Web Map Research Articles

Assessment of Farmer Characteristics and Design Preferences on Location-based Pest and Disease Reporting Service for Digital Agriculture: A Preliminary Analysis

User interface design is critical for ensuring that agricultural technology is accessible and usable for farmers. Usability and accessibility for farmers can boost crop yields and lower losses. Therefore, it is crucial to understand the preferences and needs of farmers, particularly in terms of user interface design. This study examines the preferences of chili farmers for user interface elements in reporting chili pests and diseases and investigates how farmer characteristics (i.e. age categories and educational backgrounds) influence these preferences. Data was collected from a group of chili farmers in Batu Pahat using a survey instrument and field experimental tasks that list several user interface elements according to the tasks given in relation to disease and pest attack reporting information for chili crops. The study includes user interface-related tasks for online map services such as Google Maps and Waze, which aim to report the location of attacks. The study’s findings underscore the significance of user interface elements in shaping user experiences. Google Maps emerged as the favored navigation service, highlighting the importance of a straightforward and user-friendly interface. The study suggests that enhancing user knowledge of lesser-known functions can lead to higher satisfaction and loyalty. In agricultural applications, visual cues and intuitive design, exemplified by the “Picture Radio Button” and “Horizontal Date” elements, were well-received. Overall, this study highlights the importance of considering farmers’ demographic characteristics in designing user interfaces for agricultural technology. By doing so, agricultural technology can be made more accessible and usable for farmers, ultimately leading to more effective management of chili pests and diseases. The findings of this research will yield advantages by aiding farmers, specifically in Malaysia, in transitioning into intelligent farmers, aligning with the objectives of the Shared Prosperity Vision 2030 and the realization of smart agriculture in Malaysia. Furthermore, this endeavor is consistent with the Food and Agriculture Organization of the United Nations (FAO) initiative aimed at digitizing farmers, encompassing smallholder farmers as well.

Physical activity interventions to improve mental health and wellbeing in university students in the UK: A service mapping study

Background and aimsStudent mental health is of increasing concern: over a quarter of UK students report a mental health issue, and reactive support services cannot cope with increasing demand. Physical activity (PA) is associated with improved mental health in higher education settings. Universities are well placed to offer PA to promote students’ mental health and wellbeing. This study represents the first attempt to establish how many UK universities offer PA-based interventions to improve mental health and wellbeing of students, and what these interventions entail. We sought to identify, and code the characteristics of, interventions regarding how to encourage regular PA. MethodsAll UK universities were contacted and asked to report PA provisions available to students. We extracted key information from intervention descriptions, including using the Behaviour Change Technique (BCT) Taxonomy v1, to characterise component behaviour change strategies. ResultsOf 143 public UK universities identified, 125 (87%) responded to our request. Of these, only 45 (36%) universities had PA provisions in place, with a total of 54 interventions available across the country, each delivered for between 6 and 12 weeks. Most interventions were tailored (82%) and used BCTs focused on instructing students on how to perform physical activity (61%), restructuring the environment to facilitate activity (54%), and behavioural goal setting (46%). ConclusionsOnly a minority of UK universities offer PA interventions to students. These offerings focus on enhancing students' capabilities and giving greater opportunities for PA. Universities should consider offering greater PA provision to students, and address students’ motivation to engage in PA.

Effects of web geographic information system (GIS) technology and curriculum approaches on education for disaster risk reduction

