Geospatial Methodology Research Articles

Analysing Spatio-temporal Dynamics of Urban Sprawl, Evolving Pattern of Urban Landscape and Driving Forces of Urban Growth: A Case of Varanasi Planning Region, India

Urban sprawl is at the centre of contemporary urban debates and is very often criticised for its negative social and environmental externalities. Building on geo-spatial methodology and field observations, this study sets out to develop a holistic approach to understanding urban sprawl through a particular focus on a rapidly growing metropolitan urban agglomeration of northern India, that is, Varanasi. Following an analysis of principal urban growth modes through landscape expansion index and changing patterns of urban landscape through landscape metrics, this research refers to a geo-spatially grounded logistic regression model in order to shed light on the dynamic impact of multiple growth factors on the trajectory of urban sprawl in the study area. Moreover, in order to address the evolving nature of urban sprawl in Varanasi and its immediate surroundings this study focuses on a temporal span of 20 years, encompassing two consecutive decades of the 21st century (2001–2011 and 2011–2021). The research concludes by paving a platform for an amalgamation of the findings from different geo-spatial metrics as well as by corroborating the principal findings with the propositions of a number of contemporary hypotheses concerning the spatial growth of urbanised territories.

GIS-based method for assessing the viability of solar-powered irrigation

To attain food security, sub-Saharan Africa needs to increase its use of irrigation to improve agricultural productivity and resilience to climate change-induced droughts and erratic rain patterns. Stand-alone solar-powered irrigation systems are a promising technology to power irrigation where grid electrification is unavailable. However, these systems’ economic viability and sustainability are affected by spatially varying factors such as climate, water availability, and solar potential, making it challenging to plan their roll-out. This study addresses the planning challenge by developing a novel open-source reproducible geospatial methodology to determine irrigation water demand, peak power demand, life cycle cost, and locations where solar-powered irrigation systems would be cheaper than alternative off-grid irrigation technologies. The method considers spatial–temporal data for meteorological conditions, soil quality, water distance and depth, solar resource potential, technology efficiency and local equipment and fuel costs. Maize cultivation in Kenya is taken as a case study to demonstrate the method. We find the median peak irrigation water demand in Kenya to be 65,000 l/ha/day and the median peak power demand to be 2.6 kW/ha. The levelized cost of solar-powered irrigation systems ranges from US$ 0.09/kWh to US$ 0.25/kWh, making solar irrigation cheaper than diesel-powered irrigation in all Kenyan regions. The results from the method offer valuable insights to governments, farmers and investors on where water use regulations are required for sustainable water use, where subsidies and innovative financial models are needed for the affordability of solar irrigation and where alternative uses of energy from the solar systems beyond irrigation are possible.

Integrating RUSLE Model with Cloud-Based Geospatial Analysis: A Google Earth Engine Approach for Soil Erosion Assessment in the Satluj Watershed

This study employed an advanced geospatial methodology using the Google Earth Engine (GEE) platform to assess soil erosion in the Satluj Watershed thoroughly. To achieve this, the Revised Universal Soil Loss Equation (RUSLE) model was integrated into the study, which was revealed through several analytical tiers, each with a unique function. The study commenced with estimating the R factor, which was carried out using annual precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS). The erodibility of the soil, which the K factor describes, was then calculated using the USDA soil texture classifications taken from the Open Land Map. The third layer emphasizes the LS factor, which analyzes slope data and how they affect soil erosion rates, using digital elevation models. To understand the impact of vegetation on soil conservation, the fourth layer presents the C factor, which evaluates changes in land cover, and the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 data. The P factor incorporates MODIS data to assess the types of land cover and slope conditions. Combining these layers with the RUSLE model produces a thorough soil loss map, revealing different levels of soil erosion throughout the Satluj Watershed. The preliminary findings indicate that 3.3% of the watershed had slight soil loss, 0.2% had moderate loss, and 1.2% had high soil erosion rates. And 92% had severe rates of soil erosion. After a thorough investigation, the detected regions were divided into risk classifications, providing vital information for the watershed’s land management and conservation plans. The mean soil loss throughout the watershed was determined to be 10,740 tons/ha/year. This novel method creates a strong foundation for evaluating soil erosion, while also highlighting the value of the cloud-based geospatial analysis and the RUSLE model in comprehending intricate environmental processes.

