Geospatial Analytics Research Laboratory

About the lab

The Geospatial Analytics Laboratory at the Cyprus University of Technology, conducts both theoretical and applied research on geostatistics, spatial analysis, and geoinformatics, focusing on geographic information science and systems, as well as relevant applications in earth and environmental sciences. GeoSpAL members have applied their expertise in the past to a wide spectrum of subject areas, such as GIS, remote sensing, hydrology and water resources, environmental monitoring, climatology and atmospheric sciences, mineral resources estimation, reservoir characterization, soil science, public health, ecological (bio)diversity and archaeology.

Featured projects (1)

SaRoCy responds to the cutting-edge, frontier research requirement of the “Excellence Hubs” Programme by seeking to create new knowledge on a topic that has recently attracted global archaeological attention within the broader context of island and coastal archaeology. Project also aspires to offer novel insights based on physical/environmental modelling and computer simulation into the possible prehistoric maritime pathways between Cyprus and other Eastern Mediterranean coastal regions at the boundary between Terminal Pleistocene – early Holocene (Epipaleolithic / early Neolithic), a critical period for understanding the origins of the early visitors in Cyprus in connection with the Neolithic transition. The SaRoCy project proposal was ranked 2nd (score 14.52 / 15) out of a total of 65 proposals submitted to the “Social and Humanities” scientific area of the specific call. The project will run for 24 months with an overall budget of €150k, mainly allocated to young researchers participating in the project. Website: This project has received funding from the “Excellence Hubs” Programme within the “RESTART 2016-2020” funding framework for Research, Technological Development and Innovation (RTDI) administered by Cyprus’s Research & Innovation Foundation under Grant Agreement No EXCELLENCE/0918/0143.

Featured research (9)

