Web Map Research Articles

Placing the Location Outtakes of Shoah

This article considers how to read the location outtakes of Claude Lanzmann’s film Shoah (1985). These film reels — now digitized and available on the United States Holocaust Memorial Museum’s website — often seem to offer little content, and demand different ways of viewing to make any sense of them. I link them to two other types of viewing experience: watching landscapes filmed by Danièle Huillet and Jean-Marie Straub and using online maps such as Google Street View to identify and navigate particular locations. Engaging with the outtakes in this way involves a consideration of the events of the Holocaust indexed by the locations and the images that Lanzmann made of them. It also makes it possible to glean something of the working methods of Lanzmann and his team. Finally, it offers a particular spatial experience in its own right, which, I argue, can serve as a form of Holocaust memorialization.

Multiscale Visualization of Surface Motion Point Measurements Associated with Persistent Scatterer Interferometry

Persistent scatterer interferometry (PSI) has been proven to be a robust method for studying complex and dynamic phenomena such as ground displacement over time. Proper visualization of PSI measurements is both crucial and challenging from a cartographic standpoint. This study focuses on the development of an interactive cartographic web map application, providing suitable visualization of PSI data, and exploring their geographic, cartographic, spatial, and temporal attributes. To this end, PSI datasets, generalized at different resolutions, are visualized in eight predefined cartographic scales. A multiscale generalization algorithm is proposed. The automation of this procedure, spurred by the development of a web application, offers users the flexibility to properly visualize PSI datasets according to the specific cartographic scale. Additionally, the web map application provides a toolset, offering state-of-the-art cartographic approaches for exploring PSI datasets. This toolset consists of exploration, measurement, filtering (based on the point’s spatial attributes), and exporting tools customized for PSI measurement. Furthermore, a graph tool, offering users the capability to interactively plot PSI time-series and investigate the evolution of ground deformation over time, has been developed and integrated into the web interface. This study reflects the need for appropriate visualization of PSI datasets at different cartographic scales. It is shown that each original PSI dataset possesses a suitable cartographic scale at which it should be visualized. Innovative cartographic approaches, such as web applications, can prove to be effective tools for users working in the domain of mapping and monitoring the dynamic behavior of surface motion.

Research on the Walking Accessibility Attenuation of Green Open Space in Mountainous Cities Based on Web Map Path Tracking

Research on the Walking Accessibility Attenuation of Green Open Space in Mountainous Cities Based on Web Map Path Tracking

Identification of Priorities for Colorectal Cancer Screening Interventions Among US Hispanic/Latino Populations.

Objectives. To identify nationwide census tract‒level areas where improving colorectal cancer (CRC) screening uptake via targeted local preventive intervention may benefit Hispanic or Latino/a (H/L) groups defined by region or country of origin. Methods. Using 2021 Centers for Disease Control and Prevention PLACES and American Community Survey data, we applied geographically weighted regression and Getis-Ord Gi* hot spot procedures to identify CRC screening priority zones for H/L groups in the United States. Priority zones can be conceptualized as census tracts with strong inverse associations between percentage of a particular H/L group in the population and CRC screening rate, after adjusting for socioeconomic deprivation and lack of insurance. Results. We identified 6519, 3477, 3522, 1069, and 1424 census tract CRC screening priority zones for H/L communities of Mexican, Puerto Rican, Central/South American, Dominican, and Cuban heritage, respectively. Priority zones for H/L groups had strong spatial heterogeneity, and overlap of geographic patterns among H/L groups varied by region. Conclusions. Our findings and interactive web map may serve as a translational tool for public health authorities, policymakers, clinicians, and other stakeholders to target investment and interventions to increase guideline-concordant CRC screening uptake benefitting specific H/L communities in the United States. (Am J Public Health. 2024;114(S6):S515-S524. https://doi.org/10.2105/AJPH.2024.307733) [Formula: see text].

