Temporal reasoning is an important part of the field of time geography and spatio-temporal data science. Recent advances in qualitative temporal reasoning have developed a set of 74 relations that apply between discretized time intervals of at least two pixels each. While the identification of specific relations is important, the field of qualitative spatial and temporal reasoning relies on conceptual neighborhood graphs to address relational similarity. This similarity is paramount for generating essential decision support structures, notably reasonable aggregations of concepts into single terms and the determination of nearest neighbor queries. In this paper, conceptual neighborhood graphs of qualitative topological changes, with discretized temporal interval relations in the form of translation, isotropic scaling, and anisotropic scaling, are identified using data generated through a simulation protocol. The outputs of this protocol are compared to the extant literature regarding conceptual neighborhood graphs of the Allen interval algebra, demonstrating the theoretical accuracy of the work. This work supports the development of robust spatio-temporal artificial intelligence as well as the future development of spatio-temporal query systems upon the spatio-temporal stack data architecture.

The big data revolution transformed how we think of data analytics in many ways. Critical amongst them are the somewhat interconnected ideas of volunteered geographic information, crowdsourcing, and the big data property of variety. The robust literature concerning conceptual neighborhood graphs in two of these cases considers objects whose datatypes are held stable between the relations under consideration. This, however, is a limiting factor in these three application spaces due to the unknown form that data will take. This paper considers two avenues for the conceptual neighborhood graph to take as directions to address current complications facing reasoning tasks within a practically dirty world motivated by various sources of data: discretization conceptual neighborhood graphs (changing between corresponding vector and raster spaces) and cartographic generalization conceptual neighborhood graphs (changing the form of the objects in question). This paper provides insights as to what considerations should be considered when embarking upon this idea and demonstrates these concepts applied to prior conceptual neighborhood graphs.

The past thirty years of research have provided a host of advancements in the development of spatial relation sets and organizational structures by which we can consider a partial ordering of those sets. Multi-modal geofoundation models require knowledge of these types of sets and structures to provide cognitively plausible descriptive outputs when prompted by artificially intelligent tools. Conceptual neighborhood graphs are organizational structures that provide crucial insights into several relevant aspects of spatial data mining and spatial machine learning. In this chapter, we discuss mechanisms in which conceptual neighborhood graphs can provide insights that have not been leveraged in contemporary spatial information systems that provide opportunities for enhancing generative artificial intelligence both in sequential activities and in the translation of images and languages. We specifically discuss conceptual neighbors as event detection mechanisms within sequential image batches, pre-filtering mechanisms for relevant spatial data based on prepositional keywords, and spatial language translation between spoken and written languages.

There is a voluminous literature concerning the scope of topological relations that span spaces from R^1 to R^2, Z^2, S^1 and S^2, and T^2. In the case of the *^1 spaces, those relations have been considered both as conceptualizations of both spatial relations and also temporal relations. Missing from that list are the set of digital relations that exist within Z^1, representing discretized time or discretized ordered line segments. Discretized time plays an essential role in timeseries data and in spatio-temporal information systems and geo-foundation models where time is represented in layers of consecutive spatial rasters and/or spatial vector objects colloquially referred to as space-time cubes or spatio-temporal stacks. This paper explores the digital relations that exist in Z^1 interpreted as a regular topological space under the digital Jordan curve as well as a folded-over temporal interpretation of that space for use in spatio-temporal information systems and geo-foundation models. It identifies 34 9-intersection relations in Z^1, 42 9-intersection + margin relations in Z^1, and 74 temporal relations in Z^1, utilizing the 9+-intersection. This workcreates opportunities for better spatio-temporal reasoning capacity within spatio-temporal stacks and more direct interface with intuitive language concepts instrumental for effective utilization of spatial tools.

