Web Map Research Articles

Mapping of Food Supply Ecosystem Services in Malang Regency

The increase in population is in line with the increase in resource use, including the need for land as a space for human activities to meet their needs. Land conversion, primarily agricultural land, into non-agricultural land can reduce the ability of land ecosystems to provide food. This research aims to assess the performance of ecosystem services for providing rice food in Malang Regency. The study used the Simple Additive Weighting (SAW) method, a simple weighting and scoring method of land use parameters, landform ecoregions, natural vegetation types, soil types, and rainfall to determine ecosystem services for food provision. The results showed that the Malang Regency area consists of 5 classes of Ecosystem Service Performance Index (ESPI) for rice food providers, including Very High, High, Medium, Low, and Very Low. The Medium class is the ESPI class with the highest area of 117,452.85 hectares or 34% of the total area. Followed by Low ESPI class 99,980.31 hectares (29%), High ESPI class 90,742.14 hectares (26%), Very Low ESPI class 19,191.28 hectares (6%), and Very High ESPI class 17,442.59 (5%).

End-to-End Autonomous Driving Through V2X Cooperation

Cooperatively utilizing both ego-vehicle and infrastructure sensor data via V2X communication has emerged as a promising approach for advanced autonomous driving. However, current research mainly focuses on improving individual modules, rather than taking end-to-end learning to optimize final planning performance, resulting in underutilized data potential. In this paper, we introduce UniV2X, a pioneering cooperative autonomous driving framework that seamlessly integrates all key driving modules across diverse views into a unified network. We propose a sparse-dense hybrid data transmission and fusion mechanism for effective vehicle-infrastructure cooperation, offering three advantages: 1) Effective for simultaneously enhancing agent perception, online mapping, and occupancy prediction, ultimately improving planning performance. 2) Transmission-friendly for practical and limited communication conditions. 3) Reliable data fusion with interpretability of this hybrid data. We implement UniV2X, as well as reproducing several benchmark methods, on the challenging DAIR-V2X, the real-world cooperative driving dataset. Experimental results demonstrate the effectiveness of UniV2X in significantly enhancing planning performance, as well as all intermediate output performance.

Large-Scale School Mapping Using Weakly Supervised Deep Learning for Universal School Connectivity

Improving global school connectivity is critical for ensuring inclusive and equitable quality education. To reliably estimate the cost of connecting schools, governments and connectivity providers require complete and accurate school location data – a resource that is often scarce in many low- and middle-income countries. To address this challenge, we propose a cost-effective, scalable approach to locating schools in high-resolution satellite images using weakly supervised deep learning techniques. Our best models, which combine vision transformers and convolutional neural networks, achieve AUPRC values above 0.96 across 10 pilot African countries. Leveraging explainable AI techniques, our approach can approximate the precise geographical coordinates of the school locations using only low-cost, classification-level annotations. To demonstrate the scalability of our method, we generate nationwide maps of school location predictions in African countries and present a detailed analysis of our results, using Senegal as our case study. Finally, we demonstrate the immediate usability of our work by introducing an interactive web mapping tool to streamline human-in-the-loop model validation efforts by government partners. This work successfully showcases the real-world utility of deep learning and satellite images for planning regional infrastructure and accelerating universal school connectivity.

The core readiness of Sleman Region community on mobile health implementation: a qualitative study

Introduction: The innovation of healthcare technology has developed rapidly, including mobile health (mHealth). The success of mHealth implementation in community settings obtain influenced by core readiness factors, distinctly community needs towards mHealth. This study aims to explore the core readiness of the Sleman Region community on the use of mHealth, specifically the features needed and the determining aspects of acceptance.Methods: This study used a qualitative design and conducted in-depth interviews with 13 community members. Participants were chosen purposively by Public Health Centre with various characteristics from January-March 2020. The verbatim transcripts were analysed using the direct content analysis technique with peer debriefing.Results: There are five themes of core readiness that are identification of needs obtained feature needed, satisfaction and dissatisfaction when using existing applications, knowledge of mHealth potency including advantages and disadvantages, acceptance of aspects that determine the use of mHealth, and enthusiasm for future mHealth that including reasons whether using the application or not. Specifically, the following features needed in the community are health information, self-medication, online consultation, mapping of healthcare services, peer support group, health promotion, personal training, emergency, personal health record, and feature for specific groups. The eight determining aspects of mHealth influencing community acceptance are interface design, easiness, content delivery, cost, promotion, performance, safety, privacy, and sustainability.Conclusions: The findings indicate that the Sleman Region community stands ready to accept the implementation of mHealth based on core readiness exploration. These findings can become a consideration in the development of future mHealth applications.

