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This study explores the intersection of spatial co-location patterns and social phenomena through an innovative analysis of Twitter data, addressing a gap in existing spatial co-location research that predominantly focuses on geographical phenomena. Spatial co-location pattern analysis is fundamental to understanding spatial data and enhancing geographic context-awareness in applications. While traditional studies have concentrated on identifying spatial proximity of physical features to discern interactions among geographical phenomena, this research integrates social phenomena, acknowledging the intrinsic relationship between geographic and social dynamics. Through the analysis of georeferenced Twitter data, this study identifies spatial features associated with social interactions and activities, providing a comprehensive understanding of social-spatial interplay. The research introduces an innovative Semantic Co-Location (SCL) model to analyze spatial co-location patterns from individual tweets at aggregated spatial levels. This includes developing spatial co-location mining techniques, analyzing topical categories of spatial co-location based on contextual information, and uncovering previously unknown patterns that expand current research boundaries. The findings advance our understanding of urban discourse and illuminate the relationship between place and people, specifically within spatial and social networks.

The susceptibility of cloud droplet number concentration Nd $\\left({N}_{\\mathrm{d}}\\right)$ to aerosols (β) $(\\beta )$ remains challenging to constrain in satellite observations. This difficulty arises from limitations in representing cloud condensation nuclei, which depend on aerosol size and composition. To address this, we combine aerosol‐type‐specific retrievals of dry extinction coefficient and number concentration from Cloud–Aerosol LiDAR and Infrared Pathfinder Satellite Observation with co‐located Nd ${N}_{\\mathrm{d}}$ from CloudSat and Moderate Resolution Imaging Spectroradiometer. We find that β $\\beta $ associated with mineral dust is consistently near zero across all aerosol‐Nd ${N}_{\\mathrm{d}}$ combinations. Furthermore, β $\\beta $ decreases nonlinearly as the dust fraction increases, with a pronounced reduction occurring only when dust exceeds approximately 70%. Accordingly, excluding dust from the analysis increases the globally aggregated β $\\beta $ from 0.24–0.26 to 0.30–0.37. These findings highlight the importance of considering aerosol composition when constraining aerosol–cloud interactions and their associated radiative forcing in satellite observations.

Spatial co-location pattern discovery (SCPD), a kind of knowledge discovery process, aims at discovering potentially unknown co-location patterns (co-locations). Co-locations have been widely used in many aspects, including life services, ecological environment, business research, etc. Many methods have been proposed to discover co-locations. However, these methods only discovered co-locations consisting of fine-grain spatial features, since the user knowledge is ignored, many interesting and general patterns are still undiscovered. Meanwhile, co-locations that are discovered by current frameworks are quantity-numerous and independent; thus, their usefulness is strongly limited. To overcome these shortcomings, this paper introduces the user knowledge into the process of SCPD, to discover general and intrinsic co-locations and help users quickly find their interested patterns. First, a framework OCPM (Co-location Pattern Miner using Ontology) is proposed, where an ontology is employed to integrate user knowledge to guide the process of SCPD. Second, a new co-location consisting of ontology concepts is proposed. Under the guidance of the ontology, we propose the prevalent semantic multi-level co-locations (PSMCs) consisting of ontology concepts to represent richer knowledge. Third, we design two different ways, i.e., the Apriori-like and clique-based ways, to meet the requirements of OCPM and propose a novel clique-based algorithm named IDG to discover PSMCs. Meanwhile, a top-down search strategy is proposed to help users quickly find interesting knowledge via the ontology. Finally, we validate OCPM and IDG on both real and synthetic datasets, respectively, the experimental results demonstrate their effectiveness.

From spatial data sets, identifying groups of spatial features whose utility participation index (UPI) values are larger than a user-specified utility threshold is called high utility co-location pattern (HUCP) mining that can reveal valuable information and has been applied in many fields. Since UPI does not hold the downward closure property, almost current mining HUCP algorithms design some pruning strategies to remove unnecessary candidates in advance to improve mining performance. However, these algorithms are still powerless when dealing with large and dense data. Moreover, to avoid missing interesting HUCPs, the utility threshold should be set as small as possible. Unfortunately, this setting leads to many HUCPs generated that disturb users’ understanding and requires expensive execution resources. To address these, first, a top-down HUCP mining framework is proposed. Neighboring instances are divided into maximal cliques (MCs), and then, these MCs are organized in a compact hash table structure. The longest keys in the structure correspond to the longest candidates, and the mining process starts with these candidates and performs in a top-down search style. Second, a concise representation, ϵ-closed HUCPs, is proposed that can effectively compress similar patterns. Finally, comprehensive experiments on diverse data show that the proposed method is effective and efficient.