Crop switching, in which farmers grow a crop that is novel to a given field, can help agricultural systems adapt to changing environmental, cultural, and market forces. Yet while regional crop production trends receive significant attention, relatively little is known about the local-scale crop switching that underlies these macrotrends. We characterized local crop-switching patterns across the United States using the US Department of Agriculture (USDA) Cropland Data Layer, an annual time series of high resolution (30 m pixel size) remote-sensed cropland data from 2008 to 2022. We found that at multiple spatial scales, crop switching was most common in sparsely cultivated landscapes and in landscapes with high crop diversity, whereas it was low in homogeneous, highly agricultural areas such as the Midwestern corn belt, suggesting a number of potential social and economic mechanisms influencing farmers’ crop choices. Crop-switching rates were high overall, occurring on more than 6% of all US cropland in the average year. Applying a framework that classified crop switches based on their temporal novelty (crop introduction versus discontinuation), spatial novelty (locally divergent versus convergent switching), and categorical novelty (transformative versus incremental switching), we found distinct spatial patterns for these three novelty dimensions, indicating a dynamic and multifaceted set of cropping changes across US farms. Collectively, these results suggest that innovation through crop switching is playing out very differently in various parts of the country, with potentially significant implications for the resilience of agricultural systems to changes in climate and other systemic trends.

Geographical information science (GIScience) is progressively acknowledged as a scientific field based on a wide range of theories and methods that are constantly evolving. This motivates our attempt at a tentative observation of the research progress and challenges that have gone along with its gradual recognition as a domain of its own. The brief critical review presented in this paper develops an observation of such evolution. The peculiarity of our approach is that it is not based on a quantitative evaluation of the research outputs as identified by usual journal production metrics, but rather on a progressive identification of the research questions and their evolution, which the GIS academic community has been addressing over the past 30 years since the landmark NCGIA initiatives' research agendas have largely inspired and contributed to the development of geographical information science as a field.

This learning experience helps students gain experience and proficiency with issues regarding the ethical collection and use of data. Students will gain an appreciation for the risks associated with record-level identification, where data attributes, however innocently collected, can and have been used to violate privacy and lead to discrimination against individuals and protected classes of individuals.

Social change in any society entails changes in both behaviours and institutions. We model a group-structured society in which the transmission of individual behaviour occurs in parallel with the selection of group-level institutions. We consider a cooperative behaviour that generates collective benefits for groups but does not spread between individuals on its own. Groups exhibit institutions that increase the diffusion of the behaviour within the group, but also incur a group cost. Groups adopt institutions in proportion to their fitness. Finally, the behaviour may also spread globally. We find that behaviour and institutions can be mutually reinforcing. But the model also generates behavioural source-sink dynamics when behaviour generated in institutionalized groups spreads to non-institutionalized groups and boosts their fitness. Consequently, the global diffusion of group-beneficial behaviour creates a pattern of institutional free-riding that limits the evolution of group-beneficial institutions. Our model suggests that, in a group-structured society, large-scale beneficial social change can be best achieved when the relevant behaviour and institutions remain correlated.

Topological relations and direction relations represent two pieces of the qualitative spatial reasoning triumvirate. Researchers have previously attempted to use the direction relation matrix to derive a topological relation, finding that no single direction relation matrix can isolate a particular topological relation. In this paper, the technique of topological augmentation is applied to the same problem, identifying a unique topological relation in 28.6% of all topologically augmented direction relation matrices, and furthermore achieving a reduction in a further 40.4% of topologically augmented direction relation matrices when compared to their vanilla direction relation matrix counterpart.

Societies change through time, entailing changes in behaviors and institutions. We ask how social change occurs when behaviors and institutions are interdependent. We model a group-structured society in which the transmission of individual behavior occurs in parallel with the selection of group-level institutions. We consider a cooperative behavior that generates collective benefits for groups but does not spread between individuals on its own. Groups exhibit institutions that increase the diffusion of the behavior within the group, but also incur a group cost. Groups adopt institutions in proportion to their fitness. Finally, cooperative behavior may also spread globally. As expected, we find that cooperation and institutions are mutually reinforcing. But the model also generates behavioral source-sink dynamics when cooperation generated in institutional groups spreads to non-institutional groups, boosting their fitness. Consequently, the global diffusion of cooperation creates a pattern of institutional free-riding that limits the evolution of group-beneficial institutions. Our model suggests that, in a group-structured society, large-scale change in behavior and institutions (i.e. social change) can be best achieved when the two remain correlated, such as through the spread successful pilot programs.

