Lahore in relation to their respective needs, which will make it possible to achieve the sustainable conservation of heritage structures.

Shedding light on pollination deficits: Cueing into plant spectral reflectance signatures to monitor pollination delivery across landscapes

AbstractPollination underlies plant yield, health and reproductive success in agricultural and natural systems worldwide. It is therefore concerning that declining animal pollinator populations compounded by growing demands for food are leading to rising pollination deficits, with globally significant economic and environmental implications.Despite this urgent issue, accurate and scalable tools to quantify and track pollination across useful spatiotemporal scales are lacking. Here, we propose to shed new light on pollination deficits, looking to remote sensing platforms as a transformative mapping and monitoring tool and a solution for pollinator conservation and crop management.Providing a synthesis of our current understanding of pollination‐triggered floral senescence and underlying ultrastructural and metabolic changes, we propose how spectral reflectance technologies could be applied to accurately detect pollination events in real‐time and at the landscape scale.Synthesis and applications: We highlight where research efforts can be targeted to produce scalable methods for identifying field‐relevant bioindicators of pollination. We provide guidance on how spectral imaging accompanied by machine learning and coupled with autonomous operation technologies will enable applications to detect pollination delivery across complex landscapes. Ultimately, such an ecological application will transform our quantitative understanding of pollination services and, by directly linking plant yields and health, will reveal pollination deficits at high resolution to help mitigate risks to food security and ecosystem functioning.

Partition Function Zeros of the Frustrated J1–J2 Ising Model on the Honeycomb Lattice

We study the zeros of the partition function in the complex temperature plane (Fisher zeros) and in the complex external field plane (Lee–Yang zeros) of a frustrated Ising model with competing nearest-neighbor (J1>0) and next-nearest-neighbor (J2<0) interactions on the honeycomb lattice. We consider the finite-size scaling (FSS) of the leading Fisher and Lee–Yang zeros as determined from a cumulant method and compare it to a traditional scaling analysis based on the logarithmic derivative of the magnetization ∂ln⟨|M|⟩/∂β and the magnetic susceptibility χ. While for this model both FSS approaches are subject to strong corrections to scaling induced by the frustration, their behavior is rather different, in particular as the ratio R=J2/J1 is varied. As a consequence, an analysis of the scaling of partition function zeros turns out to be a useful complement to a more traditional FSS analysis. For the cumulant method, we also study the convergence as a function of cumulant order, providing suggestions for practical implementations. The scaling of the zeros convincingly shows that the system remains in the Ising universality class for R as low as −0.22, where results from traditional FSS using the same simulation data are less conclusive. Hence, the approach provides a valuable additional tool for mapping out the phase diagram of models afflicted by strong corrections to scaling.

Self-reported public fast charging infrastructure demand: What do existing and potential electric vehicle adopters want and where?

Electric vehicles (EVs) promise emissions reduction, but adoption rates must increase to meet 2030 targets. Since public fast charging infrastructure (PFCI) is considered important to increase EV uptake, this research aims to assess its demand across existing and potential EV adopters in Ireland. Online mapping tools were used to capture 1635 self-reported locations where users prefer PFCI, along with other related preferences from 545 respondents. The results were analysed using a mixed-method approach including exploratory factor analysis (EFA), Geographic Information System (GIS), two-step cluster analysis, and qualitative analysis. The results indicate that expanding PFCI is critical for increasing EV uptake, with 81.4% of potential EV adopters showing a significant impact on uptake. Major deterrents identified include insufficient availability of public fast chargers followed by high charging costs. The qualitative analysis highlights existing EV users’ demand for improved end-user experiences, such as unified payment systems. Most preferred locations for PFCI installation include travel routes, followed by shopping centres/ supermarkets. Results also indicate that a reliable second-hand market would be key to increasing EV uptake. The findings from this study can serve policymakers in effectively rolling out PFCI, while the methodology employed can be replicated in other comparable study areas.