Where Is That Crater? Best Practices for Obtaining Accurate Coordinates from LROC NAC Data

The Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images and their derived products make up one of the highest-resolution and most spatially accurate data sets available for the Moon, making them a crucial resource for planners of landed lunar missions. However, it is essential to understand the best uses for each data type and the limits on the accuracy and resolution of the data sets available. With this understanding, users can better interpret and communicate their results. In this paper, we describe what assumptions may be made about the accuracy of LROC NAC images and the various derived products created by the LROC team. NAC digital terrain models and their corresponding orthophotos have the best accuracy of all NAC products, usually better than 10 m horizontally, and should be used where available. Other controlled NAC products usually have accuracies better than 30 m. For areas without controlled products, we describe how to process NAC images to obtain coordinates with the highest possible accuracy. We also recommend best practices for data users interacting with LROC data through online map servers, such as QuickMap or Lunaserv, and for processing LROC data locally using the US Geological Survey Integrated Software for Imagers and Spectrometers.

Mapping regional implementation of ‘Making Every Contact Count’: mixed-methods evaluation of implementation stage, strategies, barriers and facilitators of implementation

BackgroundThe Making Every Contact Count (MECC) programme provides training and materials to support public-facing workers to encourage health-promoting behaviour change by using the day-to-day interactions between organisations and individuals. This...

Paper 06: Lower Socioeconomic Status is Associated with Recurrent Shoulder Instability Before Surgical Shoulder Stabilization

Objectives: Social determinants of health (SDOH) are comprised of a patient’s environmental conditions including social and economic factors, which influence access to health care and resources. Growing evidence in orthopaedic surgery has revealed that SDOH factors lead to differential access to care and ultimately health disparities after surgery. For shoulder instability, previous literature has demonstrated that the number of previous dislocations before a stabilization procedure increases the risk of recurrent instability after the procedure. The purpose of this study was to investigate the impact of SDOH on the number of dislocation events before surgical intervention. Methods: A retrospective review of patients who underwent shoulder stabilization surgery at a health system in a large metropolitan area between January 1, 2021 and April 13, 2023 were identified. Patients’ demographic and social determinants variables were extracted using the electronic medical record. Social Vulnerability Index (SVI) socioeconomic subscore and Area of Deprivation Index (ADI) were collected using online mapping data based on patient zip codes. The number of dislocation events and the time they occurred were determined using clinical charts. Operative variables collected included the date of surgery to determine the time from clinical presentation to surgery and the procedure performed. Univariate linear regression analysis was used to evaluate potential predictors of increasing time to presentation and increasing time to surgery. Univariate logistic regression analysis was also performed of all potential predictors of having >1 dislocation event. A multivariate model was then created using all predictors with a P value < 0.05 in the univariate models. Results: There were 106 patients who underwent shoulder surgery for instability, including arthroscopic and open stabilization, and had complete social determinant data, with 54% (n = 57) identifying as White, 29% (n = 31) as Black/African American, and 17% (n = 18) as other. A total of 38 (35.8%) patients suffered 1 dislocation (single dislocation cohort) before undergoing surgery, and 68 (64.2%) experienced > 1 dislocation (recurrent cohort) before surgery. No significant variables were associated with increasing time to presentation or surgery. Univariate logistic regression revealed that decreasing age (odds ratio [OR] 0.94 [95% confidence interval (CI) 0.89- 0.99]; P = 0.02), decreasing body mass index (BMI) (OR 0.90 [95% CI 0.83-0.98]; P = 0.02), increasing SVI (OR 1.21 [95% CI 1.05-1.38]; P = 0.006), and increasing area deprivation index (ADI) (third tercile compared to first, OR 6.04 [95% CI 2.05-17.8]; P = 0.003) were associated with increased odds of having > 1 instability event before surgical intervention. There was no association with race found in terms of number of dislocations before surgery. Multivariate logistic regression modeling revealed that decreasing BMI (OR 0.87 [95% CI 0.78-0.98]; P = 0.02) and increasing ADI (third tercile compared to first tercile, OR 7.46 [95% CI 1.26-44.2]; P = 0.02) were associated with increased odds of having > 1 instability event before shoulder instability surgery. Conclusions: Lower socioeconomic status, as measured by ADI, is an independent predictor of a higher likelihood of recurrent instability before shoulder stabilization surgery. Recognizing these relationships may motivate surgeons to create pathways to prevent these treatment disparities among shoulder instability patients. Further studies are required to examine if these SDOH variables lead to disparities in postoperative outcomes.