The Role of Digital Applications in the Real Estate Industry

This study examines the effective use of digital applications in the real estate sector. In the contemporary era, the advent of technological advancements has profoundly impacted real estate transactions, facilitating more expeditious, transparent and efficient purchase-sale and management processes through digital platforms. Digital tools facilitate access to critical data, including geographical location, zoning status, geometric structure, area size, title deed information and slope of real estate, obviating the necessity for field examination. This offers a significant advantage to both buyers and investors, enabling them to make more informed decisions. Furthermore, real estate consultants and industry professionals are enabled to offer more comprehensive services to their customers by utilising these technologies. The advent of digital platforms has facilitated access to preliminary real estate information, obviating the need for physical visits to municipal or cadastre directorates. This enhancement in convenience, particularly with regard to reduced time and cost, is a significant benefit. The integration of online maps, geographic information systems, and digital databases has been instrumental in enhancing the decision-making processes by furnishing users with detailed information regarding the current status of real estate. Consequently, investors and buyers are able to access the necessary data promptly, eliminating the need for extensive research into specific real estate. The instantaneous evaluation of real estate status within purchase-sale processes enhances the efficiency and reliability of these transactions. The present study examined the findings of a pilot field study conducted with the help of digital applications and investigated the effects of these applications on the sector. The results obtained demonstrate that digital tools have the capacity to save time, increase accuracy and strengthen reliability by accelerating decision-making processes in the real estate sector. In this context, the role of digitalisation in the sector is becoming increasingly important.

Learnings From the Front Line: Early Implementation Experiences of the Central Queensland Foot Hub Virtual Multidisciplinary Team Model-of-Care.

The 'Central Foot Hub' was operationalised in mid-February 2024 to provide multidisciplinary team (MDT) support from the Royal Brisbane and Women's Hospital (RBWH) to three hospital and health services (HHS) across Central Queensland. This model aimed to enhance diabetes-related foot disease (DFD) care by integrating virtual and local MDTs. This study presents early implementation learnings from frontline clinicians involved in the co-design, implementation and refinement of this innovative model-of-care (MOC). A preparatory phase involved stakeholder engagement, service mapping and funding allocation to RBWH for recruitment. Site preparedness varied, influencing referral rates. A mixed-methods approach was used to evaluate challenges and successes, with input from local end-users. Key enablers included local champions, collective leadership and integration with existing providers. At Rockhampton Base Hospital, the MDT expanded subsequently improving care coordination. However, challenges such as funding constraints, hybrid health information systems, and technological barriers affected implementation. Discipline-specific insights highlighted the need for structured communication, local workforce expansion and ongoing refinement of MOC. The Central Foot Hub enhanced access to specialist care and optimised local service delivery for people living with DFD in Central Queensland. Sustainable scale-up will require additional funding, streamlined health information systems and continued collaboration between healthcare providers across HHS.

A FAIR and open geographic data collection for the Massaciuccoli Lake basin wetland in Italy.