Geospatial methodology for determining the regional prevalence of hospital-reported childhood intussusception in patients from India

Both developed and developing countries carry a large burden of pediatric intussusception. Sentinel site surveillance-based studies have highlighted the difference in the regional incidence of intussusception. The objectives of this manuscript were to geospatially map the locations of hospital-confirmed childhood intussusception cases reported from sentinel hospitals, identify clustering and dispersion, and reveal the potential causes of the underlying pattern. Geospatial analysis revealed positive clustering patterns, i.e., a Moran’s I of 0.071 at a statistically significant (p value < 0.0010) Z score of 16.14 for the intussusception cases across India (cases mapped n = 2221), with 14 hotspots in two states (Kerala = 6 and Tamil Nadu = 8) at the 95% CI. Granular analysis indicated that 67% of the reported cases resided < 50 km from the sentinel hospitals, and the average travel distance to the sentinel hospital from the patient residence was calculated as 47 km (CI 95% min 1 km–max 378 km). Easy access and facility referral preferences were identified as the main causes of the existing clustering pattern of the disease. We recommend designing community-based surveillance studies to improve the understanding of the prevalence and regional epidemiological burden of the disease.

Impervious Land Expansion as a Control Parameter for Climate-Resilient Planning on the Mediterranean Coast: Evidence from Greece

Impervious land expansion is linked to ecosystem fragmentation and degradation, posing threats to nature conservation and multiplying climate change impacts. This is particularly true on the Mediterranean coast, where persistent urbanization is coupled with tourism development, further intensifying coastal erosion, flood risk, heat stress and biodiversity loss, while decreasing carbon sequestration. In this research, high-resolution imperviousness data were analyzed through a geospatial methodology to detect patterns and processes in a heavily burdened Mediterranean coastal area, namely Greece. The methodology was structured on a set of GIS tools, analyzing the distribution of new impervious cover between 2006 and 2018, to evaluate pressures exerted on coastal territories and on the environmental protection network. The results revealed relatively slow rates of impervious land expansion at a nationwide scale, mostly attributed to the economic recession period in Greece. However, certain locations exhibited continuing artificialization of land even within Natura 2000 areas, while future pressures on coastal territories are expected to increase due to the restarting of construction activity and the continuing dynamic of the mass tourism sector. The conclusions imply that controlling for imperviousness is important in order to develop spatial planning policies for climate resilience, which should be decisively enforced in the Mediterranean to prevent a business-as-usual scenario.

A pseudo-3D methodology for regional-scale back-analysis of earthquake-induced landslides

Major earthquakes can cause hundreds to thousands of landslides in mountainous areas, but the methods for regional earthquake-induced landslide analyses are currently not well developed. This paper proposes an algorithm for back-analyzing earthquake-induced landslide inventories. The result of the back-analysis (or “inversion”) is a modeled three-dimensional (3D) “best-matching” landslide for each mapped landslide in an inventory as well as an estimate of average shear strength along the landslide failure plane. The algorithm leverages seismic displacement models to detect the triggering location (i.e., triggering grid cell) of modeled landslides. The triggering grid cell is then projected to form a 3D cone-shape failure surface for each modeled landslide using geomorphic characteristics, i.e., flow direction and flow accumulation, and a geometric rule, a procedure termed here as pseudo-three-dimensional (pseudo-3D). The inversion algorithm uses the landslide location, area, and volume (if available) of each mapped landslide to find a modeled landslide that minimizes the “mismatch” between the two, expressed as total error, and outputs an associated triggering depth, landslide geometry, and strength. The estimate of strength for each individual landslide can then, if needed, be aggregated using a geospatial methodology (e.g., geologic unit information or a K-means clustering method) to derive regional-scale Mohr-Coulomb shear strength parameters. The potential bias of the proposed pseudo-3D methodology on the strength estimate is assessed against synthetic landslide inventories generated using a 3D limit equilibrium method. The back-calculated friction angles agree well with 3D results, while the cohesions are lower than the estimated cohesions from 3D slope stability analysis. In addition, the effect of different seismic displacement models on the results of the back-calculated strengths is investigated. The proposed methodology is implemented to the 2015 Lefkada earthquake event in Greece, which triggered more than 700 landslides, with the goal to demonstrate the efficiency in the inversion of many landslides and the generation of regional estimates of strength parameters. For this study region, the spatial distribution of the shear strength results indicates that areas with fewer landslides have higher shear strength while areas with more landslides have lower shear strength.