Enhancing Vehicle Navigation and Safety through Integration of Pre-Recorded Maps with Vehicle Control Unit

Powerful navigation and safety systems are vital to ensuring autonomous (DV) or semi-autonomous vehicles continue well in varied environmental conditions. However, in such a world those who depend on that internet connectivity to navigate their car would have problems in areas where network reliability is less than perfect. The purpose of this work is to investigate the feasibility and advantages of combining offline mapping with locally sensed positioning systems for better vehicle navigation and safety. While connected to the internet, vehicles cache offline maps and use local sensor information (such as GPS) from their inertial navigation system or computer vision without needing continuous access. The research outlines a hardware-software architecture with embedded offline map data storage, edge-based road following algorithms and an integration mechanism to advanced driving assistance systems (ADAS). In the evaluation of performance, accuracy assessments with online mapping systems are considered for offline maps and how these can have implications on end driver usability as well impacts on real-world autonomous driving technologies. The results suggest that offline mapping and on-board sensor-based localization would be able to improve vehicle contextual navigation performance in diverse driving scenarios.

Improved assessments of subsurface projects: Systematic mapping of geosystem services and a review of their economic values

Awareness of the subsurface and its multitude of resources is generally low and decisions on access to subsurface resources are often guided by a ‘first come, first served principle’. Although not yet fully developed, the concept of geosystem services has been put forward to make subsurface resources more visible and acknowledged in decision-making. This study (1) illustrates a systematic mapping of effects on geosystem services using a process-oriented perspective in two conceptual case studies; (2) translates the mapped effects into costs and benefits items in a qualitative cost-benefit analysis (CBA) context; and (3) presents a systematic review of economic valuation studies of geosystem services to investigate the available support for a quantitative CBA. The findings suggest that systematic mapping of effects on multiple geosystem services can inform different types of assessment methods and decision-makers on trade-offs and provide a basis for well-informed and responsible decisions on subsurface use. Combining such mapping with a CBA can further strengthen decision support through indications of the net effects on human well-being. However, although economic valuation of non-market geosystem services is possible using established valuation methods, such studies are scarce in scientific literature. Thus, although a CBA can provide a basis for supporting decisions on subsurface use from a consequentialist perspective, full quantification of all effects may require great efforts, and it needs to be complemented with other methods to capture the full range of values the subsurface can provide. This study also highlights that depending on the context, supporting and regulating geosystem services can be either intermediate or final services. Therefore, if geosystem services are to be included in the abiotic extension of CICES, in which supporting services by definition are excluded, reclassification of the supporting geosystem services should be considered not to risk being overlooked in economic valuation and CBA.

A survey of machine learning techniques for improving Global Navigation Satellite Systems

Global Navigation Satellite Systems (GNSS)-based positioning plays a crucial role in various applications, including navigation, transportation, logistics, mapping, and emergency services. Traditional GNSS positioning methods are model-based, utilizing satellite geometry and the known properties of satellite signals. However, model-based methods have limitations in challenging environments and often lack adaptability to uncertain noise models. This paper highlights recent advances in machine learning (ML) and its potential to address these limitations. It covers a broad range of ML methods, including supervised learning, unsupervised learning, deep learning, and hybrid approaches. The survey provides insights into positioning applications related to GNSS, such as signal analysis, anomaly detection, multi-sensor integration, prediction, and accuracy enhancement using ML. It discusses the strengths, limitations, and challenges of current ML-based approaches for GNSS positioning, providing a comprehensive overview of the field.

