Extracting useful information from sensors that record water distribution network (WDN) data is essential to improve network performance, increase network preparedness and resilience, and advance network digitalisation. Due to the large volume of data generated, analysis of the pressure head requires advanced techniques to reduce dimensionality. While previous works were typically based on comparing hydraulic simulations and observed data, there is a lack of study on pattern recognition, a helpful method for event detection, localisation, and prevention. Since the number of metering devices and their operativity has a crucial role in the recognition of key patterns, a spatial evaluation of network behaviour (with a focus on resilience) is conducted in this study. Comparing the heatmaps leads to extracting key patterns (i.e., landmarks), which will be helpful for decision-makers to increase the preparedness by making arrangements against critical events and allow classification and prediction of the network behaviour. This paper focuses on recognising the possible landmarks in the network representing a key feature (particularly pressure) in the presence and absence of leakage through spatial analysis with the objective of dimensionality reduction. A dataset of incidents, leakage/burst events, and ordinary network operations were captured through sensors and expert knowledge in a WDN in Spain to obtain relevant information (in the form of landmarks) from them. Results were promising, recognising the patterns that characterise the network behaviour when influenced by leakage/burst events.