Combining Fog and Cloud Computing to Support Spatial Analytics in Smart Cities

Spatial data generated by an Internet of Things (IoT) network is important to assist the spatial analytics process in issues related to smart cities. In these cities, IoT devices generate spatial data constantly. Thus, data can get increasingly voluminous very fast. In this paper, we investigate the challenge of managing these data through the use of a spatial data warehouse designed over a parallel and distributed data processing framework extended with a spatial analytics system. We propose an architecture aimed to assist a smart cities manager in the decision-making process. This architecture integrates a cloud layer where these technologies are located with a fog computing layer for extracting, transforming and loading the data into the spatial data warehouse. Furthermore, we introduce a set of guidelines to aid smart cities managers to implement the proposed architecture. These guidelines describe and discuss important issues that should be faced by the managers. We validate our architecture with a case study that uses real data collected by IoT devices in a smart city. This case study encompasses the execution of three different categories of spatial queries, demonstrating the architecture's efficacy and effectiveness to support spatial analytics in the context of smart cities.

Using GPS Data for Psychological Research: A Tutorial

The ubiquity of location data-enabled devices provides novel avenues for psychology researchers to incorporate spatial analytics into their studies. Spatial analytics use GPS data to assess and understand mobility behavior (e.g., locations visited, movement patterns). This tutorial provides a practical guide to using GPS data in R, introducing researchers to key procedures and resources for conducting spatial analytics. We show readers how to clean GPS data, compute mobility features (e.g., time spent at home, number of unique places visited), and visualize locations and movement patterns. In addition, we discuss the challenges of ensuring participant privacy and interpreting the psychological implications of mobility behaviors. The tutorial is accompanied by an R Markdown script and a simulated GPS dataset made available on the Open Science Framework.

Spatial Analytics Dashboards: Assisting Researchers to Select the Appropriate Tool

This review provides an overview of the following popular spatial analytics dashboard software: Tableau, PowerBI, ArcGIS Insights, ArcGIS Operations Dashboard and covers optimal use cases for each. Also included is a curriculum for an introductory one-shot instructional session on data literacy and research visualization using Operations Dashboard for ArcGIS in an academic library context.

Spatial analytics: the evolution of public administration from monitoring and control to forecasting and planning

Many of the traditional ideas that the state, thanks to space satellites, has an “all-seeing eye” to monitor field activities remain relevant to this day. However, along with this, remote sensing of the earth has functions that, according to the authors, should become an integral part of the routine practice of the state bodies of Kazakhstan. Remote sensing should be used not only in matters of monitoring and controlling economic activities, respecting the rule of law, and so on in the territory, but also forecasting and planning its development, assessing resources, opportunities and risks, including the prevention of natural and man-made emergencies, and even identifying social prerequisites tensions.