An enhanced and expanded Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)

AbstractDetailed, accurate information on flow patterns in river channels can improve understanding of habitat conditions, geomorphic processes, and potential hazards to help inform water management. Data describing flow patterns in river channels can be obtained efficiently via image‐based techniques that have become more widely used in recent years as the number of platforms for acquiring images has expanded and the number of algorithms for inferring velocities has grown. Image‐based techniques have been incorporated into various software packages, including the Toolbox for River Velocimetry using Images from Aircraft (TRiVIA). TRiVIA is a freely available, standalone computer program that provides a comprehensive workflow for performing particle image velocimetry (PIV)‐based analyses within a graphical interface. This paper summarizes major enhancements incorporated into the latest release of TRiVIA, version 2.1. For example, a new Tool for Input Parameter Selection (TIPS) provides guidance for specifying key inputs to the PIV algorithm by allowing users to explore relationships between flow velocity, pixel size, output vector spacing, and frame interval. Improved visualization capabilities include the ability to create streamlines and display PIV output on an interactive web map. The program now provides greater flexibility for importing field data in various formats and selecting which observations to use for accuracy assessment. The most substantial additions to TRiVIA 2.1 are the ability to integrate bathymetric information with image‐derived velocity estimates to calculate river discharge and to use images acquired from moving aircraft to efficiently map long segments of large rivers to support habitat assessment, contaminant transport studies, and a range of other applications.

An updated checklist of the Hydradephaga of Switzerland (Coleoptera)

An updated checklist of the Swiss species of Hydradephaga (Dytiscidae, Gyrinidae, Haliplidae, Hygrobiidae, Noteridae) is presented and briefly discussed. A total of 139 species is retained as part of the Swiss fauna, while 53 species, which have been recorded in Switzerland in the past, are excluded from this list, either due to insufficient documentation or because their records were based on misidentified material. In contrast to previous works, the present study also provides, for the first time, distribution maps through an online map server for all Hydradephaga species in Switzerland, based on over 43,000 occurrences obtained from specimens held in museums and private collections, as well as literature data. The occurrences of the rarest species of Switzerland, as well as data on species erroneously cited in Switzerland, are presented and discussed.

Examining chlorophyll-a concentrations in tropical reservoirs under various land use changes using Sentinel – 2 and Google Earth engine – Bhadra and Tungabhadra, India

The understanding of spatio-temporal variation in land use and land cover (LULC) patterns is crucial for managing catchment land use planning, as it directly influences of tropical reservoir water quality and the subsequent Nutrient Contamination (NC) of unmonitored water bodies. The current research attempts to accurately measure the influence of LULC and its associated determinants on the quantities of NC loads by using Chl-a as a proxy, within tropical reservoirs, i.e. Bhadra and Tungabhadra, located in same river catchment. This Chl-a spread calculated by the Maximum Chlorophyll Index (MCI) derived from Sentinel 2 satellite data products covering the period from July 2016 to June 2021 were done using Google Earth Engine (GEE) platform. The validation analysis confirms the robustness of the methodology with a strong correlation between MCI-calculated values and EOMAP (Earth Observation and Environmental Services Mapping) Chl-a (μg/L) data points for both reservoirs, Bhadra (R2 = 0.64) and Tungabhadra (R2 = 0.68). The findings reveal that, Tungabhadra reservoir consistently exhibits an excessive spatial distribution of Chl-a spread area (17 km2 to 335 km2), reflecting nutrient-rich water inflows, particularly evident during the post-monsoon period. This notable rise could be linked to harvesting the Kharif crop, resulting in elevated nutrient concentrations. In contrast Bhadra reservoir, dominated by forested areas, maintains relatively lower Chl-a spread areas (<20 km2), highlighting its pivotal role in maintaining water cleanliness and serves as a riparian boundary. In addition, the changes in LULC classes show a strong relationship with variation in Chl-a during the studied period, for the Bhadra Reservoir R2 = 0.51 (F- statistics = 3.983, p = 0.021), and the Tungabhadra Reservoir R2 = 0.802 (F- statistics = 7.489, p = 0.0143). This highlights how changes in land use significantly shape contamination dynamics, deepening our understanding of nutrient inputs and contamination drivers in tropical reservoirs.

