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This paper expands upon a GA 50thAnniversary panel presentation made at the 2018 New Orleans American Association of Geographers meeting. It notes that retrospectives for this topic already exist, both in terms of the journal and the sub‐discipline. This paper presents discussion about the following selected prominent transitional themes bridging the past and the future of geographical analysis: space–time data; geographic uncertainty and error; and, dimensional reduction spatial statistical modeling. It also presents discussion about the following three themes that are expected to receive considerable future attention: negative spatial autocorrelation; synergies between spatial statistics and spatial optimization; and, big spatial data. The first two of these topics essentially have been ignored in the literature to date. [ABSTRACT FROM AUTHOR]

Moran's Index for spatial autocorrelation and localized index for spatial association have been widely applied in many research fields as the first step to explore and assess the spatial dependency in a set of geographic events. This article presents extensions to the equations for calculating global and localized spatial autocorrelation so to include the temporal attribute values of the geographic events being analyzed. The extended equations were successfully implemented and tested with a real world data set. In addition, simulated data sets were used to reveal how the extended equations performed. Beyond the usefulness of the extended equations, we suggest that care be taken with regard to assessing spatiotemporal patterns under the normality and randomization assumptions as different outcomes from different assumptions would require different approaches for interpretation. [ABSTRACT FROM AUTHOR]