Offshore wind offers an excellent opportunity for domestic renewable energy production with a vast potential for future energy systems. Offshore wind resource assessment, however, can be challenging. Remote sensing data e.g., Synthetic Aperture Radar (SAR), provide high spatial resolution detailed information on the spatial variability of offshore wind and have been used for wind resource assessment, as well as for the long-term validation of wind speed estimates from other sources (e.g. Numerical Weather Prediction models). This paper focuses on the evaluation of a 26-month time-series of Sentinel-1 SAR Level 2 OCN products for wind resource assessment in the offshore areas around Cyprus. Sentinel data were evaluated against a 10-year regional reanalysis dataset (UERRA) time-series and wind measurements from 5 coastal meteorological stations in Cyprus. Comparison revealed an overall agreement between the fitted stations and Sentinel Weibull distributions while discrepancies exist between the two data sources and UERRA. Bias observed between Sentinel and UERRA Weibull-derived statistics appears to be spatially dependent. Preliminary wind power assessment results indicate a significant wind power potential for the southwestern offshore areas of Cyprus, surpassing 400 W/m2 on average, offering thus economically viable solutions in terms of a future offshore wind power project development.
Regional offshore wind assessment studies typically rely on forecasts from Numerical Weather Prediction (NWP) models. NWP products are typically available at fine temporal resolutions (e.g., on an hourly basis) but relatively coarse spatial resolutions (e.g., on the order of several kilometers) to be used directly for more detailed local assessments. Satellite data, e.g., SAR (Synthetic Aperture Radar) data, on the other hand have been widely used in the literature to reveal high spatial resolution wind fields along with their variations but are available only at a few instances within a month’s period. The C-Band SAR instrument onboard the Sentinel-1 platform, in particular, provides wind speed data at 10 m above sea surface with a repeat frequency of 6 days since 2016.Statistical downscaling techniques are often employed to obtain finer spatial resolution products from coarse NWP products for use in finely resolved impact assessment studies. This study investigates the application of a novel geostatistical approach for downscaling Regional Reanalysis wind speed data using SAR data in order to spatially enhance information captured by the former. The data used comprise Sentinel-1A and 1B VV-polarized SAR wind field measurements and Uncertainties in Ensembles of Regional Reanalyses (UERRA) data, both bias-corrected using in-situ data from local meteorological coastalstations. The reference data used for bias correction are generated via spatial interpolation and aggregation (upscaling) of the local meteorological station wind speed values within the closest Sentinel pixel(1 km) and UERRA cell (11 km).Prior to the downscaling procedure, Weibull distribution models are fitted to the wind speed time-series both at the coarse and fine spatial resolutions. Downscaled UERRA Weibull distributions parameters (scale (a) and shape (b)) are then generated via Area-To-Point Kriging with External Drift (ATPKED), whereby Weibull parameter values are computed at a finer spatial resolution as a weighted linear combination of neighboring coarse resolution attribute values. The fine resolution parameters are used as auxiliary variables. ATPKED is mass preserving, in that the average of the downscaled Weibull parameter values within a coarse cell reproduce the bias-corrected UERRA value at that cell. Once the fine scale parameters are estimated, the wind speed distribution at the pixel level can be extracted. Statistical comparison indicated that more than half of the wind speed variability in Sentinel images can be explained by the contemporaneous downscaled estimates. Geostatistical simulation is also employed to assess the uncertaintyin the fine resolution values.As an illustration of the methodology, offshore wind speed values are estimated at a spatial resolution of 1km for the coastal areas of the Republic of Cyprus at a 6-hour interval over a period of 1 year. The results imply that the downscaled products could furnish a basis for a more spatially resolved offshore wind power assessment for the region, provided the above procedure is generalized for a longer time period.
Geography has long sought to explain spatial relationships between social and physical processes, including the spread of infectious diseases, within the context of modelling human-environment interactions. The spread of the recent COVID-19 pandemic, and its devastating effects on human activity and welfare, represent but examples of such complex human-environment interactions. In this paper, we discuss the value of agent-based models for simulating the spread of the COVID-19 virus to support decision-making with regards to non-pharmaceutical interventions, e.g., lock-down. We also develop a prototype agent-based model using a minimal set of rules regarding patterns of human mobility within a hypothetical town, and couple that with an epidemiological model of infectious disease spread. The coupled model is used to: (a) create synthetic trajectories corresponding to daily and weekly activities postulated between a set of predefined points of interest (e.g., home, work), and (b) simulate new infections at contact points and their subsequent effects on the spread of the disease. We finally use the model simulations as a means of evaluating decisions regarding the number and type of activities to be limited during a planned lockdown in a COVID-19 pandemic context.
This research poster's core subject is Human Security and the need for the empowerment and resilience of individuals. In that direction, we propose the development of the GeoSpatioTemporal Profile (GeoSTeP) assessment tool. The GeoSTeP methodology is based on: the INFORM Risk Index – a multi-layered and structured model, measuring 54 different indicators only at the country or community level and the Sendai Framework for Disaster Risk Reduction -GeoSTEP satisfies all of the four priority areas and 4 of the 13 guiding principles of the Sendai Framework The GeoSTeP model consists of 3 components, each one comprising two categories. The tool uses 28 indicators for estimating the risk level of individuals. For each of the 28 indicators, users receive a score based on their current location and answers (on a 10-point scale) provided to ArcGIS Survey123 forms. In practice, the tool is designed to be a system aiming to collect a set of geo- "snapshots" capturing individual unique and dynamic personal data, such as position, environmental conditions, proximity to critical infrastructures, users' personal socio-economic data, etc. It is developing by using ESRI's ArcGIS Survey123, a form-centric solution. The GeoSTeP tool is a preliminary effort to estimate the risk level of individuals, allowing the promotion and enhancement of individual security mentality, resilience, and civil preparedness in a tailored way, as we focus on the unique geo – Identity of each user.

Lab head

Phaedon Kyriakidis
  • Department of Civil Engineering and Geomatics
About Phaedon Kyriakidis
  • Phaedon Kyriakidis currently works at the Department of Civil Engineering and Geomatics, Cyprus University of Technology. Phaedon conducts research in Geostatistics and Spatial Analysis, Geoinformatics and GIS, and pursues relevant applications in earth and environmental sciences, atmospheric sciences, and more recently archaeology and cultural heritage.

Members (4)

George Leventis
  • Cyprus University of Technology
Stylianos Hadjipetrou
  • Cyprus University of Technology
Stelios Liodakis
  • University of the Aegean
Savvas Chrysoulidis
  • Cyprus University of Technology