Web Map Research Articles

Health economics and services research in symbiosis: STREAMLINEing mental health services in Austria

Abstract Objectives The cost of mental diseases exceeds 4% of GDP in Europe. In Austria, every 5th person lives with mental ill-health (MIH). Ongoing epidemiological, demographic and societal changes increase the need for easily accessible, better integrated and good value services. In STREAMLINE, we combine, harmonize and implement the latest methods in human service mapping and economic costing to optimize future service provision and financing for MIH in Austria. Methods We comprehensively assess the availability, activity and affordability of human services targeting MIH in Vienna within the health and social care, education, labour and justice sectors based on a conceptual mind map. We identify services through extensive literature, document and web searches plus expert consultations. Services are mapped and costed according to the European PECUNIA methods: classification of the main service types using the international DESDE taxonomy of human services; development of Reference Unit Costs using the PECUNIA RUC templates with primary data; validation of the results with secondary data; compilation of a comprehensive multi-sectoral service catalogue; and synthesis for spatial and policy analyses. Results We identified 756 sources, 75% individual organisations, 5% multi-organisations and 20% existing provider lists. 84% relate to health and social care, 10% labour, and 3% education and justice sectors each. 58% deliver care to adults, 37% to children & adolescents, and 5% support carers. Further service mapping and RUCs will be presented. Discussion STREAMLINE pioneers in coordinated public mental health services and economics research at scale. Assessed levels of fragmentation call for better integration, improved transition, reduced spatial inequity and increased support for carers. Results increase the feasibility of service evaluations from a societal perspective. Planned public service atlas facilitates information and access for service users and carers. Funding: WWTF Key messages • New methods for harmonized health services and economics research allow comprehensive information for optimized future mental health service planning and financing. • Assessed levels of fragmentation highlight the need for better integration, improved transition, reduced spatial inequity and increased support for carers.

Understanding and addressing the needs of refugee and asylum-seeking children and young people

Abstract Issue/problem Europe is home to an increasing number of refugee and asylum-seeking children at risk of health and wellbeing needs due to their life experiences. They are frequently marginalised, impoverished, and face barriers accessing the healthcare and support they need. Bevan, an English social enterprise providing health and wellbeing services to Inclusion Health populations, carried out a needs assessment of migrant children registered at one of its General Practices to determine challenges and opportunities to ensuring their right to health. The knowledge and learning generated is relevant across sectors; organisations and geographical boundaries. Description of the problem The assessment included a literature review, audit of child new patient health checks, service mapping, multi-agency stakeholder interviews and a consultation event with unaccompanied minors. Findings informed Bevan’s Migrant Children and Young People (CYP) Strategy, local service development and system-wide recommendations. Results Key areas of concern identified included mental health; safeguarding; development; nutrition; and inter agency working. Challenges to meeting needs were complex and included national policies, poor provider understanding of rights and entitlements, language and cultural barriers, health service waiting lists, housing and silo-working. Lessons Responding to findings Bevan invested in an in-house CYP mental health service and CYP social prescriber, opened a drop-in clinic and wellbeing service for unaccompanied minors, and initiated orientation and health information sessions for new arrivals with peer advocate support. Bevan now hosts a city-wide cross-sector Migrant CYP stakeholder group and collaborates with regional and national partners to develop resources to promote the health of migrant CYP. Key messages • The needs assessment enabled development of a responsive integrated service for migrant CYP that meets their health needs and addresses their social determinants of health. • The Migrant CYP stakeholder group has improved inter-agency working, helped identify service gaps, increased advocacy opportunities, and facilitated co-creation of solutions to meet population needs.

Documenting the availability and accessibility of reproductive health services for youth in Albania