Abstract. Disaster risk reduction education (DRRE) is a strategy to mitigate the harmful effects of disasters. The implementation of DRRE in schools is on the rise, and web geographic information system (GIS) technology in DRRE is becoming increasingly prevalent. However, little knowledge exists about whether students can improve their understanding of disaster risk reduction (DRR) through web GIS technology and which factors affect their learning of web hazard maps. This study has provided materials and curricula for DRR education in Chinese and Japanese high schools and utilized them eight times in the classroom between 2020 and 2022. These classes had three forms of implementation due to the COVID-19 pandemic: online, on-site and online–on-site mixed. The students first answered a pretest. Then they learned about DRR using the explanatory web pages and the web hazard maps by answering quizzes shown on the pages. After that, they answered a posttest and a questionnaire. Most students exhibit improved results after utilizing the digital DRR materials. This improvement is pronounced for the on-site implementations, while the mixed online–on-site implementation proves less effective. The pretest scores for the implementations using local disaster cases are low, but the subsequent improvement is significant. The ability of students to utilize electronic devices affects the learning of GIS-related content rather than other aspects. The daily use of online maps and attention to disaster prevention and mitigation affect the learning of all the materials. Increasing the use of online hazard maps is key to realizing social DRR.

Incorporating speed forecasting and SOC planning into predictive ECMS for heavy-duty fuel cell vehicles

This study presents a novel approach specifically designed for real-world driving scenarios of heavy-duty fuel cell vehicles, named P-ECMS. The P-ECMS addresses both charge-sustaining and charge-depleting modes of the battery to optimize the vehicle’s energy management. To this aim, the P-ECMS integrates a velocity prediction layer and a SOC planning layer. The velocity prediction layer utilizes a realistic driving dataset obtained from GT-Real Drive, using information from the TEN-T routes, to accurately predict the speed of the vehicle. The SOC planning layer, leveraging information from a map service provider, plans a target SOC trajectory at the beginning of the driving mission. It employs a neural network trained on the policy obtained from a standard optimized ECMS for various driving cycles and initial SOC values, aiming to achieve a final SOC of 30%. In charge-sustaining operations, the P-ECMS is compared to a conventional Adaptive-ECMS, the reference ECMS (Standard-ECMS), and a rule-based strategy across the HDDT driving cycle. The evaluation focuses on battery SOC sustenance, equivalence factor evolution, and hydrogen consumption. Results show that both the P-ECMS and the A-ECMS outperform the S-ECMS in terms of SOC sustenance, with the P-ECMS achieving a significant 2% reduction in hydrogen consumption compared to the A-ECMS. The study demonstrates that the P-ECMS advantages extend to battery discharge conditions, as it achieves a remarkable reduction in consumption compared to an optimal Charge-Depleting/Charge-Sustaining (up to 5%) when employing a linear battery discharge planning. The integration of the SOC planning layer proves additional benefits, and a comparison between the P-ECMS with linear battery discharge planning and the P-ECMS with the SOC planning layer integrated shows the advantages of SOC trajectory planning for different segment lengths. The study suggests an optimal segment length between 3 km and 8 km, obtained with the necessary data from a map service provider.

Quadtree decomposition-based Deep learning method for multiscale coastline extraction with high-resolution remote sensing imagery

As one of the most critical features on the earth's surface, coastal zone mandates high-quality extraction of its representative feature, the coastline. Prior methodologies primarily emphasize on edge and small-scale information. However, during large-scale image processing, misclassification might occur due to the difficulty in determining whether a local area belongs to the land or sea. To address this, we propose a deep learning-based multiscale coastline extraction algorithm in this study. It comprises a multiscale coastal zone dataset built upon a tile map service structure and a scene classification-based multiscale coastal zone classifier, employing quadtree decomposition to identify coastal zones from low to high levels. Contrasting with conventional semantic segmentation, the scene classification network, owing to its larger receptive field, can accurately discern land and sea. This accuracy is further enhanced by using quadtree decomposition to process images with lower resolution and larger coverage. The results suggest that our proposed method effectively eliminates confusing features, with the overall experimental classification accuracy attesting to the effectiveness of our approach, yielding a 6% improvement. Moreover, the screening process in this study significantly reduces the number of input samples for the segmentation network, thus boosting computational speed.

