In order to avoid such negative effects, city should be designed as a coherent organism in terms of spatial, environmental, infrastructural, social and visual aspects.
We will extrapolate different scenarios of automated driving based on current technical, economic, infrastructural, spatial, and transport developments and discuss its consequences for the transport system and mobility concepts.
Based on these assumptions one could state that "a broad societal learning process is needed, with a focus on the system as a whole: its spatial characteristics, the infrastructural and technologic options, individual needs for mobility and access, cultural norms and institutions, as well as their mutual interdependence" [85, p. 1113].
A spatial aggregation of natural and infrastructural capital, and nightlights data, could be an accurate demand-side input for electrification supply-side models including grid-expansion and off-grid strategies.
The governance of complex processes, such as energy transitions, large infrastructural projects or comprehensive spatial planning issues, has hitherto largely been approached along two lines.
Besides the low accountability of these coalitions, we identify the potential problematic effects of infrastructure bundling including: spatial exclusions and fragmentation; the valorization of commercial space and automobility; and the limited participation of wider publics in shaping urban infrastructural futures.
The results showed that among the varying spatial arrangements of urban activities, building footprints and infrastructural elements characterized by monocentric centers of use inscribed with well-defined and relatively uniform grids of streets and related networks, alongside of relatively integrated zones of use, seemed to perform best with regard to urban intensity.
While the spatial expansion patterns are mostly influenced by irrigation and road infrastructure positioning and placement, they vary in their infrastructural costs and agricultural settlement clustering and connectivity, thus affecting landscape fragmentation, travel time, and social/ecological connectivity.
Therefore, sensor-based big data have enormous potential to gain new insights into and drive decisions about how sustainability can be better translated into the built, infrastructural, operational, and functional forms of smart sustainable cities across several spatial scales.
Below, we describe the natural and infrastructural capital datasets used in our analysis and provide a description of our exploratory spatial analysis and the development of an index that measures micro-enterprise development (MED) potential.