Abstract Youth in Albania are initiating sexual activity at an earlier age. However, use of all forms of contraception has declined in the last decade and uptake now stands at 4%. The self-reported prevalence of sexually transmitted infections (STIs) and STI symptoms has risen dramatically, as has reliance on abortion as a predominant method of family planning. In 2020-2022, we conducted a multi-method qualitative study in Albania to document the availability and accessibility of sexual and reproductive health (SRH) services and examine challenges that youth face when seeking SRH care. This study comprised a document review, a curriculum review, a community-based survey with 273 youth, semi-structured interviews with 15 youth, semi-structured interviews with 15 key informants, and a service mapping exercise in Tirana. Lack of adequate infrastructure and bureaucratic hurdles make it difficult for youth to navigate and access SRH services in Albania. A third of survey participants (n = 90, 33%) expressed discomfort inquiring about SRH matters, while nearly two-thirds rarely or never sought SRH services from a doctor or clinic. Furthermore, Albania’s school-based sexuality education curriculum lacks in topics, content, and responsiveness, resulting in inconsistent learning experiences and limited knowledge of SRH. Sexuality education is primarily focused on the anatomy and physiology of reproduction, leading to significant knowledge gaps in other aspects of SRH. Only half of survey participants (n = 150, 55%) had received any education on birth control, contraception, or family planning; and only few had knowledge about gender identity (n = 103, 38%), or consent (n = 77; 28%). Widespread lack of awareness and fear of judgment contribute to the underutilization of SRH services among youth in Albania. Improving knowledge and awareness, alongside investing in infrastructure and capacities, are essential to enhancing accessibility and quality of SRH services for Albanian youth. Key messages • Lack of knowledge and awareness prevent youth from accessing SRH services. • Establishing youth-friendly services and training health providers and educators are crucial to delivering confidential and non-judgmental youth-friendly SRH care.

Sentiment Analysis of Google Maps User Reviews on the Play Store Using Support Vector Machine and Latent Dirichlet Allocation Topic Modeling

These days, traveling is made easier by utilizing easily accessible online directions such as Google Maps. Google Maps provides real-time routes by displaying and presenting the closest routes that users can take. However, lately, the routes provided by Google Maps services often get users lost by presenting routes such as forests, narrow roads, and even dead ends. Therefore, this study aims to determine the level of user satisfaction and sentiment into two categories, namely positive and negative, based on reviews on the Google Play Store platform using the Support Vector Machine (SVM) algorithm and topic modeling using Latent Dirichlet Allocation (LDA) to find out the collection of topics that are the main topics of conversation by users regarding Google Maps services. The results of this study show that the SVM algorithm is feasible to use in sentiment analysis classification with an accuracy value of 86%, precision of 93%, recall of 53%, and f1-score of 52%. In addition, topic modeling is applied to generate coherence values for each topic, which shows that the higher the coherence value, the more specific the topic is. The highest coherence value generated in this study was two topic models with a coherence value of 35.15%, but this study took five with a coherence value of 33.39%. The five topic models to be applied in this study are selected because they have a good enough coherence value to identify the main topics and hidden topics in Google Maps user reviews with the Latent Dirichlet Allocation model. The topic model shows five aspects users often discuss: Google Maps route accuracy, system and service errors, navigation application directions, lost time history, and convoluted route provision.

Participatory mapping of ecosystem services of coastal wetlands in Taiwan

AbstractThere is a lack of knowledge and understanding about what ecosystem services are and how to utilize them in a sustainable way. Ecosystem services are thus often underrecognized by the decision makers in charge of creating wetland management plans. Due to this, ecosystem services, which have been used by communities for a long time, are often left out of wetland's wise‐use plans. Inclusion of local communities and promotion of local knowledge are vital aspects of creating sustainable, equitable and resilient wetland management plans that are truly ‘wise’. In this research, we utilize participatory mapping methods to engage communities and create a database of the ecosystem services being used in Chigu Salt Pan Wetland and Chigu Lagoon, located along the coast of Southwestern Taiwan. We look at who are using the ecosystem services, to what degree they are being used and where exactly in the wetland system they are being extracted/utilized. This will pave the way for future researchers to better understand the value of Chigu Salt Pan Wetland and the ecosystem services they provide and promote the use of participatory methods in other locations.

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.

Information Technologies for Calculating Anthropogenic Heat Flux in Urban Areas

Modern megalopolis face challenges in effectively managing energy resources and minimizing the impact of human activities on the environment. One of the significant aspects of anthropogenic impact is the heat flow generated by buildings, transport and industry. This article presents a set of programs for assessing anthropogenic heat flow (AHF) caused by heat loss from buildings during the heating season. The study is based on a spatial geometric model of the city constructed using OpenStreetMap data. The thermophysical properties of enclosing structures and specific characteristics of thermal energy consumption are obtained from building codes. The proposed method includes the stages of buildings modeling, filtering, supplementing information from Yandex Maps and State Information System of Housing and Communal Services, eliminating collisions, assigning specific characteristics and calculating AHF. The method is implemented through scripts for the Rhinoceros platform, known for its wide functionality and visual programming environment Grasshopper. The proposed approach allows us to effectively analyze and visualize anthropogenic heat flow in cities, which is a key step in developing strategies for sustainable energy management and reducing negative environmental impacts.