Equity of Highway Noise Exposure

This article discusses the inequities in exposure to highway noise, which can have adverse effects on health, productivity, and quality of life. If certain subpopulations tend to live near highways because of structural inequality, gentrification, lack of social capital, or other reasons, then highway noise can contribute to inequity in a systematic way. The authors developed an online mapping application, the Noise Inequity Identification Tool (NIIT), to visualize the noise damage cost of noise pollution from road segments, the noise-equity ratios at the county level, and the population distribution of various demographics. The methodology combines estimates of roadway traffic noise levels with the cost of noise damage from the hedonic pricing literature and estimates of the number of households affected to estimate the noise damage cost for each road segment in the U.S. The cost of the noise damage accruing to each demographic is then aggregated over a larger region and normalized by their proportion of the population to calculate the “noise-equity ratio,” revealing substantial inequities in highway noise exposure between demographics at a variety of spatial scales. By using this tool, planners and practitioners can easily identify regions with existing highway noise exposure inequities and pinpoint roadways with excessive noise damage costs to recommend potential externality mitigation projects.

Continuous burned area monitoring using bi-temporal spectral index time series analysis

Extreme wildfires are a major agent of disturbance in present-day forest ecosystems and, have serious impacts on society in terms of human casualties and economic losses. Prioritizing and implementing relief and recovery measures after fires require explicit spatial information on the disaster extent. Information for detecting and monitoring ecosystem disturbance due to wildfires is obtainable using time series analysis of remote sensing data. However, the accuracy of this information depends on many factors, such as the input spectral features and the scene characteristics. In this study, we appraise the use of three spectral indices for continuous burned area monitoring in mainland Greece using the Breaks For Additive Seasonal and Trend (BFAST) Monitor unsupervised algorithm. In addition to the commonly employed Normalized Burn Ratio (NBR) we evaluated the use of differenced Normalized Burn Ratio (dNBR) and Relativized Burn Ratio (RBR) generated from a 16-day Landsat-8 time series. The spatiotemporal Overall Accuracy (OA) is assessed using Copernicus Emergency Mapping Service (EMS) grading products and an independent visual interpretation procedure. The latter approach suggests that the using the dNBR (OA = 87.0 %) and RBR (OA = 86.9 %) indices is more efficient than using the NBR time series (OA = 66.4 %). Copernicus EMS validation data show that the RBR-based approach attains a higher OA (80.5 %) -higher than either dNBR (OA = 75.0 %) or NBR (OA = 72.1 %). Evaluating the temporal agreement with Copernicus EMS products, bi-temporal indices achieved mean time lags of 0.023 (dNBR), 0.019 (RBR) and −0.015 (NBR) years. Overall, RBR usage provides the most reliable input for consistent continuous burned area monitoring.

Creación de una red virtual de antídotos entre los servicios de farmacia de los hospitales de Cataluña

ObjetiveTo design a virtual antidote network between hospitals that could help to locate on-line those hospitals that stocked those antidotes with the highest difficulty in terms of availability, and ensured that the medication was loaned in case of necessity. MethodsThe application was developed by four hospital pharmacists and two clinical toxicologists with the support of a Healthcare Informatics Consultant Company. ResultsThe antidotes network in Catalonia, Spain, was launched in July 2015. It can be accessed through the platform: www.xarxaantidots.org. The application has an open area with overall information about the project and the option to ask toxicological questions of non-urgent nature. The private area is divided into four sections: 1) Antidotes: data of interest about the 15 antidotes included in the network and their recommended stock depending on the complexity of the hospital, 2) Antidote stock management: virtual formulary, 3) Loans: location of antidotes through the on-line map application Google Maps, and virtual loan request, and 4) Documentation: As of June, 2016, 40 public and private hospitals have joined the network, from all four provinces of Catalonia, which have accessed the private area 2 102 times, requested two loans of silibinin, one of hydroxocobalamin, three of antiophidic serum and three of botulism antitoxin. Thirteen toxicological consultations have been received. ConclusionsThe implementation of this network improves the communication between centers that manage poisoned patients, adapts and standardizes the stock of antidotes in hospitals, speeds up loans if necessary, and improves the quality of care for poisoned patients.

Geographical challenges and inequity of healthcare access for high-risk paediatric heart disease.