Application of a CNN to the Boda Claystone Formation for high-level radioactive waste disposal

Nations relying on nuclear power generation face great responsibilities when designing their firmly secured final repositories. In Hungary, the potential host rock [the Boda Claystone Formation (BCF)] of the deep geological repository is under extensive examination. To promote a deeper comprehension of potential radioactive isotope transport and ultimately synthesis for site evaluation purposes, we have efficiently tailored geospatial image processing using a convolutional neural network (CNN). We customized the CNN according to the intricate nature of the fracture geometries in the BCF, enabling the recognition process to be particularly sensitive to details and to interpret them in the correct tectonic context. Furthermore, we set the highest processing scale standards to measure the performance of our model, and the testing circumstances intentionally involved various technological and geological hindrances. Our presented model reached ~ 0.85 precision, ~ 0.89 recall, an ~ 0.87 F1 score, and a ~ 2° mean error regarding dip value extraction. With the combination of a CNN and geospatial methodology, we present the description, performance, and limits of a fully automated workflow for extracting BCF fractures and their dipping data from scanned cores.

Mapping of Flood-Prone Areas Utilizing GIS Techniques and Remote Sensing: A Case Study of Duhok, Kurdistan Region of Iraq

One of the most common types of natural disaster, floods can happen anywhere on Earth, except in the polar regions. The severity of the damage caused by flooding can be reduced by putting proper management and protocols into place. Using remote sensing and a geospatial methodology, this study attempts to identify flood-vulnerable areas of the central district of Duhok, Iraq. The analytical hierarchy process (AHP) technique was used to give relative weights to 12 contributing parameters, including elevation, slope, distance from the river, rainfall, land use land cover, soil, lithology, topographic roughness index, topographic wetness index, aspect, the sediment transport index, and the stream power index in order to calculate the Flood Hazard Index (FHI). The relative importance of each criterion was revealed by a sensitivity analysis of the parameter values. This research developed a final flood susceptibility map and identified high-susceptible zones. This was classified anywhere from very low to very high classifications for its potential flood hazard. The generated map indicates that 44.72 km2 of the total land area of the study area in Duhok city has a very high susceptibility to flooding, and that these areas require significant attention from government authorities in order to reduce flood vulnerability.

Investigating groundwater potential in northeastern basement complexes: A Pulka case study using geospatial and geo-electrical techniques

