Our cities can no longer afford to be at war with nature: they need to rapidly become places where people and nature co-exist and thrive. Fortunately, there is growing recognition that nature-based solutions to cities' various challenges offer far wider benefits than traditional engineered'grey' solutions: including improving resilience, better health for its citizens, and a faster path to net zero. In our recent report with the World Economic Forum, BiodiverCities by 2030: Transforming Cities' Relationship with Nature, we highlighted that nature-based solutions are on average 50% more cost-effective than purely man-made alternatives, and deliver 28% more added value in both direct and environmental benefits. But what will wean us off our addiction to'grey' traditional concrete solutions, and move us towards approaches that better regenerate nature and reduce carbon? I believe that the innovation and fresh opportunities that come from using advanced digital tools can provide the answer.

This paper addresses the interesting problem of processing and analyzing data in geographic information systems (GIS) to achieve a clear perspective on urban sprawl. The term urban sprawl refers to overgrowth and expansion of low-density areas with issues such as car dependency and segregation between residential versus commercial use. Sprawl has impacts on the environment and public health. In our work, spatiotemporal features related to real GIS data on urban sprawl such as population growth and demographics are mined to discover knowledge for decision support. We adapt data mining algorithms, Apriori for association rule mining and J4.8 for decision tree classification to geospatial analysis, deploying the ArcGIS tool for mapping. Knowledge discovered by mining this spatiotemporal data is used to implement a prototype spatial decision support system (SDSS). This SDSS predicts whether urban sprawl is likely to occur. Further, it estimates the values of pertinent variables to understand how the variables impact each other. The SDSS can help decision-makers identify problems and create solutions for avoiding future sprawl occurrence and conducting urban planning where sprawl already occurs, thus aiding sustainable development. This work falls in the broad realm of geospatial intelligence and sets the stage for designing a large scale SDSS to process big data in complex environments, which constitutes part of our future work.

Self-driving cars will change how we live, in all sorts of ways. But they won't just affect us humans – the coming revolution in autonomous transport has significant implications for wildlife as well. Nature conservationists and planners need to think hard about the impact of driverless vehicles, most notably in terms of renewed urban sprawl. Driverless cars could mean more people will commute from further afield, researchers say - and could be bad news for green areas. Warn the technology could spark a huge rise in urbanisation, and city planners must be aware of the issues.