MapGen-Diff: An End-to-End Remote Sensing Image to Map Generator via Denoising Diffusion Bridge Model

Online maps are of great importance in modern life, especially in commuting, traveling and urban planning. The accessibility of remote sensing (RS) images has contributed to the widespread practice of generating online maps based on RS images. The previous works leverage an idea of domain mapping to achieve end-to-end remote sensing image-to-map translation (RSMT). Although existing methods are effective and efficient for online map generation, generated online maps still suffer from ground features distortion and boundary inaccuracy to a certain extent. Recently, the emergence of diffusion models has signaled a significant advance in high-fidelity image synthesis. Based on rigorous mathematical theories, denoising diffusion models can offer controllable generation in sampling process, which are very suitable for end-to-end RSMT. Therefore, we design a novel end-to-end diffusion model to generate online maps directly from remote sensing images, called MapGen-Diff. We leverage a strategy inspired by Brownian motion to make a trade-off between the diversity and the accuracy of generation process. Meanwhile, an image compression module is proposed to map the raw images into the latent space for capturing more perception features. In order to enhance the geometric accuracy of ground features, a consistency regularization is designed, which allows the model to generate maps with clearer boundaries and colorization. Compared to several state-of-the-art methods, the proposed MapGen-Diff achieves outstanding performance, especially a 5% RMSE and 7% SSIM improvement on Los Angeles and Toronto datasets. The visualization results also demonstrate more accurate local details and higher quality.

Designing and Implementing a Public Urban Transport Scheduling System Based on Artificial Intelligence for Smart Cities

Many countries encourage their populations to use public urban transport to decrease pollution and traffic congestion. However, this can generate overcrowded routes at certain times and low economic efficiency for public urban transport companies when buses carry few passengers. This article proposes a Public Urban Transport Scheduling System (PUTSS) algorithm for allocating a public urban transport fleet based on the number of passengers waiting for a bus and considering the efficiency of public urban transport companies. The PUTSS algorithm integrates artificial intelligence (AI) methods to identify the number of people waiting at each station through real-time image acquisition. The technique presented is Azure Computer Vision. In a case study, the accuracy of correctly identifying the number of persons in an image was computed using the Microsoft Azure Computer Vision service. The proposed PUTSS algorithm also uses Google Maps Service for congestion-level identification. Employing these modern tools in the algorithm makes improving public urban transport services possible. The algorithm is integrated into a software application developed in C#, simulating a real-world scenario involving two public urban transport vehicles. The global accuracy rate of 89.81% demonstrates the practical applicability of the software product.

A geographic-semantic context-aware urban commuting flow prediction model using graph neural network

Urban commuting flow prediction is crucial for urban planning, transportation optimization, and supply chain management. Traditional models and machine learning solutions often fall short in capturing the complex dynamics of urban mobility, overlooking factors like spatial correlation and human mobility patterns. Recent deep learning advancements have shown promise by integrating spatial correlation through incorporating geographical adjacency. However, this approach may not fully capture spatial correlation, particularly the semantic adjacency effect of zones connected via public transportation lines, such as metro networks—a novel dimension overlooked in prior research. The metro station serves as a major transit hub, attracting passengers from various origins and destinations and correlating the flow between the connected zones and other zones within the metro network. Devising an optimized model to encapsulate these spatial correlations is another crucial area of research where scientists are actively seeking the most effective solutions. We propose a novel sequential hybrid approach leveraging graph-based learning, focusing on the underexplored impact of metro networks on commuting flow prediction. Our model integrates two types of adjacency—geographic and semantic—using Graph Convolutional Networks (GCNs) to embed geographic correlations among proximate zones and Graph Attention Networks (GATs) to incorporate semantic adjacency defined by metro line connectivity. Furthermore, our approach innovatively incorporates features directly from online maps, eliminating reliance on third-party resources and enhancing scalability and efficiency. We validate our model using real-world datasets from Montreal, Canada, comprising travel surveys and GPS trajectory types, totaling nearly 900,000 trip records. Through evaluation using established performance metrics such as MAE, RMSE, MAPE, and CPC, we demonstrate significant improvement in predictive accuracy compared to existing state-of-the-art models. Our research has practical implications for urban planning and infrastructure development, aiding policymakers and urban planners in analyzing the impact of new urban infrastructure on commuting flow dynamics and facilitating informed decisions regarding transportation and infrastructure planning.

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.