The creation of a catalogue of geodata harmonised over time and space is essential for describing the status of ecosystem services in wetlands. In the present work, a specific methodology has been developed for the collection and generation of spatially and temporally harmonized geographic data to describe essential ecological and socio-economic charactesristics of the Massaciuccoli Lake basin (Tuscany, Italy), while providing a re-usable methodology for other areas. We developed a methodology, which we called 'Geodata Layers Harmonization Methodology' (GLHM), divided into four main phases: Geodata Census (GC), Geodata Selection (GS), Geodata Alignment (GA), and Geodata Publication (GP). The first phase, GC, involved a census of geodata made available online by public institutions, prioritizing those most relevant for describing ecosystem services, such as climatic, agro-environmental, pedo-geological, and biodiversity variables, with a preference for detailed data at the local level. The metadata of the collected geodata were organized into a structured tabular format. In the GS phase, geodata were selected based on a spatial resolution compatible with regional-scale ecological models (maximum 0.0005° ≈ 50 m), and a temporal coverage that could represent from remote past to far future scenarios. Geodata with partial spatial coverage or unsuitable for ecological models were excluded. Additionally, we evaluated the compliance of the geodata published on the websites of public institutions with the Findable-Accessible-Interoperable-Reusable (FAIR) principles through a newly developed scoring system. Based on this score, we selected only the data that exceeded a minimum FAIRness threshold. In the GA phase, the selected geodata were aligned semantically (i.e., by variable meaning), temporally, and spatially. Each geodata was georeferenced using the WGS84/EPSG:4326 reference system and clipped to the boundaries of the Massaciuccoli Lake basin. Raster data were resampled to achieve a uniform spatial resolution of 0.0005°. In the last phase, GP, the aligned geodata were published on public access repositories and services: The entire collection was organized as a QGIS project with legends and a metadata table associated. An Atlas was also produced, in PDF format, which visually represented the data and metadata. The geodata and their corresponding legends were exposed through Web Map Service (WMS) and Web feature Service (WFS) standards on a GeoServer instance and catalogued in a GeoNetwork instance, compliant with the ISO19139 standard and the INSPIRE European Directive. The collection contains 148 geo-datasets, representing 75 climatic, agro-environmental, pedo-geological, morphological, ecological, biological, and socio-economic information distributed across five temporal reference time frames: a remote past (1950-1980), a near past (1981-2015), the present (2016-2024), a near future (2025-2050), and a far future (2051-2100). Future projections are available under the Representative Concentration Pathways (RPC) 2.6, 4.5, and 8.5 to simulate low, medium, and high greenhouse gas concentration scenarios respectively. The present geodata collection is particularly useful for wetland monitoring, management and planning. It can easily be integrated with ecological models and predictive studies to analyse the effects of climate change and anthropogenic pressures on wetlands. The GLHM methodology is applicable to other ecological contexts to create standardised structured frameworks for evaluating the status of the biodiversity and the ecosystem services and the interplay between anthropic pressures and the ecosystem response.

A Novel Approach for Developing Web GIS-Based Navigation System

The campus area is gradually expanding with the increasing number of university students. Consequently, it is difficult for freshmen or visiting staff to find a specific building at the campus. The study aimed to Develop a Campus Web GIS Navigation System for the study area using Geospatial technology. The general procedures to create a web map navigation application are Geo-referencing Images, digitization, Road Network Analysis, Overlay Analysis, and finally, a Web GIS Based Campus Navigation System has been developed. The main activity of the system application is the map activity. It displays the campus map of the Wa chemo University. The user can zoom and scroll this map to get to his/her desired places. The building map is the basic layer of this screen. The end user can access it by using this link: https://wcu- webmap.github.io/Web-Map-of-WCU/

Wrong place at the wrong time: patterns explaining ungulate-vehicle collisions in Portugal

Abstract Wild ungulate-vehicle collisions (UVC) in Europe have dramatically increased, creating serious conservation, economic and health problems. To examine which temporal and environmental features promote UVC involving the three most abundant and widely distributed wild ungulate species in Portugal (wild boar, red deer and roe deer), we used Generalized Linear Models, using a database of 3,374 UVC (2019–2022). We evaluated the influence of hour, day, month, hunting season, breeding season, lunar phase, meteorological conditions and land cover on the patterns of the wild ungulate-vehicle collisions. Temporally and spatially, UVC were not randomly distributed and the distribution was species-specific. Different factors explained the vehicle collisions for different species, being precipitation, time after dusk, forest and urban area around the collisions the drivers common for all the species. Hunting season also explained the wild boar collisions and both hunting and breeding season explained both wild boar and roe deer vehicle-collisions. Our results may be considered an important step towards the development of nationwide and space-time collision risk maps to be incorporated on navigation and mapping services. The information provided in this study emphasis the need for the optimization of ongoing mitigation measures and the need to focus them on periods of elevated collision risk, namely during twilight periods. Graphical Abstract

Robustness evaluation of bus-subway composite network considering accessibility

The purpose of evaluating the robustness of the bus-subway composite network is to assess its ability to maintain normal operation under interference or failure. Most existing research focuses on the network’s structure, often overlooking critical factors such as spatial information and site accessibility. To address this, ArcGIS and network mapping services are employed to enhance the network coupling method and construct a composite network model. Three types of site importance indices are proposed, direct reachability, inter-site reachability, and off-site accessibility. After simulating various deliberate and random attacks, we analyze changes in robustness indicators, including the maximum connectivity subgraph rate, network efficiency, and network reachability. The CRITIC weighting method is employed to assess the impact of various attack modes on network robustness. The study, focusing on the bus-subway composite network in the core areas of Dalian City, shows that: sites with high off-site accessibility, have the most significant impact on overall network performance. Network reachability reflects the accessibility level of the public transportation system. Network efficiency is most influenced by critical sites. It holds significant application value for the optimization and emergency management of the bus-subway composite network.

