Geospatial Hub​

Collection and analysis of various project-related data sources such as Global Horizontal Irradiance (GHI), wind speed, land use, areas of environmental protection, excluded areas, Digital Elevation Model (DEM), aspect maps, building locations, road network, and transmission network.​

Calculation of metrics related to Levelized Cost of Energy (LCOE) - wind and solar power.​

Development of a GIS Portal including technical set-up, data research and gathering, analytical components, and visualisation part.​

Utilisation of various types of spatial and demographic data (population density, population coverage by fuel station, traffic coverage by fuel station, and capacity per fuel stations) to perform high-quality analyses and to boost model performance.​

Spatial scoring model to estimate the potential for new stations openings and optimising existing footprints. The model provided indications on market saturation and optimisation potential, including identification of underserved/overserved areas.​

Data visualisation by utilising geospatial and mapping techniques.​

Utilising calculated estimated values of average waste generation per person and total waste generated in each region. Aggregation and data analytics in H3 grid format. ​

Development of interactive GIS mapping tool, empowering users to effortlessly display, filter, style, and modify analysis outcomes.​

Project helped Client to better understand waste generation patterns that will benefit in future data-driven decisions for sustainable waste management practices.​

Defining key technologies as well as developing key Data Management requirements including Data Integration Diagram, Data Migration, and Data Loss Mitigation Plans.​

Development of Geospatial Data Model, including building Data Catalogue, Logical Data Model with Data Management / Data Governance components definition and description.​

Development of high-level architecture of Geospatial Data Management tools and Geospatial Roadmap.​

Key deliverables included analysing existing GIS data for accuracy and relevance, identifying data discrepancies through a gap analysis, and creating geospatial standards (conceptual and logical data model) compliant with International Organization for Standardization (ISO), Model for Underground Data Definition and Integration (MUDDI), and General Authority for Survey and Geospatial Information (GEOSA) guidelines.​

A robust quality framework was also established in a form of ISO compliant Policy and Procedures on Completeness, Logical Consistency, and Positional Accuracy.​

The project employed a two-tiered scoring approach, starting with a macro-level evaluation to eliminate less favourable countries cost wise, followed by a detailed micro-level analysis at the Nomenclature of territorial units for statistics (NUTS) 3 regional level.​

This methodical approach ensured a thorough assessment of potential sites, enabling the identification of an optimal location that balances operational efficiency, cost-effectiveness, and strategic market positioning.​

Development of a digital area model with topographic and market layers, along with a location model to identify optimal sites based on accessibility and spatial criteria.​

Preparation of a financial model and an Initiatives Matrix, including forecasts based on benchmarks and costs, profitability assessments, and parameterised land-use options with recommendations.​

Creation of a location potential model, considering demographics, transportation, tax, education, cost of living, skill supply, compensation, industry/occupation, and property data.​

Preparing the heat map of location potential of addresses in the United States and selection of the location for the best property.​

Introduction of components such as change detection, construction monitoring, 3D building view, street navigation, traffic flow simulation, and more.​

Comprehensive support across a range of geospatial domains, from data acquisition to application and implementation.​

Digital Twin web application development and Satellite Imagery Change Detection.​

Providing a geospatial database and mapping platform about public art relating to sculpture, extreme land art, digital lighting, and typologies of locations. These include urban, remote destination, aquatic, and aerial locations.​

Creation of additional data standards and procedures for data collection and quality control to ensure the right quality of the data.​

The scope of work included visiting all properties within a municipal area to verify the use categories of properties. This involved comparing uses and the rights associated with specific properties, as well as appropriate zoning​.

Linking cadastral data to the survey and making certain fields auto-populate when the fieldworkers arrive at the designated properties to allow for seamless integration with broader datasets, and provide a level of accuracy for the fieldworkers.​

Delivery of a mobile application for surveying and easy access to the collected data via the website​

Supporting our client in ESG reporting by utilising geospatial tools to assess the environmental impact on biodiversity of their office and factory locations in CEE.​

By mapping endangered species, protected areas, water reservoirs, and noise levels, the initiative provided a comprehensive environmental analysis of each site and facility.​

Support to a retail store provider in assessing climate risks by leveraging CMIP6 climate models to evaluate the potential impacts of climate change on their operations across Europe and Asia​.

The initiative focused on defining critical variables, developing a robust risk assessment model, and setting thresholds to identify and mitigate risks associated with climate change.​

The analysis was performed in short, medium, and long term, and supported ESRS E1: Climate Change reporting.​

Outcome was a high-level analysis on approaching climate change in Turkmenistan.​

The analysis was intended to contribute to the ongoing efforts to address climate challenges and preparation for the United Nations Climate Change Conference (COP26).​

We helped our client to analyse ambulance routes to optimise the location of new staging areas​.

Our Team cleaned and analysed our client’s accident data to pinpoint high-frequency zones​.

Then, considering factors like demographics, flood/earthquake risks, and response times, they built a network model to identify optimal locations for new ambulance bases​.

We supported the strategy team in the project delivery of spatial analysis to identify populations in buffers of 20 and 40 kilometres from hemodynamic labs in Poland​.

Analysis identified potential underserved areas for hemodynamic laboratories​.

PwC Geospatial HUB, in cooperation with the PwC HealthCare team, supported one of the Ministries in Albania during the assessment of the existing hospital master plan​.

Thanks to geospatial insights, we prepared our evaluation of public hospitals’ operations as well as their readiness to cope with the current medical needs of the Albanian population​.

The construction of the regression model, predicting the client's default, was carried out using internal data provided by the bank and external data prepared by PwC.​