Groundwater is a crucial resource for communities and plays a significant role in the economy, especially in regions where surface water sources are limited or unreliable. Nigerian Northeastern basement complexes are unique geologic formations with specific hydrological characteristics that impact groundwater availability and quality. However, there is a lack of comprehensive information on groundwater potential in these areas, hindering effective resource management. To address this gap, this study focuses on the application of geospatial and electrical techniques to map and assess groundwater potential in Pulka, a northeastern basement complex in Nigeria. This research used geospatial methodology to delineate groundwater-prospective areas in Pulka, Northern Nigeria, with the aid of a VES technique that was integrated with the Schlumberger electrode configuration, the Shuttle Radar Topography Mission (SRTM), which were used as input datasets. Lineament and drainage density, lithology and slope, land use and land cover, and thematic maps were created as GIS input rasters. The value estimates were allotted based on a pair-wise examination of elements that appeared to be important in groundwater accumulation, retention, and transportation. The Analytical Hierarchy Process (AHP) was used in GIS software to incorporate five maps of components impacting the suitability of groundwater and movement using weighted layering. The hydraulic conductivity and longitudinal conductance of Pulka are greatest towards the west, with maximum values of 0.0665 m/day and 1.75 S, respectively. Transverse resistance and transmissivity are greatest in the eastern section of Pulka, with maximum values of 48.29 m2/day and 9000 m, respectively. Based on our research using SRTM, the most important elements for groundwater potential were soil type, which represented 43%; lineament density, which represented 33%; slope, with 13%; drainage density, with 7%; and land use and land cover, which accounted for the least (around 4% of overall contributions). Pulka has enormous groundwater development potential for sustainable and profitable extractions, according to analysis based on geospatial and geo-electrical techniques.

Geospatial assessment of elevated agrivoltaics on arable land in Europe to highlight the implications on design, land use and economic level

Agrivoltaic systems (a combination of agricultural crop production and photovoltaics (PV) on the same land) have an increasing interest. Realizing this upcoming technology raises still many challenges at design, policy and economic level. This study addresses a geospatial methodology to quantify the important design and policy questions across Europe. An elevated agrivoltaic system on arable land is evaluated: three crop light requirements (shade-loving, shade-tolerant and shade-intolerant) are simulated at a spatial resolution of 25 km across the European Union (EU). As a result, this study gives insight into the needed optimal ground coverage ratio (GCR) of the agrivoltaic system for a specific place. Additionally, estimations of the energy production, levelized cost of energy (LCOE) and land equivalent ratio (LER) are performed in comparison with a separated system. The results of the study show that the location-dependent solar insolation and crop shade tolerance have a major influence on the financial competitiveness and usefulness of these systems, where a proper European policy system and implementation strategy is required. Finally, a technical study shows an increase in PV power of 1290 GWp (almost × 10 of the current EU’s PV capacity) if potato cultivation alone (1% of the total arable agricultural area) is converted into agrivoltaic systems.

Geospatial passives for dynamic vegetation monitoring around thermal power plants.

As point sources of pollution, thermal power plants (TPPs) emanate hazardous gaseous and particulate matter that are of significant detriment to surrounding biological landscapes. To provide support to ecological conservation and resource management in developing countries, this study aims to establish a cost effective and robust geospatial methodology for dynamic vegetation monitoring of local pollution zones around TPPs using passive satellite-based indicators. The extent and severity of hazardous bio-influence around four TPPs is identified and monitored for a period of 5years, using vegetation indices (VIs). High correlations of vegetation health with distance from TPPs have also been identified, signifying the hazardous impact of TPP emissions to surrounding vegetation. Variations in behavior of zones of high pollutant concentration are observed both in space and time, as a response to local seasonal weather, nature of fuel used in TPP, and type and areal coverage of vegetation around the power plants. Winter and Monsoon seasons have been identified to create favorable conditions for sustaining high pollution concentration around TPPs, and hence, the extent of hazardous bio-influence zones in these seasons is maximum. Moreover, oil-based power plant is revealed to be associated with large radial zones of degraded vegetation around it and, therefore, poses greater ecological hazard than gas-powered TPPs. The average bio influence zone measured for the test sites has been found to be 1660m that ranges from 1600 to 1730m for different power plants, explaining variable behavior of the used fuel and surrounding vegetation conditions. In this way, the study stresses upon the importance of geospatial data and analytical frameworks in reliable and economical monitoring of environmental pollution associated with anthropogenic sources, using passive environmental indices derived from remote data.

Approximating Community Water System Service Areas to Explore the Demographics of SDWA Compliance in Virginia.