Geographical context is an important consideration for health system design to promote equality in access to care for patients with childhood heart disease (CHD), particularly those living in regional, rural, and remote areas. To help inform future policy and practice recommendations, this study aimed to (i) describe the geographic distribution of high-risk CHD patients accessing an Australian state-wide specialist service and (ii) estimate travel time for accessing healthcare via general practitioners (primary), nearest paediatric centre (secondary) and specialist paediatric cardiac centre (tertiary). Participants included a cohort of children (0-18year) who accessed state-wide specialist CHD services over a 3-year period (2019-2021) in Queensland, Australia. Locations for patient residence, general practitioner, closest paediatric centre and tertiary cardiac centre were mapped using geographical information system (GIS) software (ArcGIS Online). Travel distance and times were estimated using a Google Maps Application Programming Interface (API). 1019 patients (median age 3.8 years) had cardiac intervention and were included in the sample. Of this cohort, 30.2% lived outside the heavily urbanised South East Queensland (SEQ) area where the tertiary centre is located. These patients travel substantially further and longer to access tertiary level care (but not secondary or primary level care) compared to those in SEQ. Median distance for patients residing outside SEQ to access tertiary care was 953km with a travel time of 10h 43min. This compares to 5.5km to the general practitioner and 20.6km to a paediatric service (8.9 and 54min respectively). This geographical mapping of CHD services has demonstrated a key challenge inherent in providing specialist cardiac care to children in a large state-based healthcare system. A significant proportion of high-risk patients live large distances from tertiary level care. The greater accessibility of primary care services highlights the importance of supporting primary care physicians outside metropolitan areas to acquire or build the ability and capacity to care for children with CHD. Strengthening local primary and secondary services not only has the potential to improve the outcomes of high-risk patients, but also to reduce costs and burden associated with potentially avoidable travel from regional, rural, or remote areas to access specialist CHD services.

Down the (digital) rabbit hole: Mapping and decolonizing Safavid women's imagery in digital museums

In this article, I trace Safavid paintings depicting women's imagery online and explore the possibility of digitally mapping Safavid (1501–1736) paintings featuring women on publicly accessible platforms. Along with the practice of online mapping that led me to digital museums, I investigated the descriptions presented on three digitized paintings on different platforms to address the questions, “Where and how can Safavid paintings be digitally encountered” and “In light of the theoretical developments in the scholarship on pre-modern discourses of Safavid gender and sexuality, how do the descriptions of Safavid paintings reflect gender discourses online?” By following Safavid paintings of women online and probing the textual descriptions attached to them, and using netnographic research methods to document my experience and encounter with the digitized Safavid paintings, I explore whether the online descriptions accompanying the images could contribute to the making of decolonial knowledge about non-Western gender discourses.

Wildfire risk weighting and behaviour prediction using open geospatial data and ontologies

This article presents a novel approach to wildfire risk assessment and behaviour prediction by leveraging open geospatial data and ontologies. The proposed methodology includes a spatially weighted index model and multicriteria analysis to represent the risk of forest fires in the affected area. It bridges gaps in theory and practice, offering a comprehensive solution for evaluating potential forest fire risk in near real time, predicting fire behaviour and elucidating the semantics of fire management. During dry and hot conditions, forest fires tend to escalate. Hence, we propose an algorithm that combines experts’ empirical criteria and open-source data to identify dangerous fires in near real time, aiding authorities in directing attention to the riskiest areas. The objective is to predict forest fire behaviour, a complex and nonlinear system influenced by dynamic factors such as weather conditions, topography and land use. Our methodology enables real-time assessment of potential forest fire risks, complemented by predictive fire behaviour scenarios and a descriptive ontology of fire management semantics. We examine existing fire-related ontologies and propose a comprehensive one encompassing incident descriptions, firefighting resources, actor interrelations and knowledge for effective action. By classifying fire sources, our algorithm enables strategic decision-making to prevent uncontrolled fires. This solution significantly enhances data using semantic and spatial relationships among wildfire resources. Furthermore, we demonstrate how ontologies improve data integration and interoperability among diverse systems and organisations involved in forest fire risk management, fostering better coordination and faster responses to critical situations. To facilitate decision-making, we create decision-making scenarios linked to analysed hot spots, drawing from open hot spot data such as National Aeronautics and Space Administration (NASA) Fire Information for Resource Management System (FIRMS), OpenStreetMap (OSM), OpenWeatherMap (OWM) and OpenTopoData (OTD). We propose an ordinal and linguistic classification system (F1–F5) denoting risk levels as low, moderate, high, very high and extreme. These values are obtained through factor aggregation and fuzzy logic. A publicly accessible, interactive web map displays the results derived from this model. Overall, our contributions to wildfire risk management provide authorities with a valuable tool to make informed decisions and mitigate the damaging effects of wildfires.

