Geospatial Research Research Articles

Considerations for enhancing participation and data accuracy in geospatial research in rural areas: experiences with PGIS in northern Malawi

ABSTRACT Rural environments are experiencing rapid changes that must be explored to understand, enhance, and facilitate positive changes and adapt to detrimental changes. However, the information researchers can obtain about the environment to identify effective management strategies for rural resources is hindered by several factors. Participatory geospatial research presents an approach that integrates local voices to map the facts and values of rural people and represent environmental changes. Here, we draw on more than six years of participatory geospatial research in rural northern Malawi to identify and present various considerations that participatory geospatial researchers and planners should be mindful of when working with rural people to enhance participation in research and improve spatial data accuracy. Based on experiences using various research methods and activities applied in several transdisciplinary collaborative research projects, we posit that rural geospatial researchers should keenly consider i) ethical issues concerning data collection, analysis, and representation, e.g. taboos and sacred spaces, ii) integrating local spatial ecological knowledge of people about the environment, and iii) economic conflicts and gender dynamics that tend to disempower and limit participation in research and affect data quality. Considering these would build rapport between participants and researchers to facilitate active participation and data accuracy.

Validating claims-based algorithms for a systemic lupus erythematosus diagnosis in Medicare data for informed use of the Lupus Index: a tool for geospatial research

ObjectiveThis study aimed to validate claims-based algorithms for identifying SLE and lupus nephritis (LN) in Medicare data, enhancing the use of the Lupus Index for geospatial research on SLE prevalence...

Private, Public, Personal: Shifting Patterns in Geospatial Data Sources in Geographic Research

Geospatial data sources include data collected by the public sector (i.e., government), private sector (i.e., industry), or through field work. Of these categories, the private sector, especially through tech firms, has provided new types of cutting-edge data sets that are particularly large and novel. These include data from social media platforms and location-based technologies such as Global Positioning Systems and mobile phones. Here, we explore the extent to which peer-reviewed geospatial research has adopted these data sources, perhaps in lieu of more traditional data types. We review peer-reviewed journal articles from six flagship journals in geographic information systems, geography, and urban analytics that publish research that uses spatial data analysis. We find that geospatial researchers continue to rely on government-collected data for their research, but that researchers’ use of data from the private sector have increased in recent years. This finding implies that as spatial analysis studies increasingly rely on private data sets, we should revisit (1) how industry data collection faces fewer regulations on data quality and has different motivations for their collection, (2) how this affects our ability to trust our data sets, and (3) the role of government and field work in data collection and dissemination in the future.

An open GNSS spoofing data repository: characterization and impact analysis with FGI-GSRx open-source software-defined receiver

Spoofing is becoming a prevalent threat to the users of Global Navigation Satellite Systems (GNSS). It is important to deepen our understanding of spoofing attacks and develop resilient techniques to effectively combat this threat. Detecting and mitigating these attacks requires thorough testing, typically conducted in a laboratory environment through the establishment of a spoofing test-bed. The complexity, cost and resource demands of creating such a test-bed underscore the necessity of utilizing openly available datasets. To address this need, this paper introduces a new GNSS spoofing data repository from Finnish Geospatial Research Institute (FGI) named hereafter as ‘FGI-SpoofRepo’. This data repository consists of raw In-phase and Quadrature (I/Q) data of live recordings of GPS L1 C/A, Galileo E1, GPS L5, and Galileo E5a signals. These datasets encompass three distinct types of spoofing characteristics (synchronous, asynchronous, and meaconing), making them very useful example candidates of open data for testing the performance of any anti-spoofing techniques (be it detection or mitigation). The inclusion of live signals in multiple GNSS frequencies and the presence of cryptographic signatures in Galileo E1 signal make these datasets potential benchmarks for assessing the resilience performance of multi-frequency multi-constellation receivers. The analysis of the datasets is carried out with an open-source MATLAB-based software-defined receiver, FGI-GSRx. An updated version of FGI-GSRx, equipped with the necessary modifications for processing and analyzing the new datasets, is released alongside the datasets. Therefore, the GNSS research community can utilize the open-source FGI-GSRx or any third-party SDR to process the publicly available raw I/Q data for implementation, testing and validation of any new anti-spoofing technique. The results show that time-synchronous spoofing seamlessly takes over positioning solution, while time-asynchronous spoofing acts as noise or in some cases, completely prevent the receiver from providing a positioning solution. Signal re-acquisition during an ongoing spoofing attack (cold start), the receiver tends to lock onto the spoofing signal with the highest peak, posing a potential threat to GNSS receivers without assisted information. Overall, this research aims to advance the understanding of complex spoofing attacks on GNSS signals, providing insight into enhancing resilience in navigation systems.