Although the United States Safe Drinking Water Act (SDWA) theoretically ensures drinking water quality, recent studies have questioned the reliability and equity associated with community water system (CWS) service. This study aimed to identify SDWA violation differences (i.e., monitoring and reporting (MR) and health-based (HB)) between Virginia CWSs given associated service demographics, rurality, and system characteristics. A novel geospatial methodology delineated CWS service areas at the zip code scale to connect 2000 US Census demographics with 2006–2016 SDWA violations, with significant associations determined via negative binomial regression. The proportion of Black Americans within a service area was positively associated with the likelihood of HB violations. This effort supports the need for further investigation of racial and socioeconomic disparities in access to safe drinking water within the United States in particular and offers a geospatial strategy to explore demographics in other settings where data on infrastructure extents are limited. Further interdisciplinary efforts at multiple scales are necessary to identify the entwined causes for differential risks in adverse drinking water quality exposures and would be substantially strengthened by the mapping of official CWS service boundaries.

Identifying priority and bright spot areas for improving diabetes care: a geospatial approach

The objective of this study was to describe a novel geospatial methodology for identifying poor-performing (priority) and well-performing (bright spot) communities with respect to diabetes management at the ZIP Code...

Geodata-driven approaches to financial inclusion – Addressing the challenge of proximity

Financial Inclusion is, in many ways, a spatial planning issue: Where do financial institutions provide services, how far do customers travel to access mobile money, which services are available where and how is agent cash-flow handled? Utilising geodata can contribute significantly to measuring financial access and thus assist in improving Financial Inclusion by expanding the reach of services and locating areas of economic exclusion. This study presents a new Spatial Decision Support System, with a frontend embedded directly within a spreadsheet interface, that enables measuring and planning financial access through geospatial analysis and Earth Observation derived products. The purpose is to complement existing Financial Inclusion measures, which rely significantly on large-scale representative household surveys to quantify financial access and opportunity to proxy quality of inclusion. The Decision Support System relies on Earth Observation and Public Participatory GIS, which enables a decoupling from the census cycle and global reach. Our findings indicate that a geospatial approach to measuring and making decisions regarding the location of financial access points can positively affect both tracking and delivering Financial Inclusion and reducing the urban–rural service cliff-edge. Our proposed geospatial methodology is useful for decision-makers in two ways: a) It allows the measurement of the large-scale geospatial reach of financial services – useful for decision-makers, planners, politicians, national statistical offices, and NGOs in charge of tracking progress towards the Sustainable Development Goals. b) It helps with planning and optimising services for local financial entities such as mobile money agents, brick and mortar bank branches, and less formal saving mechanisms such as saving clubs. The Spatial Decision Support System is currently used by several Financial Service Providers in Ghana and undergoing implementation for one in North-western Tanzania.

A Geospatial Approach for Mapping the Earthquake-Induced Liquefaction Risk at the European Scale

This paper presents a geospatial methodology for zoning the earthquake-induced soil liquefaction risk at a continental scale and set-up in a Geographic Information System (GIS) environment by coupling data-driven and knowledge-driven approaches. It is worth mentioning that liquefaction is a phenomenon of soil instability occurring at a very local spatial scale; thus, the mega-zonation of liquefaction risk at a continental scale is a hard facing challenge. Since the risk from natural disasters is the convolution of hazard, vulnerability, and exposure, the liquefaction risk mapping is based on the combination of geospatial explanatory variables, available at the continental scale, of the previously listed three assumed independent random variables. First, by applying a prediction model calibrated for Europe, the probability of liquefaction is mapped for the whole continent. Then, the Analytical Hierarchy Process (AHP) is adopted to identify areas that have a high risk of liquefaction, taking into account proxy data for exposure. The maps are computed for different levels of severity of ground shaking specified by three return periods (i.e., 475, 975, and 2475 years). A broad variety of stakeholders would benefit from the outcomes of this study, such as civil protection organizations, insurance and re-insurance companies, and infrastructure operators.