Cartographic Considerations for Ethical Rockhounding

When does location information on an interactive web map become too detailed and identifiable? In this case study, I discuss the issues that arose when the Washington Geological Survey converted a decades-old printed rockhounding location map into multiple interactive, variable-scale web maps. These issues include topics relating to privacy, land management, rock-collecting laws, and the ethical responsibility of a state agency to consider the many different ways that such maps could be used.

Mapping COVID-19: Applying Ethical Strategies in Web Mapping Decisions

In December 2019, the world was introduced to a coronavirus, the likes of which have never been seen before, resulting in the COVID-19 pandemic. During the initial months of the pandemic, academics, government agencies, and concerned citizens, among others, raced to generate maps to help explain the geographic spread of the virus over space and time. Maps, however, can be intentionally or unintentionally misleading; we saw this play out during the pandemic as we rushed to make COVID-19 dashboards and online maps. While the Johns Hopkins University’s COVID-19 dashboard served as the first authoritative resource on the pandemic, it lacked the spatial granularity required for smaller states, such as Connecticut. Realizing this limitation, a team of geography Ph.D. students and faculty came together to develop a dashboard better suited to serve the residents of the state and aid policymakers during a time when each decision could have vast consequences. This case study reflects on our team’s strategies to address the ethical considerations to deliver high-quality maps and to promote healthy skepticism among users of the COVID-19 dashboard. We first provide a historical background of the discussion around ethics in cartography that we used to frame our arguments and strategies. Second, we summarize our deliverables, starting with an online interactive dashboard for 169 towns suitable for both mobile and desktop viewing. For the visualizations, we tried to balance the right amount of health information so that they were cartographically sound, easy to understand, and not misleading. As geographers, this is one of our responsibilities to our communities.

Putting people at the center: methods for patient journey mapping of viral hepatitis services across two LMICs in the Asia Pacific

BackgroundTo ensure that health services are high-quality, trusted and used by the population, their design and improvement should start from the perspective of what matters to people. Patient journey mapping (PJM) is one research method that centers the experiences of individuals living with health conditions and follows their pathways through care and recovery. This paper describes a novel, qualitative PJM methodology used in Vietnam and the Philippines to inform the co-design of a people-centered viral hepatitis screening, care and treatment pathway for individuals living with chronic hepatitis, which is a significant public health concern in the Asia-Pacific region.MethodsData collection involved in-depth interviews with a purposive sample of 63 people living with hepatitis (demand-side) and focus group discussions with healthcare providers working in the same geographical areas (supply-side). Rapid deductive qualitative analysis was used to identify typical journeys, and related barriers and enablers. The methodology was implemented over 8 weeks, adapting the Consolidated Criteria for Reporting Qualitative Research (COREQ).ResultsThis paper demonstrates how a PJM methodology that incorporates patient and HCP perspectives can be feasibly implemented in two LMIC contexts, while fulfilling many of the criteria identified by the COREQ guidelines. Sharing such methods and associated instruments may help to enable broader uptake and application in other LMIC settings, providing health systems practitioners with a critical tool to identify and overcome barriers in and promote the delivery of people-centered health services globally.ConclusionDespite limited uptake, especially in resource-limited contexts and at the primary healthcare level, PJM is a novel research method with the potential to make promising contributions to people-centered health service design.

The Role of AI in Optimizing Cloud-Native API Architectures

This article examines the transformative role of artificial intelligence in optimizing cloud-native API architectures across multiple dimensions. As organizations increasingly adopt distributed microservices, APIs have become fundamental components requiring sophisticated management approaches to address growing complexity. Integrating AI and machine learning technologies offers powerful solutions for enhancing API performance, security, reliability, and maintenance. Through real-world implementations and industry data, the article demonstrates how AI-driven techniques enable intelligent traffic analysis, adaptive caching strategies, anomaly detection, predictive maintenance, and self-healing capabilities. These advancements allow organizations to proactively identify potential issues, automatically remediate failures, and optimize their API ecosystems. The comprehensive exploration covers how machine learning models can analyze complex patterns, predict resource requirements, map service dependencies, and guide architectural evolution, ultimately transforming how enterprises manage their cloud-native infrastructures while delivering significant operational benefits