An Enhanced FGI-GSRx Software-Defined Receiver for the Execution of Long Datasets.

The Global Navigation Satellite System (GNSS) software-defined receivers offer greater flexibility, cost-effectiveness, customization, and integration capabilities compared to traditional hardware-based receivers, making them essential for a wide range of applications. The continuous evolution of GNSS research and the availability of new features require these software-defined receivers to upgrade continuously to facilitate the latest requirements. The Finnish Geospatial Research Institute (FGI) has been supporting the GNSS research community with its open-source implementations, such as a MATLAB-based GNSS software-defined receiver `FGI-GSRx' and a Python-based implementation `FGI-OSNMA' for utilizing Galileo's Open Service Navigation Message Authentication (OSNMA). In this context, longer datasets are crucial for GNSS software-defined receivers to support adaptation, optimization, and facilitate testing to investigate and develop future-proof receiver capabilities. In this paper, we present an updated version of FGI-GSRx, namely, FGI-GSRx-v2.0.0, which is also available as an open-source resource for the research community. FGI-GSRx-v2.0.0 offers improved performance as compared to its previous version, especially for the execution of long datasets. This is carried out by optimizing the receiver's functionality and offering a newly added parallel processing feature to ensure faster capabilities to process the raw GNSS data. This paper also presents an analysis of some key design aspects of previous and current versions of FGI-GSRx for a better insight into the receiver's functionalities. The results show that FGI-GSRx-v2.0.0 offers about a 40% run time execution improvement over FGI-GSRx-v1.0.0 in the case of the sequential processing mode and about a 59% improvement in the case of the parallel processing mode, with 17 GNSS satellites from GPS and Galileo. In addition, an attempt is made to execute v2.0.0 with MATLAB's own parallel computing toolbox. A detailed performance comparison reveals an improvement of about 43% in execution time over the v2.0.0 parallel processing mode for the same GNSS scenario.

Multi-observation points setting problem based on stepwise maximum viewshed approach

Terrain visibility analysis is vital in geospatial research, employing computer geometry and graphics principles for computing and visualizing visibility between observation and target points. The observation point setup problem is crucial for tasks like sentry point selection and signal base station placement. This problem involves choosing the minimum viewpoints on terrain for optimal joint view coverage, presenting a combinatorial optimization challenge. As technology advances, data can be acquired with increasing precision, the number of viewpoints that can be extracted from the same area is gradually increasing as well, obtaining candidate viewpoints and determining optimal combinations become challenging. This paper proposes a novel method, Stepwise Maximum Viewshed (SMV), addressing observation point setup by stepwise filtering the maximum viewshed and can customize the size of the observation point combination. In complex mountainous terrain, the SMV algorithm demonstrates superior joint view coverage compared to Candidate Viewpoints Filtering (CVF) and Simulated Annealing (SA) algorithms. Experimental results reveal up to 5.59% improvement over CVF and a maximum of 12.52% over SA in joint viewshed coverage.

Data quality analysis after hyperspectral LiDAR sequentially mapping trees

Abstract. Light detection and ranging (LiDAR), as an innovative remote sensing tool, not only captures target reflectance but also provides its morphological parameters. Traditional single/multi-band LiDAR and multispectral LiDAR (MSL) are presently employed in applications such as 3D modeling and plant biochemical parameter inversion albeit with effectiveness limited. Moreover, hyperspectral LiDAR (HSL) distinguished by its expanded array of spectral detection channels and enhanced spectral resolution, has proven more effective in meeting these requirements and also exhibits superior capabilities in both feature and land cover classification tasks. Nevertheless, point clouds acquired through HSL frequently exhibit quality deficiencies, including uneven density and excessive noise. Meanwhile, there exists a notable absence of technical specifications and operational standards governing the measurement protocols for HSL systems globally. To address this gap, this study constructed a systematic analysis framework of data quality in hyperspectral point clouds and endeavors to qualitatively analyse 30 tree point clouds continuously scanned with Finnish Geospatial Research Institute (FGI) 8-band hyperspectral laser scanner. Furthermore, this research validated the theoretical feasibility of employing the 8-band HSL system for inversion processes aimed at quantifying chlorophyll leaf content. Apart from detecting the time-varying patterns of reflectance within birch canopy point clouds, the results of this study also effectively pinpointed the band exhibiting heightened noise level of the HSL system, demonstrating the efficacy of our proposed quality analysis methodology. The endeavor presented in this study can serve as a cornerstone for advancing hyperspectral LiDAR across a diverse array of related remote sensing and earth observation applications.