Exploring Spatiotemporal Patterns of Frequently Congested Urban Road Segments Based on Multi-Source Data: A Case Study of China’s Super-Large Cities

Traffic congestion has emerged as a primary obstacle to sustainable urban development and the livability of life. Seven super-large cities in China were the study area of this paper as the problem tends to be more severe in larger cities. First, frequently congested road segments were defined based on real-time traffic condition data from online maps. Then, the spatiotemporal characteristics of these road segments were explored. Finally, mitigation measures and ways to ease traffic congestion in big cities were discussed against the background of rapid urbanization. The results demonstrate that the proportion of frequently congested road segments takes up between 1.8% and 9.87% of China’s super-large cities. There is a positive correlation between the overall operational efficiency of the urban road network and the ratio of frequently congested road segments. Temporally, commuting patterns during the morning and evening are displayed by these road segments. Spatially, they also demonstrate significant clustering characteristics. The proportion of frequently congested road segments caused by the lack of alternative routes is about 20% in the six cities studied. The data for Chongqing is even higher at 42.94%. Therefore, congestion caused by the lack of alternative routes may be underestimated by existing research. This work could provide theoretical support for transportation planning and management. As cities continue to expand and urbanization accelerates, the significance of this research will increase.

A participatory mapping approach to capturing perceived walkability

We present a novel method for capturing citizens’ views on perceived walkability. Peoples’ decisions to use any transport mode are determined by perceived accessibility, thus perceived walkability is vital to the decision to walk for transport. To date, approaches to understanding perceived walkability, such as detailed ‘walkability audit’ instruments, have been difficult to scale and linked only to respondents’ residential locations. In contrast our research uses an online mapping tool designed to rapidly capture vague knowledge about places. Respondents use a map interface to spray-paint the most and least walkable areas across the city of Sydney, Australia, along with defining the area they regularly walk from home and where they would be willing to walk. They also provide free-text input to explain their responses. This approach enables data collection of respondents’ holistic understanding of the walkability of different areas based on the local knowledge and experience of the city. Pilot results together with qualitative analysis of text submitted in response to open ended questions are presented to demonstrate the feasibility, face validity and potential of the method. A comparison with an accessibility-based walkability index, WalkTHERE, for Sydney is shown. Results are broadly aligned, but the perceived walkability results presented highlight the negative environmental quality of walking near high-traffic roads and the positive aspects of natural and water views, which are not captured in this walkability index. Perceived walkable areas around the home were on average similar in overall area to the common standard of 15-minute buffers, but longer in their longest dimension, and have irregular shapes. Detailed methodology for analysis of the online mapping inputs is provided. This method has potential for rapid yet rich data collection, particularly when used together with a walkability model to understand differences which can point to localised problems with walking environment quality.

Citizens as Environmental Sensors: Noise Mapping and Assessment on Lemnos Island, Greece, Using VGI and Web Technologies

Noise caused by industrial activities, urban life and traffic has a negative impact on people's health and quality of life. Environmental noise mapping is an important tool to study and address this problem. Through specialized measurement and analysis techniques, it is possible to identify areas at high risk of noise and under-stand the extent of the problem. This information can be used to develop strategies to protect the population exposed to high noise levels. In this paper, a method for collecting, processing, managing and mapping spatial noise data based on Volunteered Geographic Information (VGI) and web mapping technologies is present-ed. The proposed method uses noise pollution data collected from citizens using mobile devices. This includes both on-site measurements and information on users' subjective perceptions, providing a comprehensive picture of the problem. This enables authorities to take into account the individual perceptions and needs of citizens when planning noise mitigation measures, which has not been the case in the inter-national literature to date. To achieve this goal, a prototype of a free and open-source tool called "Noise Pollution" was developed. The proposed methodology and the resulting system are intended to help improve the quality of life in noisy environments and protect the health of citizens. They provide a practical tool for collecting and analyzing noise data and enable the development of noise mitigation strategies for the benefit of the population exposed to high noise levels.

