When the primary objective of QSD is to inform technology deployment, spatial analysis is often used to account for site-specific parameters that can influence the sustainability of systems (Figure ). At its core, spatial analysis can be considered as a special category of scenario analysis where each deployment site is a scenario, and the implications of deployment site are reflected through the values of contextual parameters that are site-specific. To facilitate spatial analysis, geospatial data that link locality-specific contextual parameters with location information are collected, and they can be further combined with temporal information to reveal the evolution of attributes over time. These geospatial data may include physical information (e.g., geological properties, 285,303,304 distances, 305,306 existing infrastructure ), policies, 304 cultural preferences, 291 and any other contextual parameters that could serve as QSD inputs (e.g., energy and water unit impacts, 307,308 costs and impact characterization factors 309 ). As contextual parameters can be particularly important for the human health and social dimensions of sustainability that are highly site-specific, measures should be taken to ensure the representativeness of these values (e.g., use high stakeholder input and influence methods discussed in Section 4.4). To address spatial considerations, GIS is often used to capture, organize, calculate, and integrate multiple layers of spatial information into QSD (e.g., assessing spatial co-location of recoverable nutrients and agricultural demands for deployment of nutrient recovery sanitation systems ). Further, as uncertainties in these data (e.g., from different resolutions 312 ) can directly affect the conclusions of sustainability analyses, uncertainty analysis is often incorporated in spatial analysis and tailored to the data (e.g., based on the data structure 313 ).