Introduction to Reproducible Geospatial Analysis and Figures in R: A Tutorial Article

The present article is intended to serve an educational purpose for data scientists and students who already have experience with the R language and which to start using it for geospatial analysis and map creation. The basic concepts of raster data, vector data, CRS and datum are first presented along with a basic workflow to conduct reproducible geospatial research in R. Examples of important types of maps (scatter, bubble, choropleth, hexbin and faceted) created from open-source environmental data are illustrated and their practical implementation in R is discussed. Through these examples, essential manipulations on geospatial vector data are demonstrated (reading, transforming CRS, creating geometries from scratch, buffer zones around existing geometries and intersections between geometries).

Szanse reintegracji a remediacja cyfrowa geografii

Nowadays, geographers and specialists in other disciplines perceive information and communication technologies, including GIS software, as part of the inevitable changes in practice and science (including geography). Recent ways of generating, processing, storing, analyzing and sharing data, creating and sharing texts, visualizing, mapping, analyzing, communicating ideas, video, podcasts, and presentations, sharing information, social networks and mainstream media are entirely dependent on computing technologies. Modern science in the digital age uses big data, artificial intelligence – machine learning methods (data mining, pattern recognition, geovisualization, spatial statistics, optimization and spatial simulation), which in turn have renewed interest in and ties with the fields of computational social sciences and data-driven geography, to gain deeper insights into quantitative, time-scale, multi-resolution and multi-scale research. The tool and product of a geographer’s research is, among other things, the map, a medium, a vehicle for geographic theories and ideas and a tool for conveying geo-information. However, the remediation of a traditional map into a digital map involves the transformation, addition, restriction or change of the meaning system of the original. The new digital medium arouses different attitudes, different emotions and triggers new practices of receiving geo-information among professionals and the public. Undoubtedly, this also applies to researchers–geographers who apply in their geospatial research multi-, inter- or transdisciplinary approaches based on different research paradigms. Integrated and problem-focused scientific discourse in geographic research and digital remediation of maps allow us to look at the future of (currently diversified) geographic research with some hope for its reintegration. The aim of the paper is to identify the possibilities, current and prospective paths and research topics of geography in Poland, which are interdisciplinary, combining the field of interest of physical, socio-economic geographers and specialists of space economy, using the modern instrumentarium of information and communication technologies in geography, i.e. geographic information systems.

Marginalized Children’s Views of School Choice in Global Cities: The Significance of Neighbourhood and Nature

This study aims to illuminate the perspectives of 40 marginalized children about school choice in two global cities in Canada. It builds on the sociology of school choice and critical geography, with a focus on children. By utilizing mixed-methods geospatial research, this study finds that children prefer attending a neighbourhood school where they feel supported by the community and experience daily connections to nature. These children’s perspectives offer new insights into how to move beyond neoliberal market reforms by pursuing transformative and decolonial approaches to educational policy making.

Small area analysis methods in an area of limited mapping: exploratory geospatial analysis of firearm injuries in Port-au-Prince, Haiti

BackgroundThe city of Port-au-Prince, Haiti, is experiencing an epidemic of firearm injuries which has resulted in high burdens of morbidity and mortality. Despite this, little scientific literature exists on the topic. Geospatial research could inform stakeholders and aid in the response to the current firearm injury epidemic. However, traditional small-area geospatial methods are difficult to implement in Port-au-Prince, as the area has limited mapping penetration. Objectives of this study were to evaluate the feasibility of geospatial analysis in Port-au-Prince, to seek to understand specific limitations to geospatial research in this context, and to explore the geospatial epidemiology of firearm injuries in patients presenting to the largest public hospital in Port-au-Prince.ResultsTo overcome limited mapping penetration, multiple data sources were combined. Boundaries of informally developed neighborhoods were estimated from the crowd-sourced platform OpenStreetMap using Thiessen polygons. Population counts were obtained from previously published satellite-derived estimates and aggregated to the neighborhood level. Cases of firearm injuries presenting to the largest public hospital in Port-au-Prince from November 22nd, 2019, through December 31st, 2020, were geocoded and aggregated to the neighborhood level. Cluster analysis was performed using Global Moran’s I testing, local Moran’s I testing, and the SaTScan software. Results demonstrated significant geospatial autocorrelation in the risk of firearm injury within the city. Cluster analysis identified areas of the city with the highest burden of firearm injuries.ConclusionsBy utilizing novel methodology in neighborhood estimation and combining multiple data sources, geospatial research was able to be conducted in Port-au-Prince. Geospatial clusters of firearm injuries were identified, and neighborhood level relative-risk estimates were obtained. While access to neighborhoods experiencing the largest burden of firearm injuries remains restricted, these geospatial methods could continue to inform stakeholder response to the growing burden of firearm injuries in Port-au-Prince.

