Journal of Geographical Systems最新文献

This paper proposes a new method of point cluster analysis. There are at least three important points that we need to consider in the evaluation of point clusters. The first is spatial inhomogeneity, i.e., the inhomogeneity of locations where points can be located. The second is aspatial inhomogeneity, which indicates the inhomogeneity of point characteristics. The third is an explicit representation of the geographic scale of analysis. This paper proposes a method that considers these points in a statistical framework. We develop two measures of point clusters: local and global. The former permits us to discuss the spatial variation in point clusters, while the latter indicates the global tendency of point clusters. To test the method’s validity, this paper applies it to the analysis of hypothetical and real datasets. The results supported the soundness of the proposed method.

Recent studies on green space exposure have argued that overlooking human mobility could lead to erroneous exposure estimates and their associated inequality. However, these studies are limited as they focused on single cities and did not investigate multiple cities, which could exhibit variations in people’s mobility patterns and the spatial distribution of green spaces. Moreover, previous studies focused mainly on large-sized cities while overlooking other areas, such as small-sized cities and rural neighborhoods. In other words, it remains unclear the potential spatial non-stationarity issues in estimating green space exposure inequality. To fill these significant research gaps, we utilized commute data of 31,862 people from Virginia, West Virginia, and Kentucky. The deep learning technique was used to extract green spaces from street-view images to estimate people’s home-based and mobility-based green exposure levels. The results showed that the overall inequality in exposure levels reduced when people’s mobility was considered compared to the inequality based on home-based exposure levels, implying the neighborhood effect averaging problem (NEAP). Correlation coefficients between individual exposure levels and their social vulnerability indices demonstrated mixed and complex patterns regarding neighborhood type and size, demonstrating the presence of spatial non-stationarity. Our results underscore the crucial role of mobility in exposure assessments and the spatial non-stationarity issue when evaluating exposure inequalities. The results imply that local-specific studies are urgently needed to develop local policies to alleviate inequality in exposure precisely.

Accessing massive datasets can be challenging for users unfamiliar with programming codes. Combining Konstanz Information Miner (KNIME) and MySQL tools on standard configuration equipment allows for addressing this issue. This research proposal aims to present a methodology that describes the necessary configuration steps in both tools and the required manipulation in KNIME to transmit the information to the MySQL environment for further processing in a database management system (DBMS). In addition, we propose a procedure so that the use of this point-and-click software in research work can gain in reproducibility and, therefore, in credibility in the scientific community. To achieve this, we will use a big database regarding patent applications as a reference, the PATSTAT Global 2023, provided by the European Patent Office (EPO). As well known, patent data can be a valuable source for understanding innovation dynamics and technological trends, whether for studies on companies, sectors, nations or even regions, at aggregated and disaggregated levels.

Many studies seek to study the relationship between socioeconomic factors and human mobility indicators. However, it is well documented that mobility levels are also driven by the geographical context where individual movement takes place. Here we test whether accounting for geographical context leads to new or different interpretations of human mobility behavior when studying associations with socioeconomic factors. Specifically, we define mobility deviation index as the relative level of observed mobility when compared to expected mobility for a specific location, where expected mobility accounts for geographical context. Our results highlight the significant role of context when interpreting spatial patterns of human mobility. We demonstrate that controlling for the effects of geographical context will substantially impact our interpretation of associations between measures of human mobility and socioeconomic variables. These results represent an important step in furthering our understanding of the role of place on human mobility patterns.

The passing of Professor Arthur Getis in May of 2022 initiated a number of events to both reflect on and remember the tremendous contributions he has made over his career to geographical systems more broadly, but also spatial analysis, spatial statistics, regional science, geography and GIScience, among others. This began with a series of sessions at the North American Regional Science Council meetings held in Montreal, Canada in November of 2022, followed by an invitation from the editors of Journal of Geographical Systems, Manfred Fischer, Antonio Paez, and Petra Staufer-Steinnocher, to organize a special issue in his honor. Soon thereafter, an open call was initiated to solicit submissions to this special issue, seeking original contributions that overlap and complement the broad range of research undertaken by Professor Getis over a distinguished career and life. This paper offers an overview of prominent geographical systems work carried out by Professor Getis. Reflections on his many contributions are also detailed. A summary of the contributions to this special issue is given, along with final thoughts.

This paper quantifies and graphically illustrates the distance decay effect and spatial reach of spillover effects derived from a spatial Durbin (SD) model with parameterized spatial weight matrices. Building on attributes of the concept of spatial autocorrelation developed by Arthur Getis, we adopt a distance-based negative exponential spatial weight matrix and parameterize it by a decay parameter that is different for each spatial lag in this model, both of the regressand and of all regressors. The quantification and illustration are applied to the spatially augmented neoclassical growth framework, which we estimate using data for 266 NUTS-2 regions in the EU over the period 2000–2018. We find distance decay parameters ranging from 0.233 to 2.224 and spatial reaches ranging from 700 to more than 1500 km for the different growth determinants in this model. These wide ranges highlight the restrictiveness of the conventional SD model based on one common spatial weight matrix for all spatial lags.

Spatial flows represent spatial interactions or movements. Mining colocation patterns of different types of flows may uncover the spatial dependences and associations among flows. Previous studies proposed a flow colocation pattern mining method and established a significance test under the null hypothesis of independence for the results. In fact, the definition of the null hypothesis is crucial in significance testing. Choosing an inappropriate null hypothesis may lead to misunderstandings about the spatial interactions between flows. In practice, the overall distribution patterns of different types of flows may be clustered. In these cases, the null hypothesis of independence will result in unconvincing results. Thus, considering the overall spatial pattern of flows, in this study, we changed the null hypothesis to random labeling to establish the statistical significance of flow colocation patterns. Furthermore, we compared and analyzed the impacts of different null hypotheses on flow colocation pattern mining through synthetic data tests with different preset patterns and situations. Additionally, we used empirical data from ride-hailing trips to show the practicality of the method.

In today's era, the address plays a crucial role as one of the key components that enable mobility in daily life. Address data are used by global map platforms and location-based services to pinpoint a geographically referenced location. Geocoding provided by online platforms is useful in the spatial tracking of reported cases and controls in the spatial analysis of infectious illnesses such as COVID-19. The first and most critical phase in the geocoding process is address matching. However, due to typographical errors, variations in abbreviations used, and incomplete or malformed addresses, the matching can seldom be performed with 100% accuracy. The purpose of this research is to examine the capabilities of machine learning classifiers that can be used to measure the consistency of address matching results produced by online geocoding services and to identify the best performing classifier. The performance of the seven machine learning classifiers was compared using several text similarity measures, which assess the match scores between the input address data and the services' output. The data utilized in the testing came from four distinct online geocoding services applied to 925 addresses in Türkiye. The findings from this study revealed that the Random Forest machine learning classifier was the most accurate in the address matching procedure. While the results of this study hold true for similar datasets in Türkiye, additional research is required to determine whether they apply to data in other countries.