Research examining homelessness in rural areas has been sparse. The current study aims to expand conceptions of rural homelessness by mapping community-level risk factors related to housing insecurity. Geographic information systems (GIS) techniques were used to map the distribution of select community-level risk indicators in the State of Maine. Three methodological choices related to this process are demonstrated: (1) selection and distribution of housing insecurity risk indicators; (2) use of location quotients; and (3) use of spatial lags. After examining and mapping selected risk factors against the location of homeless service supports, four areas in Maine were identified as communities of concern for housing insecurity. Better understanding the extent and location of areas of high need that are resource poor can help service and funding agencies to plan for the more efficient and effective distribution of homeless prevention and mitigation services. Implications for research in rural areas are discussed.

For the past several decades, political scientists have sought to understand the impact of legislative redistricting and gerrymandering on a variety of outcomes. However, traditional metrics such as compactness scores and newer metrics such as aggregated simulations impose very strong assumptions that make their use difficult. In this study, we propose a new graphical framework for analyzing districts that relaxes current assumptions while allowing analysts to focus on the choices that redistricting parties may potentially make. We then leverage the newest advances in district simulation algorithms to extend this framework to propose four new metrics. These new metrics are Edge-Cut Growth (ECG), Excess Edge (EE), and Edge per District Gain (EDG), and Internal Boundary Growth (IBG). These new metrics are then compared to several existing metrics, allowing us to test the attributes that our approach is similar to. In doing so, we demonstrate that the four new metrics are best seen as theoretical and technical advances on current metrics that focus on district geometry.

It has long been noted that electing members of the U.S. House of Representatives in single-member, mutually exclusive districts often leads to discrepancies between the partisanship of the electorate and the party distribution in the House. There has been a spirited debate in the literature about the extent to which this is due to demographic clustering or intentional gerrymandering. This study presents direct tests of both of these potential causes using a simulation-based approach that improves upon similar studies in the past. We find that while there is a significant amount of demographic clustering, redistricting procedures account for a much greater portion of the partisan bias in the House. Furthermore, our results indicate that, in the 113th Congress (2013-15), Republicans were overrepresented in the House by about 15 seats, of which only a few can be attributed to demographic clustering.

Topological relations are the predominant backbone of qualitative spatial reasoning, focusing to date mostly on objects that are embedded in R2. With the advent of ball-shaped screen technologies and the growing importance of globally situated data, the digital sphere, known as T2, becomes a viable embedding. While topological relations have been investigated for a spherical embedding S2 within a continuous framework, the lack of a full account of the relations in T2 prevents more refined query systems on global discrete data. This paper extends the concept of spherical topological relations to the digital spherical embedding, building on the model of a planar digital region in Z2 whose boundary comprises the pixels along the region's margin. With the 9-intersection and its detailed descendant, the 9+-intersection, 29 relations between two digital regions are derived. This set of binary digital spherical relations is jointly exhaustive and pairwise disjoint. The relations' conceptual neighborhood graph, capturing the similarity among the relations, further highlights that 18 of the 29 relations are refinements of those found in S2. These topological relations between digital regions coincide with the binary relations found between broad-boundary regions when the broad boundaries are of uniform thickness, a scenario preventing the inclusion of an entire interior of one region within the boundary of the other. Five different rationales for coarsening are investigated, each leading to a different group of the relations along the conceptual neighborhood graph.

Artificial intelligence and satellites have brought spatial-image interpretation to the forefront. Over the past two decades, the literature has provided three distinct methods for identifying and classifying the boundary of raster objects: the digital Jordan curve, the hyper-raster, and a marginal approach. While each approach has its own merits and drawbacks, there should be methods by which to convert one representation to another. This, however, is currently unattainable as the digital Jordan curve approach and the hyper-raster approach are formulated upon different domains of objects, with the more verbose digital Jordan curve approach serving as a subset of the hyper-raster approach with respect to objects which are simple regions in R2. This paper revisits the approach of the digital Jordan curve by removing the digital Jordan curve restriction, replacing it with the set of pixels that neighbor an exterior pixel, allowing for a host of different relations in pixel space. This increase in domain results in a set of 62 available 9-intersection matrices, a nearly four-fold increase from the original 16 matrices and demonstrates the novelty of this approach with respect to the egg-yolk relations, a model in R2 for relations between regions with broad boundaries.