Classification and Object Detection on Satellite Images Using Custom CNN Architecture

As the variety of applications for satellite image analysis growing, some of these applications include surveillance, military operations, geospatial research, and environmental impact and climate change monitoring. One of the most important aspects of satellite image analysis is the capacity to automatically recognise and classify objects. Recognizing and classifying items in aviation images may be challenging due to the type and size of the objects as well as the shifting visual features. Because of the type and information included in these photographs, manual article location is quite laborious. It is appealing to automate the finding of various highlights or articles from these satellite photos. The conventional techniques for categorising things include two steps: I identifying the areas of the image where objects are present, and (ii) categorising the items in those areas. It's harder to recognise items because of the complicated background, size, noise, and distance features. According to this research, a specially created convolutional neural network may be used to recognise and classify three distinct objects in the photographs, including trees, buildings, and cars. Also, it aims to grasp and briefly sum up the performance characteristics of the fictitious custom CNN.

Geolocalization of apiaries in areas at risk of natural disasters using Geographic Information Systems in Villa Clara, Cuba

The honey bee depends on environmental conditions for its optimal development, its food base must be in healthy ecosystems and in equilibrium with diverse and abundant blooms, as well as abundant water. Cuba is exposed to different natural hazards and is affected every year by extreme meteorological phenomena. The objective of the study was to georeferenced apiaries in areas at risk to natural disasters in order to establish beekeeping reorganization and mitigation measures based on Geographic Information Systems (GIS). The work was carried out in Villa Clara province, Cuba. 100% of the apiaries and beehives were identified and an informative platform was created to locate them with the agroecosystem and their environment. The program Quantum GIS (QGIS, 2013) version 3.2.1 was used, a commercial and free GIS package for the basics of geospatial research, which made it possible to create an interactive platform of thematic layers for the evaluation and geographic analysis in relation to the apiaries and their environmental surroundings. The design of the information platform with thematic layers based on GIS facilitated the visualization of areas densely populated by beehives with bee colonies, the integral analysis of beekeeping agroecosystems, the identification and georeferencing of apiaries in areas at risk from natural disasters, and made it possible to establish measures for beekeeping reorganization and mitigation of meteorological events, such as flooding due to heavy rains, sea penetration, strong winds, and forest fires.

A COMPARATIVE STUDY OF METHODS FOR DRIVE TIME ESTIMATION ON GEOSPATIAL BIG DATA: A CASE STUDY IN USA

Abstract. Travel time estimation is crucial for several geospatial research studies, particularly healthcare accessibility studies. This paper presents a comparative study of six methods for drive time estimation on geospatial big data in the USA. The comparison is done with respect to the cost, accuracy, and scalability of these methods. The six methods examined are Google Maps API, Bing Maps API, Esri Routing Web Service, ArcGIS Pro Desktop, OpenStreetMap NetworkX (OSMnx), and Open Source Routing Machine (OSRM). Our case study involves calculating driving times of 10,000 origin-destination (OD) pairs between ZIP code population centroids and pediatric hospitals in the USA. We found that OSRM provides a low-cost, accurate, and efficient solution for calculating travel time on geospatial big data. Our study provides valuable insight into selecting the most appropriate drive time estimation method and is a benchmark for comparing the six different methods. Our open-source scripts are published on GitHub (https://github.com/wybert/Comparative-Study-of-Methods-for-Drive-Time-Estimation) to facilitate further usage and research by the wider academic community.