Spatial prediction and mapping of the COVID-19 hotspot in India using geostatistical technique

The world has now facing a health crisis due to outbreak of novel coronavirus 2019 (COVID-19). The numbers of infection and death have been rapidly increasing which result in a serious threat to the social and economic crisis. India as the second most populous nation of the world has also running with a serious health crisis, where more than 8,300,500 people have been infected and 123,500 deaths due to this deadly pandemic. Therefore, it is urgent to highlight the spatial vulnerability to identify the area under risk. Taking India as a study area, a geospatial analysis was conducted to identify the hotspot areas of the COVID-19. In the present study, four factors naming total population, population density, foreign tourist arrivals to India and reported confirmed cases of the COVID-19 were taken as responsible factors for detecting hotspot of the novel coronavirus. The result of spatial autocorrelation showed that all four factors considered for hotspot analysis were clustered and the results were statistically significant (p value < 0.01). The result of Getis-Ord Gi* statistics revealed that the total population and reported COVID-19 cases have got high priority for considering hotspot with greater z-score (> 3 and > 0.7295 respectively). The present analysis reveals that the reported cases of COVID-19 are higher in Maharashtra, followed by Tamil Nadu, Gujarat, Delhi, Uttar Pradesh, and West Bengal. The spatial result and geospatial methodology adopted for detecting COVID-19 hotspot in the Indian subcontinent can help implement strategies both at the macro and micro level. In this regard, social distancing, avoiding social meet, staying at home, avoiding public transport, self-quarantine and isolation are suggested in hotspot zones; together with, the international support is also required in the country to work jointly for mitigating the spread of COVID-19.Electronic supplementary materialThe online version of this article (10.1007/s41324-020-00375-1) contains supplementary material, which is available to authorized users.

Facing a Second Wave from a Regional View: Spatial Patterns of COVID-19 as a Key Determinant for Public Health and Geoprevention Plans.

Several studies on spatial patterns of COVID-19 show huge differences depending on the country or region under study, although there is some agreement that socioeconomic factors affect these phenomena. The aim of this paper is to increase the knowledge of the socio-spatial behavior of coronavirus and implementing a geospatial methodology and digital system called SITAR (Fast Action Territorial Information System, by its Spanish acronym). We analyze as a study case a region of Spain called Cantabria, geocoding a daily series of microdata coronavirus records provided by the health authorities (Government of Cantabria—Spain) with the permission of Medicines Ethics Committee from Cantabria (CEIm, June 2020). Geocoding allows us to provide a new point layer based on the microdata table that includes cases with a positive result in a COVID-19 test. Regarding general methodology, our research is based on Geographical Information Technologies using Environmental Systems Research Institute (ESRI) Technologies. This tool is a global reference for spatial COVID-19 research, probably due to the world-renowned COVID-19 dashboard implemented by the Johns Hopkins University team. In our analysis, we found that the spatial distribution of COVID-19 in urban locations presents a not random distribution with clustered patterns and density matters in the spread of the COVID-19 pandemic. As a result, large metropolitan areas or districts with a higher number of persons tightly linked together through economic, social, and commuting relationships are the most vulnerable to pandemic outbreaks, particularly in our case study. Furthermore, public health and geoprevention plans should avoid the idea of economic or territorial stigmatizations. We hold the idea that SITAR in particular and Geographic Information Technologies in general contribute to strategic spatial information and relevant results with a necessary multi-scalar perspective to control the pandemic.

STUDYING EVOLUTION OF HYDROTHERMAL ALTERATION MATERIALS IN THE TURRIALBA VOLCANO TROUGH MULTISPECTRAL AND HYPERSPECTRAL IMAGES