It is usually difficult for prevalent negative co-location patterns to be mined and calculated. This paper proposes a join-based prevalent negative co-location mining algorithm, which can quickly and effectively mine all the prevalent negative co-location patterns in spatial data. Firstly, this paper verifies the monotonic nondecreasing property of the negative co-location participation index (PI) value as the size increases. Secondly, using this property, it is deduced that any prevalent negative co-location pattern with size n can be generated by connecting prevalent co-location with size 2 and with an n − 1 size candidate negative co-location pattern or an n − 1 size prevalent positive co-location pattern. Finally, the experiment results demonstrate that while other conditions are fixed, the proposed algorithm has an excellent efficiency level. The algorithm can eliminate the 90% useless negative co-location pattern maximumly and eliminate the useless 40% negative co-location pattern averagely.

The literature has highlighted that the propensity of MNEs to co-locate offshore R&D labs with their production plants can vary substantially according to firm and industry characteristics. In this paper, we apply a novel two-stage estimation procedure that allows us to tease out this heterogenous behavior and investigate the factors that are associated with a higher propensity to colocate production and R&D activities abroad. Using data on 1483 greenfield international investments in R&D activities made by 855 firms in 587 cities worldwide, we uncover that the strength of the co-location effect is indeed highly heterogenous across firms. In particular, it is higher among firms with less international experience and geographical dispersion of international activities, as well as with a lower share of intangible assets. These results are consistent with the idea that co-location is a substitute for firms’ ability to coordinate complex and dispersed organizational structures, and that firms relying relatively less on codified knowledge can use co-location of offshore R&D and production to facilitate knowledge transfer across activities.

Rapid technological developments make firms favor the creation of new approaches to technology management. Startups can offer large firms access to new technologies and the emphasis on corporate-startup collaboration has therefore reached a new level. Many models exist and co-location is one of these. While co-location in the context of clusters and innovation systems has been studied in previous literature, research on corporate-startup co-location is very limited. The purpose of this paper is to investigate the broader phenomenon of business co-location and based on this review, suggest a framework and metrics to evaluate the effects of corporate-startup co-location. The paper originates from earlier conducted studies on corporate-startup collaboration models. For this paper a literature review on the broader phenomenon of business co-location is conducted. The theoretical contribution is a proposed multi-stakeholder framework and metrics for evaluating the effects of corporate-startup co-location.

Buffer analysis is a common tool of spatial analysis, which deals with the problem of proximity in GIS. Buffer analysis researches the relationship between the center object and other objects around a certain distance. Buffer analysis can make the complicated problem be more scientifically and visually, and provide valuable information for users. Over the past decades, people have done a lot of researches on buffer analysis. Along with the constantly improvement of spatial analysis accuracy needed by people, people hope that the results of spatial analysis can be more exactly express the actual situation. Due to the influence of some certain factors, the impact scope and contact range of a geographic elements on the surrounding objects are uncertain. As all we know, each object has its own characteristics and changing rules in the nature. They are both independent and relative to each other. However, almost all the generational algorithms of existing buffer analysis are based on fixed buffer distance, which do not consider the co-location relationship among instances. Consequently, it is a waste of resource to retrieve the useless information, and useful information is ignored.

The explosive growth of spatial data and the widespread use of spatial databases emphasize the need for spatial data mining. The subsets of features frequently located together in a geographic space are called spatial co-location patterns. It is difficult to discover co-location patterns because of the huge amount of data brought by the instances of spatial features. A large fraction of the computation time is devoted to generating row instances and candidate co-location patterns. This paper makes three main contributions for mining co-location patterns. First, the definition of maximal instances is given and a row instance (RI)-tree is constructed to find maximal instances from a spatial data set. Second, a fast method for generating all row instances and candidate co-locations is proposed and the feasibility of this method is proved. Third, a maximal instance algorithm with no join operations for mining co-location patterns is proposed. Finally, experimental evaluations using synthetic data sets and a real data set show that maximal instance algorithm is feasible and has better performance.

Plural forms exist when managers use two owners to perform one activity. Franchising is a plural form explained by agency theory, however, the theory is unable to explain two franchisor actions: 1) allowing franchisees to own multiple outlets and 2) co-locating company-owned and franchised outlets. We use research that describes a symbiosis between company-owned and franchised outlets to extend agency theory and explain these actions. Our investigation of ownership patterns among 4,339 outlets of 16 plural form franchisors is consistent with our theory that multi-outlet franchising is cost efficient and that co-location occurs when franchisors fill market gaps left by franchisees.