Fine-scale mapping of urban malaria exposure under data scarcity: an approach centred on vector ecology

BackgroundAlthough malaria transmission has experienced an overall decline in sub-Saharan Africa, urban malaria is now considered an emerging health issue due to rapid and uncontrolled urbanization and the adaptation of vectors to urban environments. Fine-scale hazard and exposure maps are required to support evidence-based policies and targeted interventions, but data-driven predictive spatial modelling is hindered by gaps in epidemiological and entomological data. A knowledge-based geospatial framework is proposed for mapping the heterogeneity of urban malaria hazard and exposure under data scarcity. It builds on proven geospatial methods, implements open-source algorithms, and relies heavily on vector ecology knowledge and the involvement of local experts.MethodsA workflow for producing fine-scale maps was systematized, and most processing steps were automated. The method was evaluated through its application to the metropolitan area of Dakar, Senegal, where urban transmission has long been confirmed. Urban malaria exposure was defined as the contact risk between adult Anopheles vectors (the hazard) and urban population and accounted for socioeconomic vulnerability by including the dimension of urban deprivation that is reflected in the morphology of the built-up fabric. Larval habitat suitability was mapped through a deductive geospatial approach involving the participation of experts with a strong background in vector ecology and validated with existing geolocated entomological data. Adult vector habitat suitability was derived through a similar process, based on dispersal from suitable breeding site locations. The resulting hazard map was combined with a population density map to generate a gridded urban malaria exposure map at a spatial resolution of 100 m.ResultsThe identification of key criteria influencing vector habitat suitability, their translation into geospatial layers, and the assessment of their relative importance are major outcomes of the study that can serve as a basis for replication in other sub-Saharan African cities. Quantitative validation of the larval habitat suitability map demonstrates the reliable performance of the deductive approach, and the added value of including local vector ecology experts in the process. The patterns displayed in the hazard and exposure maps reflect the high degree of heterogeneity that exists throughout the city of Dakar and its suburbs, due not only to the influence of environmental factors, but also to urban deprivation.ConclusionsThis study is an effort to bring geospatial research output closer to effective support tools for local stakeholders and decision makers. Its major contributions are the identification of a broad set of criteria related to vector ecology and the systematization of the workflow for producing fine-scale maps. In a context of epidemiological and entomological data scarcity, vector ecology knowledge is key for mapping urban malaria exposure. An application of the framework to Dakar showed its potential in this regard. Fine-grained heterogeneity was revealed by the output maps, and besides the influence of environmental factors, the strong links between urban malaria and deprivation were also highlighted.

IJAGR at the International Medical Symposium Geography 2022 Edinburgh, Scotland

Donald Albert (co-Editor-in-Chief) from Sam Houston State University and Dhitinut Ratnapradipa (Associate Editor) from Creighton University participated in the 19th International Medical Symposium Geography (IMGS) from June 19-June 24, 2022, in Edinburgh, Scotland. The event operated out of the Royal College of Surgeons located in the Old Town of Edinburgh. This historic venue provided an appropriate setting to contemplate patterns of health and disease. Jamie Pearce and Niamh Shortt (University of Edinburgh) were co-Chairs of the IMGS 2022. The authors' poster abstract was entitled, “The International Journal of Applied Geospatial Research: Temporal Metrics and Coverage of Medical Geography, 2010-2021.”

Geospatial Research Supporting Decision-Making in Court Services – an Assessment of the 2019 District Court Reform in Finland

As a result of the district court reform which came into force in Finland in 2019, the number of district court locations decreased from 57 to 36. The idea is that the reform strengthens the operational capacity of the district courts by forming administratively larger units. The main objective of this study is to assess the district court reform with the spatial optimization technique and demonstrate how the geospatial analysis could be utilized in the planning and decision-making process of the district court network. The results show that the reform is not geographically optimal and that, in the future, the district court network cannot be centralized very much without worsening the accessibility of the nearest district court radically. The study demonstrates that district court reform could benefit from geospatial research methods, such as location allocation analysis, as these methods offer an objective criterion for the planning and decision-making of the service networks and operational practices. Based on the findings, the next structural reform and development of the district court network in Finland should therefore be based on the development of functionality instead of shrinking the size of the network if the distances are not to increase further. The results also underline the need for new ways to develop the accessibility of district courts through digitalization.

Experience and prospects of mobile GIS use for practical training of geography students