The survival of Streptococcus equi subspecies equi and zooepidemicus in soiled equine bedding (SEB) and compost was evaluated. Dacron bags containing SEB were inoculated with 10(10) c.f.u. of each subspecies, and stored at 21 degrees C-23 degrees C for 24 hours, then placed in compost windrows containing SEB and feed waste (Experiments 1 and 2). Streptococci were quantified immediately after inoculation in the bags, and during the 336 hours after placement in the windrow. Next, SEB, autoclaved and nonautoclaved, was inoculated with 10(10) c.f.u. of each subspecies and sampled from 0 to 264 hours (Experiment 3). Finally, SEB was dried at 37 degrees C for 48 hours and sterile water added (0, 25, 50, 75, and 100 mL) to 5 subsamples of 100 g of dried bedding, which were inoculated with 10(10) c.f.u of each subspecies and sampled at intervals from 0 to 120 hours (Experiment 4). In Experiments 1 and 2, heavy Streptococcal growth was detected immediately after inoculation of Dacron bags, but no Streptococci were isolated 48 hours after placement in compost windrows. In Experiment 3, S. zooepidemicus was isolated from sterilized SEB up to 168 hours. In nonsterilized SEB, Streptococci were isolated up to 72 hours (P < .001). In Experiment 4, S. equi was isolated from dried SEB, with no added water, up to 120 hours, whereas in dried SEB, with added water, S. equi, was isolated up to 48 hours (P < .001). These data suggest that, depending on moisture, microbes in SEB may eliminate equine Streptococci. (C) 2018 Elsevier Inc. All rights reserved.

Equine endometritis continues to be a major cause of subfertility and infertility in mares, and recently, the subclinical form of endometritis has been in focus. The purposes of this study were to investigate the prevalence of subclinical infectious endometritis, defined as mares without clinical symptoms indicating endometritis, but with bacterial growth and positive cytology from an endometrial sample, and the relation to clinical parameters before and after artificial insemination (AI). A total of 76 Standardbred mares submitted for AI were included in the study, in which a uterine sample was collected before (biopsy) and after AI (swab) for bacterial culture, cytology, and histology. The results showed that 28.6% of clinically normal mares, submitted for breeding with AI, had subclinical infectious endometritis before breeding. The endometrial edema score early in estrus was a strong diagnostic indicator of a subclinical infection with Streptococcus equi subsp. zooepidemicus (odds ratio, 5.48; P < .0001). An altered endometrial edema pattern in a mare showing increased edema could therefore imply a subclinical uterine infection, and therefore, further examination of the mare should be conducted to rule out infectious endometritis. Sensitivity, specificity, positive predictive value, and negative predictive value of the cytology smear compared with the presence of polymorphonuclear neutrophils (PMNs) on histology were 0.78, 0.84, 0.72, and 0.87, respectively. A moderate agreement (k = 0.60) was found between the cytologic response of the smear and the presence of PMNs on histology.

The current state of the art for partition based qualitative spatial reasoning systems such as the 9-intersection, 9+-intersection, direction relation matrix, and peripheral direction relations is that of the binary set intersection — either empty or non-empty — conveying the intersection (or lack thereof) of an object in the sets deriving the partition. While such representations are sufficient for topological components of objects, these representations are not sufficient for various tasks in qualitative spatial reasoning (composition, representation transfer, converse, etc.) regarding partitions as tiles. Topological augmentation expands the current binary status quo into a system of assigning topological relations between objects and tiles. A case study is presented in the form of the direction relation matrix, demonstrating that an increased vocabulary has benefits for spatial information systems, providing localized context within a qualitative embedding.

The argument that U.S. House districts have become more demographically homogenous over time—either through intentional gerrymandering, geographic self-sorting, or some combination of the two— is a central component of most explanations of the growth of partisan polarization in Congress. In recent research, we developed a highly sophisticated method of creating simulated, randomized districts in all states with more than one member of the House (see Powell, Clark, and Dube 2015, 2016). In this paper we build on that prior research to examine the extent to which geographic self-sorting may or may not account for trends in district homogeneity. To do so, we compare the homogeneity of actual districts as drawn to repeated iterations of randomized, simulated districts within each state. We extend this analysis to examine changes in the margins of victory in these districts. Thus, this paper presents a thorough examination of the hypothesis that geographic self-sorting (or intentional gerrymandering) is creating safer House seats and a more polarized legislative environment in Congress.