OnMapGaze and GraphGazeD: A Gaze Dataset and a Graph-Based Metric for Modeling Visual Perception Differences in Cartographic Backgrounds Used in Online Map Services

OnMapGaze and GraphGazeD: A Gaze Dataset and a Graph-Based Metric for Modeling Visual Perception Differences in Cartographic Backgrounds Used in Online Map Services

Pembuatan Modul Ajar Peta Online untuk Masyarakat Umum

One of the conveniences on the internet is the existence of online maps, where with online maps, if someone wants to go to an area, they no longer need to look for information by asking other people, but can use an online map called Google Maps. In this community service, a teaching module was created to create online maps with Google My Maps which is expected to help the general public who want to develop map facilities provided by the internet, in this case Google. The method of implementing this community service is planning, analysis, collecting data via the internet (which includes Longitude coordinates, Latitude coordinates, and names of tourist attractions), implementing online map making, and making reports. The data used as a sample is map data of tourist attractions in Pacitan Regency. The result of this community service is a teaching module or activity report that can be used to develop map facilities provided by the internet, in this case Google.

Methodology for burned areas delimitation and fire severity assessment using Sentinel-2 data. A case study of forest fires occurred in Spain between 2018 and 2023

The objective of this article was to develop a methodology for the burned areas delimitation and fire severity assessment in forest fires occurred in Spain between 2018 and 2022. As input data, this study was based on the use of Sentinel-2 spectral indices, which are characterized by having spectral bands in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions, allowing a high distinction between burned and unburned areas, and between different fire severity degrees too. All possible combinations between Sentinel-2 bands applied to a spectral normalized difference index (SP) were analyzed, along with the most commonly used burn spectral indices in remote sensing as the Burned Area Index (BAI), the Burned Area Index for Sentinel-2 (BAIS2), the Mid-Infrared Burn Index (MIRBI), the Normalized Burn Ratio (NBR), the Relativized Burn Ratio (RBR) and the relative differential Normalized Burn Ratio (RdNBR). In addition, in order to delete confusions between burned area and the presence of other land cover areas, the Sentinel-2 Global Land Cover (S2GLC 2017) and the temporal differences between pre-fire and post-fire dates were obtained for each spectral index (dSP). The results were compared by: in the case of burned areas, the Emergency Mapping Service (EMS) and the Galicia forest service; in the case of fire severity, using field plots classified as in Ruiz-Gallardo et al. (2004) study (null, low, moderate and high severity). The final statistic results obtained showed that the dNBR2 spectral index (using B11 and B12 Sentinel-2 spectral bands) provided the highest results of burned area delimitation (7% of commission error and 3% omission error, respectively) whereas, the combination of the BAIS2, the NBR and the modified Normalized Burn Ratio (NBR2, using B7 and B12 Sentinel-2 spectral bands), used in areas with low, mix and full vegetation respectively, provided the highest results in fire severity assessment (kappa statistic, F1-score and Balanced Accuracy equal to 0.87, 0.86 and 0.92, respectively). The methodology developed in this work allows obtaining accurate maps of burned area and fire severity in Spain, contributing to the reinforcement of national forest fires statistics.

Application Design For Palm Oil Ramp Search in North Bengkulu Web Based Gis

The development of the world of oil palm plantations in the Bengkulu region is growing very rapidly and one of them is the North Bengkulu district. With the increasing number of oil palm plantations, most people do not know where to sell the products from their oil palm plantations. The lack of available information and the unclear distance to the location makes it difficult for farmers to sell the products from their plantations. This research is aimed at solving problems by creating new applications. a smartphone (Android) based application which aims to provide solutions to the public so that they can easily find out where they are and how far they have to go to reach the oil palm road they want without being limited by time or place. GIS-based applications have been proven to have a major impact on economic and social development, and the use of GIS has become a current trend. The GIS application was developed to help search for and locate oil palm RAMPs. The aim of this research is to help oil palm farmers find and determine the best locations to sell their crops. They can also compare the selling prices of the most expensive palm oil. For this application, the Android operating system is combined with Google Maps, a web-based virtual world map service created by Google. This application has features such as the name of the palm ramp owner, a map of the palm ramp location, palm oil prices, location suggestions, palm oil views, application instructions, and much.