Abstract. The aim of this work is to develop a geospatial methodology for the analysis of the time evolution of The Turrialba volcano using different automatic imaging techniques compared to expert-based remote sensing techniques. Change detection of hydrothermal alteration materials in relation with time series from multisensor data acquired in spectral ranges of the visible (VIS) and short wave infrared (SWIR) have been calculated. We used for this purpose multispectral and hyperspectral scenes of the Sentinel 2, ALI and Hyperion sensors, respectively, on four dates from 2013 and 2018. This work adopts a multi-source approach, applied to the analysis of the correlations between hydrothermal materials and spectral anomalies in The Turrialba volcano complex, located in The Central Volcanic Range (Costa Rica).An expert-based technique called Crosta’s technique for detecting hydrothermal materials have been applied. We have chosen four variables for generating a different Principal Component Analysis (PCA) for groups of channels, two highly reflective and two highly absorptive for each mineral. We have tested another technique to detect hydrothermal materials based on a discrete spectral profile analysis and an unsupervised data mining approach. In other sense, we have applied an automatic technique called anomaly detection to compare with the hydrothermal materials results. Results are presented as an approach based on a comparison of two different strategies whose main future interest lies in the automated identification of patterns of hydrothermally altered materials without prior knowledge or poor information about the area, which has relevant implications in image-based prospecting.

Current State of Geospatial Methodologic Approaches in Canadian Population Oncology Research.

Geospatial analyses are increasingly used in population oncology. We provide a first review of geospatial analysis in Canadian population oncology research, compare to international peers, and identify future directions. Geospatial-focused peer-reviewed publications from 1992-2020 were compiled using PubMed, MEDLINE, Web of Science, and Google Scholar. Abstracts were screened for data derived from a Canadian cancer registry and use of geographic information systems. Studies were classified by geospatial methodology, geospatial unit, location, cancer site, and study year. Common limitations were documented from article discussion sections. Our search identified 71 publications using data from all provincial and national cancer registries. Thirty-nine percent (N = 28) were published in the most recent 5-year period (2016-2020). Geospatial methodologies included exposure assessment (32.4%), identifying spatial associations (21.1%), proximity analysis (16.9%), cluster detection (15.5%), and descriptive mapping (14.1%). Common limitations included confounding, ecologic fallacy, not accounting for residential mobility, and small case/population sizes. Geospatial analyses are increasingly used in Canadian population oncology; however, efforts are concentrated among a few provinces and common cancer sites, and data are over a decade old. Limitations were similar to those documented internationally, and more work is needed to address them. Organized efforts are needed to identify common challenges, develop leading practices, and identify shared priorities.

Assessing marine ecosystem services richness and exposure to anthropogenic threats in small sea areas: A case study for the Lithuanian sea space

• The Lithuanian sea space is a small sea area. • Twelve MES were assessed and mapped. • MES rich areas are located in the Mainland Coast. • Coastal Stripe and Territorial Waters are priority planning areas. • The study area is subjected to multiple anthropogenic threats. The Lithuanian sea space belongs to the smallest sea areas in Europe. The sea space incorporates multiple marine ecosystem services (MES) that support human-wellbeing and sustain maritime economies, but is also subjected to intensive anthropogenic activities that can affect its vulnerable ecological components. We present a flexible geospatial methodology to assess MES richness ( MESR ) and to analyse areas of exposure of MES to human impacts using a MES exposure index ( MESEx ). Source of anthropogenic threats to MES were firstly derived from the Marine Strategy Framework Directive and include marine litter (from ports and shipping), underwater noise (from offshore pile driving and shipping) and hazardous substances (from oil extraction platforms). Results were presented for the three main planning areas in Lithuania, the Lithuanian Coastal Stripe, territorial waters and EEZ. In detail, areas of highest MESR are located in the coastal areas of the Lithuanian Mainland Coast that are particularly rich in ecosystem services such as nursery function from for Baltic Herring and cultural services related to valuable recreational resorts, landscape aesthetic values and natural heritage sites. Modelled pressure exposure on selected MES show that cultural ecosystem services in proximity of Klaipėda Port can be particularly affected by marine litter accumulation phenomena, while transboundary effects of potential oil spills from D6-Platform (Kaliningrad Region) can affect valuable fish provisioning areas and coastal cultural values in the Curonian Spit. Results were discussed for the relevance in MES assessment for marine spatial planning in small sea areas and the methodological outlook of the application of geospatial techniques on cumulative impacts assessment within this region of the Baltic Sea.