A national scale web mapping platform for mainstreaming ecosystem services in Greece

This study outlines the conceptualisation and development of a public participation geographic information system (ppGIS) and web GIS (webGIS) platform for hosting, visualizing, and updating ecosystem services (ES) related data at national scale through participatory mapping. The ppGIS/webGIS is developed within the framework of the LIFE-IP 4 Natura, a project has received the most substantial nature-focused funding scheme for Greece approved by the European Commission. One of the project's central goals is the mapping and assessment of ecosystems and their services (MAES) in Greece. This initiative contributes to the implementation of Action 5 of Target 2 of the European Union (EU) Biodiversity Strategy to 2020, following the standard EU MAES guidelines. The LIFE-IP 4 Natura online mapping infrastructure hosts and visualizes spatially explicit maps and information produced earlier in the project at a national level. It also provides a mechanism for updating this baseline information via public participation, thereby fostering increased public awareness of the necessity for ecosystem protection, conservation, and management. Following a multi-stakeholder user requirement analysis, the herein presented ppGIS/webGIS platform is designed and developed. The platform incorporates various geospatial data explorers (GDE), ppGIS services for local and national public participation, geotools and data, visual content representation, and an introductory-level knowledge repository for ES (ES-pedia). An, overview of the platform's preliminary usage is presented, and its potential in promoting the uptake and mainstreaming of the ES concept is discussed.

A Web-GIS tool for diagnosing spatial orientation of young adults: design and evaluation of Geo-Survey

Spatial orientation is the effectiveness with which one is able to assess the mutual location of objects relative to a point of reference or a system of coordinates. Traditionally, this ability has been evaluated through field navigation tests, which do not take into account the prevailing influence of free online maps and virtual walks on a person’s interpretation of space. In this context, this study presents a Web-GIS tool designed and developed to examine spatial orientation skills in the context of the used map type. The tool, named Geo-Survey, enables combination of survey questions with customized maps, providing users with a set of possible answer types. Moreover, using the unique concept of predefined answers, the tool attempts to automate the process of analysing research results. The tools’ performance is evaluated via assessing the spatial orientation skills of a group of young adults.

Application of Gated Recurrent Unit Neural Network for Flood Extraction from Synthetic Aperture Radar Time Series

Floods are a sudden and influential natural disaster, and synthetic aperture radar (SAR) can image the Earth’s surface almost independently of time and weather conditions, making it particularly suitable for extracting flood ranges in time. Platforms such as Google Earth Engine (GEE) can provide a large amount of SAR data and preprocess it, providing powerful assistance for real-time flood monitoring and time series analysis. However, the application of long-term series data combined with recurrent neural networks (RNNs) to monitor floods has been lacking in current research, and the accuracy of flood extraction in open water surfaces remains unsatisfactory. In this study, we proposed a new method of near real-time flood monitoring with a higher accuracy. The method utilizes SAR image time series to establish a gated recurrent unit (GRU) neural network model. This model was used to predict normal flood-free surface conditions. Flood extraction was achieved by comparing and analyzing the actual flood surface conditions with the predicted conditions, using a parameter called Scores. Our method demonstrated significant improvements in accuracy compared to existing algorithms like the OTSU algorithm, Sentinel-1 Dual Polarized Water Index (SDWI) algorithm, and Z-score algorithm. The overall accuracy of our method was 99.20%, which outperformed the Copernicus Emergency Management Service (EMS) map. Importantly, our method exhibited high stability as it allowed for fluctuation within the normal range, enabling the extraction of the complete flood range, especially in open water surfaces. The stability of our method makes it suitable for the flood monitoring of future open-access SAR data, including data from future Sentinel-1 missions.