Mapping service quality in higher education: insights from bibliometric study

PurposeThis study aims to examine the publishing trends on service quality in higher education (SQHE) from 1993 to 2023. In addition, this bibliometric study looks at highly productive counties, authors, journals and affiliations in SQHE research. Furthermore, the study analyzes the relationships between the research constituents using science mapping techniques.Design/methodology/approachData has been extracted from the Scopus database using a bibliometric approach. To gather pertinent information, a comprehensive search strategy was designed. Data analysis has been performed using Word, Excel, R (Bibliometric R package) and VOSviewer software.FindingsA total of 552 studies were published in the Scopus database from 1993 to 2023. Results of the study reveal that there is a rising trend in the number of publications and the maximum number of articles published in the year 2021 (n = 59). Malaysia was found to be the most productive country. Joseph, Sultan and Yeo were found to be the most prolific authors. Quality Assurance in Education was the leading journal, and Universiti Teknologi Mara was the most productive affiliation in SQHE research.Research limitations/implicationsThis study relies on a single database for literature retrieval.Practical implicationsThe qualitative analysis carried out in this study will assist education administrators in making informed decisions.Originality/valueTo the best of current knowledge, this seems to be the first in-depth bibliometric study that represents a comprehensive view of SQHE research.

Do Blue Light Filters Reduce Visual Fatigue When Using Digital Maps? An Eye Tracking Experiment to Promote Vision Health

ABSTRACTIn this paper, we present an eye tracking experiment to evaluate how blue light filtering of digital maps reduces visual fatigue and thus promotes vision care in public health. Prolonged exposure to light from digital screen reading results in visual fatigue, and visible light in the blue range exacerbates visual fatigue because it requires the highest energy output to display onscreen. System‐level blue light filtering increasingly is used as a “nighttime” mode for short screen sessions at night and is purported to combat visual fatigue for longer screen sessions throughout the day. Blue light filtering is particularly relevant to cartography, as blue is among the most common colors used on maps for both denotative and aesthetic purposes. To this end, we conducted a one‐factor eye tracking experiment with 78 participants to evaluate the effects of three blue light filtering conditions. The experiment used the recently released “teal‐based” style of Google Maps as the visual input for blue light filtering, a digital web map that is publicly available and has a broad user base. We collected eye tracking measures correlated with visual fatigue related to map reading such as blinking frequency and pupil diameter, with a mixture of Likert‐based self‐reported measures. We demonstrate empirically that low levels of blue light filtering can alleviate visual fatigue without compromising the denotive and aesthetic quality of digital maps, although high levels of blue light filtering impedes performance and aesthetics. Our results indicate that low blue light filtering delays visual fatigue by 3–4 min and high blue light filtering by 6–7 min. Our findings also provide preliminary evidence that reading digital maps for a short period of time does not lead to visual fatigue; however, visual fatigue may appear after reading digital maps for 18 min, offering new evidence for the design of digital maps that consider eye care and visual health.

Operationalising participatory action research to evaluate early years’ population health services

BackgroundEarly years interventions are critical to children’s health and development and are emerging as core to public health programmes in the UK and globally. Evaluating such interventions is complex. The study reported in this article evaluates a place-based public health initiative ‘A Better Start Southend’ (ABSS) aimed at facilitating early years’ development specifically. It centres on examining the access barriers and facilitators experienced by parents and young children, as perceived by health professionals providing these services.MethodsThis paper illustrates the utilisation of participatory action research (PAR) approach, employing creative methods, including spider grams, service mapping and photovoice, with health professionals delivering ABSS services. PAR methods enabled exploration of community resources that facilitate or impede early childhood development in the local context.ResultsOperationalising PAR yielded critical providers’ perspectives on key challenges of delivering these programmes, and the factors that in their view impeded their uptake by families and hence effectiveness. The approach provided space for authentic knowledge production through critical reflexive enquiry, exchange, collaborative dialogue and transformation. Through the process, participants revealed the social and commercial determinants of childhood development and how these determined the reach and success of ABSS. Health professionals especially highlighted poor-quality housing, poor public transport, the cost-of-living crisis and harmful commercial marketing practices as key barriers to promoting good early childhood development. System-wide barriers were also reported and included poor resourcing of health and social care services, lack of culturally and linguistically accessible provisions, and exclusionary practices creating inequitable access to health for many families and children.ConclusionPAR is a potentially valuable tool for healthcare evaluations with the ability to generate nuanced reflexive perspectives and considerations that go beyond identifying the outcomes and gaps in interventions. It draws participants into a reflexive process to define pathways for change. Health professionals identified social inequities as the most significant barrier to promoting early childhood development. These inequities were not addressed in the design and implementation of the early year programme under study. The study supports the need for a multi-level, multi-systems and intersectional framework for place-based public health programmes to have the desired impact and reduce inequalities in access to early years interventions.