Environmental Justice and Sustainable Development: Cumulative Environmental Exposures and All-Cause Mortality in Colorado Counties

(1) Background: Colorado’s (CO) Environmental Justice mapping tool, CO EnviroScreen, quantifies environmental injustices through “EnviroScreen Scores”, highlighting areas likely affected by environmental health disparities. Identifying the specific scores most strongly associated with mortality could help prioritize interventions and allocate resources to address these issues. This study contributes to sustainable development goals by examining the relationship between environmental justice indicators and population health outcomes. By utilizing the CO EnviroScreen tool, we assess how cumulative environmental exposures and vulnerabilities impact mortality rates, providing insights for sustainable planning and public health policies. (2) Methods: We assessed the cross-sectional association between county-level all-cause mortality rates in CO, using 2019 data from the Colorado Department of Public Health and Environment, and three county-level component scores obtained from CO EnviroScreen: sensitive populations (i.e., health-related outcomes), environmental exposures (e.g., from air, water, noise), and climate vulnerability (i.e., risk of drought, flood, extreme heat, wildfire). A quasi-Poisson generalized linear model was utilized, incorporating covariates (county-level metrics for insufficient sleep, alcohol overconsumption, physical inactivity, and smoking) to explore associations adjusted for behavioral risk factors (n = 64 counties). (3) Results: The analysis revealed that a 10% increase in the “Environmental Exposures” component score was associated with a 3% higher all-cause mortality rate (95% CI: 1.00, 1.05), highlighting the importance of addressing environmental determinants for sustainable community health. No significant associations were observed for the “Sensitive Populations” or “Climate Vulnerability” component scores. (4) Conclusions: This study provides novel evidence of an association between the CO EnviroScreen score, particularly the environmental exposure component, and all-cause mortality rates at the county level in Colorado in 2019. The findings suggest that cumulative environmental exposures may contribute to geographic disparities in mortality risk, even after adjusting for key behavioral risk factors. These results underscore the importance of integrating environmental justice considerations into sustainable development strategies to promote equitable health outcomes and resilient communities. While our study demonstrates the utility of CO EnviroScreen in identifying areas at risk due to environmental factors, it does not establish a direct link to broader environmental justice outcomes. Further research is needed to explore specific environmental exposures and their direct impacts on health disparities to provide a more complete picture of environmental justice in Colorado.

A bibliometric review of functional ingredients and their efficacy in developing functional biscuits

Introduction Numerous studies have concluded that the functional ingredients benefit human health. Similarly, present times have seen exponential growth in functional food in bakery product segments like breads and biscuits. However, there is a lack of information on functional ingredients and their usefulness in developing functional bakery products. This bibliometric study addresses this gap by identifying the current research trends in functional ingredients. Objective To investigate current research trends on functional ingredients and their usefulness in developing functional biscuits. Method The study followed the “Scientific Procedures and Rationales for Systematic Literature Reviews” standards for retrieving literature. The study went through three major stages, “assembling”, “arranging”, and “assessing” to retrieve 612 articles from the Scopus database from 2013 to 2023. Through further filtering, 395 articles were selected. Result The analysis was conducted using R Studio and VOS viewer. The performance analysis and science mapping tools were used to evaluate the articles. The results showed a 5.76% annual growth in publication trends. The most researched functional ingredients were antioxidants, bioactive compounds, and dietary fiber. The review summarized the most studied foods used to develop functional biscuits and highlighted the most experimented technological advancements. Conclusion The study revealed the need for future research studies on functional ingredients with a focus on studying the implications of technical implications of technical advancements in extracting functional ingredients from foods. The study highlights the significance of future studies based on the acceptance of functional biscuits and their sensory properties focusing on the mass population. The study derives the knowledge on possible applicability of functional ingredients in developing new formulations from publications and their usefulness in developing new formulations. This insight on the applicability of functional ingredients provides an opportunity for biscuit/cookie manufacturing to boost consumption among the population to a new ascending graph.