Pengaruh Penggunaan Media Filtrasi Terhadap Kualitas Air Sumur Gali di Kota Sorong

Sorong City is a city in Southwest Papua Province, with an area of 1,105 km2. The need for clean water is not directly proportional to the mapping of clean water services provided by the government so far it has not reached 30% of the total population in the Sorong City Region. The aims of the study were to analyze the existing water quality in Sorong City, to analyze the effect of the filter media on the planned filtration device on the water quality of the research sample, and to analyze the cost of the planned filtration device. The method used is a qualitative method that is carried out is the monitoring and interpretation of water quality data including physical and chemical quality. Observations of water from dug wells in the City of Sorong have the quality or nature of water that smells, has a salty taste, and is slightly brown in color. Of the 3 samples analyzed according to the existing location, only 2 samples were quality standards, the effect of the filtration media on water quality from 3 samples. Filtration equipment with filtration media 1 has an average per parameter of sample 1 of 25.78%, sample 2 of 36.59%, and sample 3 of 21.86%. The filtration device with 2 filtration media has an average sample 1 of 34.24%, sample 2 of 49.51%, and sample 3 of 25.44%. The filtration device with 3 filtration media has an average sample 1 of 22.32%, sample 2 of 30.6%, and sample 3 of 20.87%. and the cost of making a filtration device with filtration media 1 is IDR 300,000.00, filtration media 2 is IDR 295,000.00, filtration media 3 is IDR 290,000.00. The recommended filtration device is one that uses 2 filtration media.

Enabling simulation services for digital twins of 5G/B5G mobile networks

Digital Twins (DTs) have been proposed as digital replicas of physical entities (e.g., manufacturing equipment), which one can observe in real-time and interact with. Digital Twins of Networks (DTNs) are increasingly being discussed in the literature, as an enabler for efficient data-driven network management and performance-driven network optimization (e.g., to support dynamic reconfiguration, or anticipate the effects of faults). A DTN includes service mapping models, i.e. models that can be fed with acquired data to produce insight on the network itself - e.g., to run what-if scenarios, based on multiple underlying technologies, from Machine Learning to analytical models, e.g. Markov Chains. In this paper we examine the case of DTNs of mobile networks, DTMNs, tailored to 5G and beyond, where issues of dynamic reconfiguration and fault anticipation are critical. We argue that simulation services should be offered by the DTMN in order to allow performance-driven network optimization, and that discrete-event network simulators are ideal instruments to be employed for this purpose. We discuss the challenges that need be addressed to make this happen, e.g., centralized vs. distributed implementation, gathering input from the physical network, security issues and hosting, and we review the possibilities offered by network simulation in terms of what-if analysis, defining the concepts of lockstep and branching analysis. We present a framework to endow a DTMN with simulation services and we exemplify it using Simu5G, a popular 5G/B5G simulation library for OMNeT++, as a reference case study.

The NOAA NCEI marine microplastics database

Microplastics (<5 mm) pollution is a growing problem affecting coastal communities, marine ecosystems, aquatic life, and human health. The widespread occurrence of marine microplastics, and the need to curb its threats, require expansive, and continuous monitoring. While microplastic research has increased in recent years and generated significant volumes of data, there is a lack of a robust, open access, and long-term aggregation of this data. The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) now provides a global open access to marine microplastics data on an easily discoverable and accessible GIS web map and data portal (https://www.ncei.noaa.gov/products/microplastics). The objective of this data portal is to develop a repository where microplastics data are aggregated, archived, and served in a user friendly, consistent, and reliable manner. This work contributes to NCEI’s efforts towards data standardization, integration, harmonization, and interoperability among national and international collaborators for monitoring global marine microplastics. This paper describes the NOAA NCEI global marine microplastics database, its creation, quality control procedures, and future directions.