Problem statement. In recent decades, approaches to conducting field research have been significantly modernized. The use of smartphones, tablets and other portable devices with mobile GIS for field data collection has increased significantly. The rapid change in geospatial research technologies makes it necessary to introduce them into the educational process, in particular, in practical training of geography students. Research methodology. Paper forms of field data collection, materials and reports from practical trainings of the previous years, stored at the Department of Physical Geography and Cartography of V. N. Karazin Kharkiv National University, served us as the source materials for the research. To implement the practical part of the research, ESRI software products such as Collector for ArcGIS, ArcGIS Online, ArcGIS Desktop were used. The purpose of the research is to substantiate the content and to create electronic forms of field data collection with mobile GIS for the physiographic part of the educational natural science training of first-year students of specialty 106 Geography of V. N. Karazin Kharkiv National University. Results. The use of mobile GIS for practical training of future geographers is widespread in Ukrainian and foreign universities. In particular, in recent years, teachers of the Department of Physical Geography and Cartography have been introducing mobile GIS into field data collection. We have developed 4 electronic forms of field data collection for the physiographic part of the educational natural science training of first-year students. They are soil profile description, forest phytocenosis description, geological and geomorphological observations, and hydrological station. Due to the wide functionality of Collector for ArcGIS, in particular, the ability to work offline, this mobile application and ArcGIS Online were used to develop forms. To optimize the work with individual text fields in electronic forms, we created domain values that allow students to select a certain option from the drop-down list. Practical significance and research perspectives. The implementation of mobile GIS into practical training of future geographers contributes to the formation of cartographic and geoinformation competencies and helps students master modern approaches to the organization of various types of field studies. Testing developed electronic forms during summer practical training of students or through independent research in the study area remains a relevant task. In the future, it is advisable to create electronic forms for all sections of the physiographic part of the first-year students' practical training and to adapt data collection forms for independent use.

Geospatial Analysis and Land Suitability for “FloodWise” Practices: Nature-Based Solutions for Flood Mitigation in Eastern, Rural North Carolina

As the global climate continues to change, extreme weather events such as hurricanes and heavy rainfall are becoming more frequent. Subsequently, flooding and standing water disrupt and negatively impact many communities. The use of nature-based solutions (NBS) is an innovative and sustainable approach to flood mitigation. Geospatial research and applications have developed rapidly to identify and map broad regions in the world, as well as specific locations for NBS. We conducted a geospatial analysis in ArcGIS Pro to identify areas where NBS, referred to as “FloodWise” practices in this study, could be sited in the North Carolina Coastal Plain to strategically reduce flooding and provide water quality and habitat improvement. The study provides a spatially explicit application of integrated remote sensing, scientific and professional knowledge, and extant databases to screen diverse variables and identify potential specific NBS opportunities and sites. The practices modeled in this study are wetland restoration, afforestation, agroforestry, “water farming” (which uses a combination of dry dams and berms), and stream restoration. Maps of specific areas and tracts in the county for the NBS practices in Robeson County, North Carolina were developed based on the land ownership size, biophysical characteristics, current land uses, and water management opportunities. Land suitability locations revealed in these maps can be used in future resilience planning initiatives to reduce floodwaters on North Carolina’s rural landscapes. The geospatial analysis methodologies employed in this study can be followed to model NBS locations for flood reduction and water storage opportunities in other counties in Eastern North Carolina or other regions with similar topographies and land-type characteristics.

How public and participant voices have influenced the UK Longitudinal Linkage Collaboration and its Trusted Research Environment.

ObjectivesUK longitudinal population studies have complex governance structures and participant safeguards. As a new Trusted Research Environment (TRE) for >20 longitudinal studies, the UK Longitudinal Linkage Collaboration (UK LLC) must ensure effective public/participant input to make central design and operational decisions whilst retaining participant trust and acceptability. ApproachThe UK LLC’s governance model is based on the principle that participant trust lies with their study; not supporting infrastructure providers. Therefore, the UK LLC takes responsibility for system governance and data sharing agreements whilst studies retain control over participant data and its uses. Our public/participant model reflects this. We have established centralised groups to advise on data access applications and on UK LLC communications. Simultaneously we work with study managers to gain insight from their involvement work and also commission UK LLC specific activities. Including participants as well as the public helps elicit a range of input and views. ResultsWe have integrated public/participant input across the UK LLC by recruiting: a public panel to review applications; an advisory panel to contribute to our methodological development; and public/participant advisors to sit on the UK LLC Strategic Advisory Committee. We also work with studies’ involvement managers to draw insights from, and coordinate new, study-level involvement. This has informed the development of data application frameworks and brings a public viewpoint to decisions regarding linkage of functionally anonymous participant NHS records for research. The involvement of study participant groups has shaped the design of data flows into the TRE (e.g., where some studies do not permit the flow of address data for geospatial research) and has helped set a ‘social contract’ regarding acceptable UK LLC data use. ConclusionWorking with public/participant representatives directly, and in partnership with study managers, we have developed safeguards and integrated a public/participant voice into key UK LLC activities. Our ongoing aim is to develop a sustainable new-way-of-working for record linkage in longitudinal research which is acceptable to participants and the public.