Accuracy Assessment of Land Use Land Cover Classification Using Machine Learning Classifiers in Google Earth Engine; A Case Study of Jammu District

Abstract. LULC (Land Use and Land Cover) involves classifying and describing different land types and their usage. Using satellite imagery for LULC mapping is increasing in remote sensing. This study focuses on Jammu district in India, situated between mountain ranges from north and south make it eco-sensitive zone. Expanding of human activity and loss of natural resources make it vulnerable if mismanaged. Study of LULC is important because of it and this study deals with efficiency and accuracy of various machine learning classifiers for LULC. This study uses machine learning classifiers - Random Forest (RF), Support Vector Machine (SVM), Gradient Boosted Trees (GTB), and Classification and Regression Trees (CART) - for the task, leveraging Google Earth Engine (GEE). Sentinel-2 satellite data from January 1 to March 31, 2023, with specific spectral bands (B4, B3, B2 & B8, B4, B3), were used for image preprocessing. Six classes: built-up, water, agricultural land, fallow land, forest, and barren land, each with 100 sample points, were used for classification. After training, the classifiers' accuracy was evaluated using Overall Accuracy and Kappa Coefficient. The results showed RF as the top performer with an overall accuracy of 99.36% and a Kappa coefficient of 99.11%, followed by SVM, GTB, and CART. This highlights the effectiveness of machine learning classifiers, especially RF and SVM, in accurately mapping LULC patterns in Jammu district, suggesting RF's potential as a reliable tool for remote sensing-based LULC mapping.

The Sibling Sexual Behaviour Mapping Tool (SSBMT): Supporting practitioner confidence, planning and competency when responding to sexual behaviours between siblings

ObjectivesThis paper outlines the development and initial pilot of the Sibling Sexual Behaviour Mapping Tool (SSBMT). Building on the findings of the National Project on Sibling Sexual Abuse (King-Hill, McCartan, et al., 2023) and work by Yates and Allardyce (2023), the tool was devised with the aim of supporting frontline child-protection social workers during the initial stages of working with families where sibling sexual behaviours (SSB) may be present. MethodsThis paper explores the rationale for the SSBMT design, and preliminary findings from the first phase of resource evaluation. The evaluation process comprised a small pilot with social workers across seven local authorities in the United Kingdom (UK). The pilot study sought to assess the workability and effectiveness of the SSBMT within social work practice, providing stakeholder insight prior to its launch. The study employed mixed methods to gain feedback on the SSBMT design, accessibility, ease of use and compatibility with current child protection work processes, which included training of 50 practitioners, surveys with 20 professionals and two semi-structured interviews. FindingsThe findings suggest that the SSBMT increased practitioner confidence when working with SSB, and promoted holistic, whole-family approaches in the initial stages of work with children and families affected by SSB.

Vocabulary mapping for archaeological infrastructure

This article reports and reflects on vocabulary mapping techniques, tools and experience from the ARIADNE European archaeological infrastructure projects, where the widely differing terminology of subject indexing in the different partner languages posed significant challenges for effective data integration. The Getty Art & Architecture Thesaurus is employed as a central spine vocabulary for partners to map their native vocabularies and term lists – a hub structure enables a multilingual search capability via vocabulary mapping. Mappings are expressed via SKOS mapping relationships and output as structured JSON for use in the overall data aggregation process and in the ARIADNE portal. The approach followed offers some automatic support for final intellectual judgement. The method can be characterised as providing lexical support in an interactive tool that aims to intuitively visualise semantic context. The experience of partners in producing the vocabulary mappings is discussed in light of previous work in this area. Reflections on lessons learned both for the immediate project and for vocabulary mapping in general contribute to the conclusions. Future search functionality could take account of available vocabulary mappings via a range of search options, such as query expansion including compound mappings and mapping types. Further work on mapping guidelines and metadata is recommended.

Exploring the Role of Mind Mapping Tools in Scaffolding Narrative Writing in English for Middle-School EFL Students

The purpose of this study was to investigate the effects of scaffolded English writing instruction using mind mapping tools on English narrative writing proficiency among Chinese middle-school EFL students. Specifically, we examined its effects on four subcomponents of narrative writing compositions: lexical complexity, grammatical complexity, accuracy, and fluency. Non-equivalent group pre-test–post-test design was employed in this study. The participants were 55 eighth-grade students in an English class. The data were collected through two writing tests administered before and after a two-month intervention. The results showed that students’ writing demonstrated significant improvements in lexical complexity, grammatical complexity, accuracy, and fluency. These findings suggest that scaffolded writing instruction with mind mapping can effectively enhance multiple dimensions of writing skills in adolescent EFL learners. This study provides insights into the application of using mind mapping to scaffold EFL learners’ narrative writing proficiency.

