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Abstract Urban informatics appears to be a suitable area for the application of digital twins. Definitions of the term share some characteristics, but these definitions do not agree on what exactly constitutes a digital twin. The term has the potential to be misleading unless adequate attention is paid to the inherent uncertainty in any replica of a real system. The question of uncertainty is addressed, together with some of the issues that make its quantification problematic. Digital twins for urban informatics pose questions of purpose, governance, and ethics. In the final section the paper suggests some research issues that will need to be addressed if digital twins are to be successful. 

Abstract In 2020, Arizonans approved Proposition 207, the Smart and Safe Arizona Act, which legalized recreational marijuana sales. Previous research has typically used non‐spatial survey data to understand marijuana legalization voting patterns. However, voting behavior can, in part, be shaped by geographic context, or place, which is unaccounted for in aspatial survey data. We use multiscale geographically weighted regression to analyze how place shaped Proposition 207 voting behavior, independently of demographic variations across space. We find significant spatial variability in the sensitivity of voting for Proposition 207 to changes in several of the predictor variables of opposition and support for recreational marijuana legalization. We argue that local statistical modeling approaches provide a more in‐depth understanding of ballot measure voting behavior than the current use of global models. Related ArticlesBranton, Regina, and Ronald J. McGauvran. 2018. “Mary Jane Rocks the Vote: The Impact of Climate Context on Support for Cannabis Initiatives.”Politics & Policy46(2): 209–32.https://doi.org/10.1111/polp.12248.Brekken, Katheryn C., and Vanessa M. Fenley. 2020. “Part of the Narrative: Generic News Frames in the U.S. Recreational Marijuana Policy Subsystem.”Politics & Policy49(1): 6–32.https://doi.org/10.1111/polp.12388.Fisk, Jonathan M., Joseph A. Vonasek, and Elvis Davis. 2018. “‘Pot'reneurial Politics: The Budgetary Highs and Lows of Recreational Marijuana Policy Innovation.”Politics & Policy46(2): 189–208.https://doi.org/10.1111/polp.12246. 

Abstract There is an on-going challenge to describe, analyse and visualise the actual and potential extent of human spatial behaviour. The concept of an activity space has been used to examine how people interact with their environment and how the actual or potential spatial extent of individual spatial behaviour can be defined. In this paper, we introduce a new method for measuring activity spaces. We first focus on the definitions and the applications of activity space measures, identifying their respective limitations. We then present our new method, which is based on the theoretical concept of significant locations, that is, places where people spent most of their time. We identify locations of significant places from GPS trajectories and define the activity space of an individual as a set of the first three significant places forming a so-called “activity triangle”. Our new method links the distances travelled for different activities to whether or not people group their activities, which is not possible using existing methods of measuring activity spaces. We test our method on data from a GPS-based travel survey across three towns is Scotland and look at the variations in size and shape of the designed activity triangle among people of different age and gender. We also compare our activity triangle with five other activity spaces and conclude by providing possible routes for improvement of activity space measures when using real human movement data (GPS data). 

The concept of scale is inherent to, and consequential for, the modeling of geographical processes. However, scale also causes huge problems because the results of many types of spatial analysis appear to be dependent on the scale of the units for which data are reported (measurement scale). Consequently, when the same spatial models are calibrated at different scales of aggregations, the results are often vastly different (the well-known Modifiable Areal Unit Problem or MAUP). With the advent of local models and the fundamental difference in their scale of application compared to global models, this issue is further exacerbated in unexpected ways. For example, a global model and local model calibrated using data measured at the same aggregation scale can also result in different and sometimes contradictory inferences (the classic Simpson's Paradox). Here we provide a geographical perspective on why and how contrasting inferences might result from the calibration of a local and global model using the same data. Further, we examine the viability of such an occurrence using a synthetic experiment and two empirical examples. Finally, we discuss how such a perspective might inform the analyst’s conundrum: when the respective inferences run counter to one another, do we believe the local or global model results?