Suicidal behaviors among refugee women in Jordan: post-traumatic stress disorder, social support and post-displacement stressors

BackgroundThis paper examines the frequency of suicidal behaviors (suicidal ideation or attempt) among a sample of Syrian refugee women living in non-camp settings in Jordan. We asked several questions surrounding suicide and examined the associations between post-traumatic stress disorder (PTSD), social connectedness, post-displacement stressors and suicidal behaviors.MethodsParticipants (n = 507) were recruited using a clinic-based systematic sampling from four health clinics throughout Jordan in 2018. We used a multivariable logistic regression to examine the hypothesis of whether positive screening for PTSD (PCL-5), social isolation (have no friends or family members available to help), and greater number of post-displacement stressors (PMLD Checklist) is associated with suicidal behaviors.ResultsApproximately one-tenth (9.86%) of participants surveyed reported suicidal behaviors (suicidal ideation or attempt) in the past six months. Our hypothesis was partially supported. In the adjusted multivariable analyses, screening positive for PTSD [OR:4.02 (95% CI:1.33, 12.15)] increased odds of suicidal behaviors, while having one friend or family member available to help when in need [OR:0.31 (95% CI:0.13, 0.78)] decreased odds of suicidal behaviors. We did not find any associations between the number of post-displacement stressors and suicidal behaviors in the multivariable model.ConclusionAgencies and practitioners addressing suicidal behaviors among Syrian refugee women should provide interventions that aim to reduce PTSD symptoms and social isolation. Potential intervention includes screening for mental health symptoms and suicidal behaviors during routine visits with service providers, as well as providing proper mental health and psychosocial support services according to the mapping of available services.

How to visualize the spatial uncertainty of landmark representations in maps?

Landmark representations in maps play an important role in spatial tasks such as self-localization, spatial orientation, and navigation. By matching landmarks to their map representations, people can identify their current location and orientation within a map. However, spatial inaccuracies of landmark representations like these of pictograms in maps have been found to affect the ability to successfully match spatial environments to their map representations. These map matching difficulties could negatively affect the performance in spatial tasks. Due to the increasing number of data sources used in modern map services, for example provided by volunteers, map data quality has become more heterogeneous and uncertain. Therefore, solutions for counteracting the negative effects of spatially inaccurate landmark representations on map matching are required. Here, we report two studies that investigate whether visualizing spatial landmark uncertainty can improve map matching. Although suggestions for visualizing uncertainty exist, there is no empirical evidence whether they can intuitively communicate spatial uncertainty of landmark representations. If map users are made aware of potential spatial inaccuracies in a map, they may observe small or moderate spatial inaccuracies of landmark representations but still be able to match these to the represented landmarks. Our findings support this assumption. Using pictogram size, transparency, or circular uncertainty areas around pictograms to communicate spatial uncertainty increased the likelihood of a perceived match between 3D environments or 360° images and a corresponding map representation when landmark representations were spatially inaccurate. Furthermore, our findings indicate that, in addition to landmarks, people use spatial reference points such as road segments for map matching. Thus, if sufficient other spatial reference points are available both in the environment and its map representation, negative effects of spatially inaccurate landmark representations on map matching may be less severe.

Empowering geoportals HCI with task-oriented chatbots through NLP and deep transfer learning

ABSTRACT In the past ten years, chatbot development has matured to become one of the most well-distinguished outcomes of artificial intelligence. Despite some criticism, Bing AI, ChatGPT and other natural language processing (NLP) products of similar nature are becoming more popular. The creation of chatbots can close several gaps in geographic information retrieval as well. This research introduces and successfully implements, for the first time, a model for integrating task-oriented chatbots into geoportals, with the goal of easing user requests, improving access to geospatial services, and fostering human-computer interactions (HCI). Additionally, it presents a novel recommendation solution for matching the most appropriate volunteer to the user’s geospatial needs based on expertise similarity, semantic similarity, and community feedback. The three categories of finding map services, discovering geoprocessing services, and volunteer expert recommendations were shown to be the most significant geoportal bot intents. Depending on the requirement, each intent additionally includes various entities such as time, place, description, skill, etc. The notion of deep transfer learning (DTL) was then put into practice by customizing a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model for our particular aim and creating a task-oriented conversational agent. According to the results, effective intent classification and entity recognition in the geospatial domain could arise from this approach. We performed the training process with 200 sample data, 20% of which were utilized in a stratified manner for testing, and we obtained f1-scores of at least 0.75. Finally, a pilot Geoportal Chabot that combines crowdsourcing and conversational agents’ approaches was put into use and tested with success. In keeping with SDI technical purposes, the system might direct users to common geospatial web services, namely WMS and WPS, in addition to including natural language understanding (NLU) and natural language generation (NLG) capabilities. Result of user-centered evaluation indicated that the integration of a chatbot significantly reduces the average time required to access geospatial data and processing services by more than 50%. Notably, this effect is even more pronounced when locating an expert, with a fivefold decrease in the time required. Finally, overall user satisfaction rose from 86% to 94%.

