High Resolution Köppen‐Geiger Classifications of Paleoclimate Simulations

The development and application of an algorithm to compute Köppen‐Geiger climate classifications from the Coupled Model Intercomparison Project (CMIP) and Paleo Model Intercomparison Project (PMIP) climate model simulation data is described in this study. The classification algorithm was applied to data from the PMIP III paleoclimate experiments for the Last Glacial Maximum, 21k years before present (yBP), Mid‐Holocene (6k yBP) and the Pre‐Industrial (0k yBP, control run) time slices. To infer detailed classification maps, the simulation datasets were interpolated to a higher resolution. The classification method presented is based on the application of Open Source Software, and the implementation is described with attention to detail. The source code and the exact input data sets as well as the resulting data sets are provided to enable the application of the presented approach.     

Small-world characteristics on transportation networks: a perspective from network autocorrelation

Studies on small-world networks have received intensive interdisciplinary attention during the past several years. It is well-known among researchers that a small-world network is often characterized by high connectivity and clustering, but so far there exist few effective approaches to evaluate small-world properties, especially for spatial networks. This paper proposes a method to examine the small-world properties of spatial networks from the perspective of network autocorrelation. Two network autocorrelation statistics, Moran’s I and Getis–Ord’s G, are used to monitor the structural properties of networks in a process of “rewiring” networks from a regular to a random network. We discovered that Moran’s I and Getis–Ord’s G tend to converge and have relatively low values when properties of small-world networks emerge. Three transportation networks at the national, metropolitan, and intra-city levels are analyzed using this approach. It is found that spatial networks at these three scales possess small-world properties when the correlation lag distances reach certain thresholds, implying that the manifestation of small-world phenomena result from the interplay between the network structure and the dynamics taking place on the network.      

Using the Newly‐created ILE DBMS to Better Represent Temporal and Historical GIS Data

This article introduces a type of DBMS called the Intentionally‐Linked Entities (ILE) DBMS for use as the basis for temporal and historical Geographical Information Systems. ILE represents each entity in a database only once, thereby mostly eliminating redundancy and fragmentation, two major problems in Relational and other database systems. These advantages of ILE are realized by using relationship objects and pointers to implement all of the relationships among data entities in a native fashion using dynamically‐allocated linked data structures. ILE can be considered to be a modern and extended implementation of the E/R data model. ILE also facilitates storage of things that are more faithful to the historical records, such as gazetteer entries of places with imprecisely known or unknown locations. This is difficult in Relational database systems but is a routine task using ILE because ILE is implemented using modern memory allocation techniques. We use the China Historical GIS (CHGIS) and other databases to illustrate the advantages of ILE. This is accomplished by modeling these databases in ILE and comparing them to the existing Relational implementations.     

Important considerations about space‐time data: Modeling,scrutiny, and ratification

Space‐time data are becoming more abundant as time goes by, with hands‐on interest in them becoming more prevalent. These data have a very sensitive ordering in space and time, one that the simplest of recording errors can scramble. These data are also complex, containing both spatial and temporal autocorrelation coupled with their interaction. One goal of many researchers is to disentangle and account for these autocorrelation components in a parsimonious way. This article presents three competing model specifications to achieve this end. In addition, it outlines seven best practices for vetting space‐time datasets. This article cites a publicly available corrupt (containing at least errors of omission) rabies dataset to illustrate how a large volume of potentially valuable data can be rendered meaningless. In addition, it exemplifies postulated contentions about the United States National Cancer Institute Surveillance, Epidemiology, and End Results Program’s 1969–2018 population‐by‐county dataset, a collection of population counts held in high esteem. One major empirical finding is that this particular dataset exhibits traits that may merit remedial revisions action. A key conceptual finding is a suggested set of best practices for space‐time data proofreading. These two findings contribute to an ultimate goal of a large collection of certified open access space‐time datasets supporting repeatable and replicable scientific analyses.     

Student Learning in an Introductory GIS Course: Using a Project‐Based Approach

Introductory courses in Geographic Information Science (GIS) expose students to the concepts and practices necessary for future academic and professional use of GIS tools. Traditional GIS courses balance lectures in the theories of GIS with pre‐built and pre‐packaged lab activities to learn the practices of GIS. This article presents a case study of an experimental introductory course in which students conducted novel, independent project‐based group research under the supervision of graduate or advanced undergraduate students enrolled in the course, culminating in a class presentation and publication quality paper. Surveys and interviews indicated that students reacted more positively to project‐based group research than to traditional activities. Students felt the projects better prepared them for ‘real world’ applications of GIS, and recommend project‐based group research in other GIS coursework. Additionally, our findings indicate that students appreciate interactions with peers of varying skill levels and experiences, as these broaden their capabilities to work with other GIS users.