World of Crayfish™: a web platform towards real-time global mapping of freshwater crayfish and their pathogens

Freshwater crayfish are amongst the largest macroinvertebrates and play a keystone role in the ecosystems they occupy. Understanding the global distribution of these animals is often hindered due to a paucity of distributional data. Additionally, non-native crayfish introductions are becoming more frequent, which can cause severe environmental and economic impacts. Management decisions related to crayfish and their habitats require accurate, up-to-date distribution data and mapping tools. Such data are currently patchily distributed with limited accessibility and are rarely up-to-date. To address these challenges, we developed a versatile e-portal to host distributional data of freshwater crayfish and their pathogens (using Aphanomyces astaci, the causative agent of the crayfish plague, as the most prominent example). Populated with expert data and operating in near real-time, World of Crayfish™ is a living, publicly available database providing worldwide distributional data sourced by experts in the field. The database offers open access to the data through specialized standard geospatial services (Web Map Service, Web Feature Service) enabling users to view, embed, and download customizable outputs for various applications. The platform is designed to support technical enhancements in the future, with the potential to eventually incorporate various additional features. This tool serves as a step forward towards a modern era of conservation planning and management of freshwater biodiversity.

Planetary health in action: developing a heatwave vulnerability tool for primary care.

Heatwaves are becoming longer and more frequent. Despite the availability of open environmental data, little is operable and formatted for primary care use. Create a user-friendly online mapping tool to assess the vulnerability of communities to heatwaves for use by primary care practitioners. This study questioned what knowledge needed to be deployed, who needed to participate and how the knowledge should be shared. A participatory action-research project based on knowledge mobilization in France as part of the Green Data for Health Challenge. Knowledge was summarized on the factors most affecting heatwave vulnerability in a collaborative process, enabling a consensus on data variables and mobilised content for the online tool. Purposive sampling included primary care stakeholders with Regional Health Agencies (ARS), Public Health France, and data scientists. Nineteen participants participated in ten co-construction workshops, a brainstorming carousel strategy and five weekly co-design meetings between December 2022 and June 2023. The heatwave vulnerability variable was constructed using surface temperature, social deprivation, vegetation coverage, and presence of air conditioning equipment. Identified experts mobilized data on the national composite indicator at the communal level for heatwave morbidity. There is no standard platform for sharing environmental data in France. This co-creation study offers a new approach to incorporating environmental data on heatwaves into primary care consultations. We demonstrate the importance of knowledge mobilisation in primary care to bridge the research-practice gap. Integrating primary care records with environmental data may promote broader applications for planetary health research.

MapGPT: an autonomous framework for mapping by integrating large language model and cartographic tools

ABSTRACT The mapping process generally involves intricate operations, such as symbol design, layout design, and text annotation, demanding a high level of professional expertise. The high requirement for map producers hinders the promotion and widespread adoption of mapping. Consequently, researchers are concentrating on techniques to automate and enhance the intelligence of the mapping process. For example, some studies attempt to train deep learning models for mapping, including methods like map style transfer. However, these approaches typically treat the entire map as a global input and generate a new map as output, lacking the flexibility to consider and control detailed elements within a map. Therefore, in this paper, we propose a large language model-based intelligent mapping framework, termed MapGPT, which can be used for mapping by considering the map as an integration of various map elements. Specifically, multiple professional mapping tools are designed in MapGPT, and each tool is designed to control a corresponding map element. With these tools, a large language model is used to first understand the demand of users based on mere natural language descriptions, and subsequently automatically invoke appropriate tools in sequence to generate a map. Furthermore, by utilizing a memory component to store interaction information, users can interact with MapGPT through conversation to adjust map elements such as color and position. In conclusion, MapGPT offers user-friendly mapping experience, showing potential to be a mapping assistant for professional map producers. A comprehensive demonstration of this framework is provided in a visual case study video, accessible at https://github.com/AGI-GIS/MapGPT.