Understanding the Carbon Footprint of Tile Transfer for Web Maps

As web maps are now extensively used by billions of users, the energy consumption of these maps is not marginal anymore. Green cartography seeks to reduce the energy consumption of maps to promote more sustainable digital tools. To reduce energy consumption, we first need to better understand the different sources of energy consumption for web maps. Among these sources, this paper focuses on the tiles that are stored on servers and then constantly transferred each time a user explores the map. This paper presents several experiments carried out with current web maps to assess this energy consumption. We first try to assess the number of map tiles that are loaded through the web when users explore web maps, and we determine which types of interaction are used with the maps, and a similar amount of tiles is loaded. Then, we try to assess which zoom levels are the most loaded by users; it appears that the medium–large scales are the most used (between zoom levels 11 and 17). Then, we explore the size of the map tiles and try to assess which ones are larger and thus require more energy to load over the web; we can find clear differences between zoom levels. Finally, we discuss how map generalization could be used to reduce energy consumption by creating lighter tiles. These experiments show that the current web maps are suboptimal regarding energy consumption, with many tiles loaded at zoom levels where the tiles are larger than necessary.

Retrofit-Oriented Large Parks’ Walking-Shed Evaluation—A Case Study of Rizhao, China

Large parks play a key role in the identity of urban public spaces and as destinations for residents’ urban walks, with the social benefits they provide being irreplaceable by other types of green spaces. This study examines the accessibility of large urban parks in Rizhao, China, focusing on spatial distribution, service equity, and optimization strategies. Using GIS-based walking route proximity analysis, the study identifies significant accessibility gaps in high-density urban areas. Rizhao is a typical coastal tourist city, selected as the study area due to its low level of urbanization and the underutilization of its natural resources. This study uses online map data to evaluate the service efficiency and supply–demand heterogeneity of large parks from multiple perspectives, proposing targeted, practical, and micro-intervention-based spatial measures based on typical case analysis. The results show that 70.52% of the population in the study area is served by park entrances within a 1500 m walking distance, indicating that a considerable portion of residents remain beyond a reasonable walking distance. In the context of urban renewal and sustainable development, this study proposes practical improvements to park accessibility, including suggestions for determining suitable locations for new large parks as a long-term goal, alongside low-cost interventions such as increasing park entrances to maximize the use of existing resources and optimizing pedestrian routes (including opening gated communities and adding crossing facilities) to improve park walking service catchment in smaller environments. This study provides insights for urban park renewal, retrofitting, and expansion, supporting accessibility measures in planning practices, and is expected to provide valuable references for urban managers and policymakers. Furthermore, the study suggests that policy adjustments are necessary to integrate green spaces into urban development more effectively, particularly in rapidly urbanizing areas.

Proximity-based service discovery for distributed digital twin systems

Over the past decade, there has been a significant increase in interest in digital twin (DT) technology in a variety of domains. While research on DTs of single assets was initially prevalent, there has been a notable shift towards distributed systems of DTs, which connect to each other to collaborate. Typically, collaboration is enabled by DTs providing services that can be consumed by other DTs. In service-oriented systems, a service is typically identified by type information. However, this is not sufficient in distributed DT systems, where DTs associated with different physical entities may provide the same type of service. Consequently, selecting the appropriate service depends not only on the service type, but also on the associated physical entity. However, requiring DTs to know the mapping of services to their physical environment is not feasible for large dynamic systems. This paper presents a novel proximity-based service discovery method that allows DTs to select services based on service type and their proximity to other objects. That is, service specifications are fully abstracted from the mapping of services to physical objects, relieving DTs from maintaining information about this mapping. Furthermore, service discovery is robust to changes in the physical environment and service population. The proposed service discovery method has been implemented on top of a spatial DBMS. We argue that this implementation is optimal in terms of network utilization and latency, and perform comprehensive evaluations to show the performance of discovery queries as a function of their complexity.