Spatial social networks for the humanities: A visualization and analytical model

Abstract

In this article, we propose a model to identify the spatial and structural characteristics of a specific type of social networks: the family. The model is the third (Giordano & Cole, 2018, 2019-and the most fully realized-in a series we have developed in the last four years as part of our work on the topic of platial GIS, or the GIS of place. The model is both a visualization of a spatial social network and an analytical tool and it is also replicable and adaptable to projects in which the dataset Dunder study includes temporal and spatial information as well as demographic data. The case study we discuss in this article concerns the immediate post-Holocaust period in Budapest: in the summer of 1945, the Hungarian Section of the World Jewish Congress-American Joint Distribution Committee and the Jewish Agency for Palestine Statistical and Search Department (World Jewish Congress in brief) commissioned a door-to-door survey to determine where Jewish survivors were living in the city, which at the time was the largest single urban concentration of Jews in post-war Europe. The information collected in the survey

GIScience III: Questions of time

“…To place history is to enable specific temporal and spatial analytics. As Le Noc, Giordiano, and Cole (2020) suggest in their historical GIS that documents the spaces and times of Holocaust victims, the analysis of “spatial and temporal closeness” results when arrests were grouped by families in Italy (2020: 581, see also Giordiano et al, 2022 for a similar analysis in Hungary). For historians, this kind of spatial history is perhaps understood as “an approach to history,” where space is characterized as both “an object of study and a methodology (employed to learn more about a distinct object of study)” (Campbell 2018: e44).…”

Is your neighbor your friend? Scan methods for spatial social network hotspot detection

“…More broadly, adding a connectivity metric to typical point pattern hotspot detection helps disambiguate dense social-spatial networks from (disconnected) point clusters. In addition, this method results in scalar numbers at the node level, and thus, nodes can be ranked, compared, and symbolized by these values on a map, leading to more facile visualization of spatial social networks (SSNs) (an emergent issue in GIScience (Giordano et al, 2022;Sarkar & Yadav, 2021)). EdgeScan and NDScan methods are shared as a R package and an online tutorial (See Data Availability Statement).…”