Mapping and valuation of ecosystem services of mangroves: a detailed study from the Andaman island

Mapping and valuation of ecosystem services of mangroves: a detailed study from the Andaman island

Fast Algorithms for Computing Fixed-Length Round Trips in Real-World Street Networks

This paper proposes and evaluates algorithms for calculating round trips of a prescribed length on directed street networks. This problem has several real-world applications, such as designing jogging routes and cycling tours. In this work, we focus specifically on methods that avoid the need to download, process, and store large map databases. At the same time, we aim for our methods to be fast, accurate, and capable of handling a wide range of prescribed distances, from just a few meters to many kilometres. To achieve this, our overall strategy involves using a small number of calls to a suitable online mapping service to collect relevant structural information for the problem at hand. All remaining computations are then performed locally on the client. Empirically, we demonstrate that our suggested techniques outperform existing open-source algorithms in terms of both accuracy and runtime requirements. Our most successful approach is based on multi-objective local search, utilizing specialized neighbourhood operators that exploit the underlying graph-theoretical properties of this problem, resulting in runtimes of around 2–3 s on a typical desktop computer.

Self-Attention Multiresolution Analysis-Based Informal Settlement Identification Using Remote Sensing Data

The global dilemma of informal settlements persists alongside the fast process of urbanization. Various methods for analyzing remotely sensed images to identify informal settlements using semantic segmentation have been extensively researched, resulting in the development of numerous supervised and unsupervised algorithms. Texture-based analysis is a topic extensively studied in the literature. However, it is important to note that approaches that do not utilize a multiresolution strategy are unable to take advantage of the fact that texture exists at different spatial scales. The capacity to do online mapping and precise segmentation on a vast scale while considering the diverse characteristics present in remotely sensed images carries significant consequences. This research presents a novel approach for identifying informal settlements using multiresolution analysis and self-attention techniques. The technique shows potential for being resilient in the presence of inherent variability in remotely sensed images due to its capacity to extract characteristics at many scales and prioritize areas that contain significant information. Segmented pictures underwent an accuracy assessment, where a comparison analysis was conducted based on metrics such as mean intersection over union, precision, recall, F-score, and overall accuracy. The proposed method’s robustness is demonstrated by comparing it to various state-of-the-art techniques. This comparison is conducted using remotely sensed images that have different spatial resolutions and informal settlement characteristics. The proposed method achieves a higher accuracy of approximately 95%, even when dealing with significantly different image characteristics.

Circular Economy: Research in the Maritime Domain

The maritime domain is a very broad domain encompassing ship design, construction, transportation, export–import, port management, energy, ocean, and waterways management. The assumption that the marine industry is less flexible for a transition from linear economy to circular economy (CE) is being challenged. A ship is a factory/city in motion, and therefore, CE principles may be applied to a ship. CE principles can be applied to ports, energy, and waterways management. Successful implementation of CE requires a comprehensive mapping of business models, projects, products, and services; identification of all stakeholders; and using biomimicry in manufacturing. CE can help in the regeneration and restoration of natural capital. The circular business models can help in increasing revenue, reducing costs, and saving the environment. CE can be extended to include reimagining, realigning, reworking, and reaching out to different players in the maritime domain. The marine industry faces the challenges of decarbonization and end-of-life vessel management.

Implementing geographic information systems in academic paper search: A framework and case study: GISAPS

The Geographic Information Systems Academic Publication Search (GISAPS) project pioneers the integration of spatial data into academic research through a novel online platform. Utilizing Web Map Service (WMS) and advanced search functionalities, GISAPS enables efficient access and analysis of georeferenced academic publications. This paper outlines the development and capabilities of GISAPS, including experimental WMS layer integration for enhanced data visualization. Key contributions include facilitating interdisciplinary research, improving spatial data accessibility, and promoting open science through its open-source framework. GISAPS's implications for academia and the research community are significant, offering new avenues for data-driven research and education. Future enhancements aim to incorporate real-time data feeds, advanced spatial analysis tools, and mobile accessibility, ensuring GISAPS remains at the forefront of GIS technology in academic settings.