Mapping cultural ecosystem services: A case study in Lesvos Island, Greece

Coastal and marine ecosystems are under high pressure from multiple activities that take place in coastal areas. Mapping and assessment of ecosystem services are considered essential to support decision-making in policies on the sustainable management of these areas. In this paper, an integrated indicator has been developed and applied to map and assess Recreation Opportunities that are supplied by coastal and marine ecosystems on the Island of Lesvos, North Aegean, Greece, which is an area of great natural wealth and beauty, but it is under numerous threats from various activities that take place within its boundaries. The proposed indicator integrates biophysical, social and managerial aspects of Ecosystem Services (ES) to assess potential ES flow. It is a flexible methodology that utilizes available data and user-friendly geo-technologies and can be used for local scale assessment. A discussion is carried out on the importance of such methodologies for Marine Spatial Planning (MSP), and recommendations are made as to how this indicator may be improved.

Methods and insights on enabling geovisualisation for coastal communities of the North Shore of Vancouver

Contemporary sea level rise (SLR) research seldom considers enabling effective geovisualisation for the communities. This lack of knowledge transfer impedes raising awareness on climate change and its impacts. The goal of this study is to produce an online SLR map accessible to the public that allows them to interact with evolving high-resolution geospatial data and techniques. The study area was the North Shore of Vancouver, British Columbia, Canada. While typically coarser resolution (10 m+/pixel) Digital Elevation Models have been used by previous studies, we explored an open access airborne 1 m LiDAR which has a higher resolution and vertical accuracy and can penetrate tree cover at a higher degree than most satellite imagery. A bathtub method model with hydrologic connectivity was used to delineate the inundation zones for various SLR scenarios which allows for a not overly complex model and process using standard tools such as ArcGIS and QGIS with similar levels of accuracy as more complex models, especially with the high-resolution data. Deep learning and 3D visualizations were used to create past, present, and modelled future Land Use/Land Cover and 3D flyovers. Analysis of the possible impacts of 1, 2, 3, and 4 m SLR over the unique coastline, terrain, and land use was detailed. The generated interactive online map helps local communities visualize and understand the future of their coastlines. We have provided a detailed methodology and the methods and results are easily reproducible for other regions. Such initiatives can help popularize community-focused geovisualisation to raise awareness about SLR.

Bringing spatial interaction measures into multi-criteria assessment of redistricting plans using interactive web mapping

ABSTRACT Redistricting is the process by which electoral district boundaries are drawn so as to capture coherent communities of interest (COIs). While states rely on various proxies for community illustration, such as compactness and municipal split counts, to guide redistricting, recent legal challenges and scholarly works have shown the difficulty of balancing multiple criteria in district plan creation. To address these issues, we propose the use of spatial interaction to directly quantify the degree to which districts capture the underlying COIs. Using large-scale human mobility flow data, we condense spatial interaction community capture for a set of districts into a single number, the interaction ratio (IR), for redistricting plan evaluation. To compare the IR to traditional redistricting criteria (compactness and fairness), we employ a Markov chain-based regionalization algorithm (ReCom) to produce ensembles of valid plans and calculate the degree to which they capture spatial interaction communities. Furthermore, we propose two methods for biasing the ReCom algorithm towards different IR values. We perform a multi-criteria assessment of the space of valid maps, and present the results in an interactive web map. The experiments on Wisconsin congressional districting plans demonstrate the effectiveness of our methods for biasing sampling towards higher or lower IR values. Furthermore, the analysis of the districts produced with these methods suggests that districts with higher IR and compactness values tend to produce district plans that are more proportional with regard to seats allocated to each of the two major parties.