In this paper, we present the algorithm aimed to segment the type of geographical space where points are a substantial component. The research problem falls within the mainstream of Automated Unit Design (AUD). The objective function of the solution is a balance between the size of segmented units expressed as an attribute of points datasets and its agreement with constraints provided by the geographic space. An algorithm has three free parameters; two of them allow one to control the objective function: the size of segmented units and allowable deviation from the size. The paper contains a case study where we show how our approach segment the geographic space of the City of Poznań.
The inadequacy of spatially explicit and accessible data portals continues to be a substantial barrier for policymakers and concerned authorities in the least developed countries. The purpose of this study is to determine the potentiality of night-time light (NTL) data to measure spatial road infrastructure development. The Day-Night Band (DNB) NTL data from the Visible Infrared Imaging Radiometer Suite (VIIRS) as well as Google Maps highways road data (RD) were used in this research. In order to analyze the correlation between VIIRS NTL and RD for two least developed countries, we performed the Chi-square test of independence, which revealed that the variables are dependent on one another. Following that, we computed the Cramer’s V test as a correlation coefficient to determine the strength of the association for both countries. Our findings revealed a correlation value of 0.334 in Bangladesh and a correlation value of 0.299 in Rwanda, demonstrating that VIIRS NTL and RD are strongly correlated. Following the discovery of a statistically significant correlation, we utilized the data to do more exploratory analysis.
Even if we are not aware, our emotions can influence and interplay with our navigation and use of mobile navigation aids. A given map display can make us feel good by reminding us of pleasant past experiences, or it can make us feel frustrated because we are not able to understand the information provided. Navigation aids could also make a given landmark emotionally charged, and thus more salient and memorable for a navigator, for example, by using an auditory narrative containing emotional cues. By storytelling, it would also be possible to provide details about a given landmark and connect proximal landmarks to each other. But how do navigational instructions in the form of emotional storytelling affect spatial memory and map use? Results from a preliminary study indicated that a video presentation viewed from a first person perspective is looked at more often than an abstract map, and this evidence becomes even stronger when instructions are emotionally laden. We discuss results in the context of place meaning and how emotions’ role in navigation should be further assessed, in particular to increase spatial learning from navigation aids.
Recently, major cities are facing air pollution problems mostly caused by individual car traffic. Besides the emission of greenhouse gases, particulate matter is a particular concern for public health. In order to mitigate these emission related issues, we developed an environmentally friendly routing approach, which calculates the most fuel-efficient route - based on the driving dynamics of the road, vehicle, and traffic characteristics. In addition, the calculated route is designed to avoid regions of high particulate matter concentration. In order to integrate real-time air quality data of moving and stationary sensors using OGC Sensor Observation Service. Cars are used as moving sensors in the city. The paper evaluates the effects of air quality (particulate matter & greenhouse gases) on the route calculation - so that cars/bikes may receive real-time recommendations to avoid polluted areas.
This study builds a bridge between the literatures for geographically weighted regression (GWR) and compositional data analysis (CoDA). GWR allows the modeling of spatial heterogeneity in regression models and is increasingly used in various fields. CoDA provides unique and useful tools for compositional data, which are restricted by a constant-sum constraint. Although compositional data are common in many scientific areas, it is not until recently that increasingly sophisticated statistical methods have been deeply investigated. Many types of spatial models based on geostatistics, spatial statistics, and spatial econometrics for compositional data have been proposed. However, there is less attention to both spatial heterogeneity and the constant-sum constraint. In this study, we propose a GWR model for compositional data. This allows us to model spatially varying relationships while considering the constant-sum constraint. We applied this model to analyze household income compositions at the county level in the US. The interpretational usefulness of the results of spatially varying compositional semi-elasticities is empirically performed.
Superpixels are a promising group of techniques allowing for generalization of spatial information. Among this group, the Simple Linear Iterative Clustering (SLIC) superpixels algorithm proved to be first-rate, both in terms of the quality of the output and the performance. SLIC, however, is limited to detecting homogeneous areas and uses the Euclidean distance only. Here, we propose an extension of SLIC allowing to use any specified distance measure for single or multi-layered spatial raster data. To present our idea, we use the extension to create an over-segmentation of areas with similar proportions of different land cover categories in Ohio. Given a proper distance measure, the proposed extension can also be used for other scenarios, including creating regions of similar temporal patterns or similarly ranked areas. Depending on the use case, the resulting superpixels could be either the result of the analysis or the input for further classification or clustering.
We identify automated landmark salience assessment in indoor environments as a problem related to pedestrian navigation systems that has not yet received much attention but is nevertheless of practical relevance. We therefore evaluate an approach based on visual information using images to capture the landmarks’ outward appearance. In this context we introduce the largest landmark image and salience value data set in the domain so far. We train various classifiers on domain agnostic visual features to predict the salience of landmarks. As a result, we are able to clarify the role of visual object features regarding perception of landmarks. Our results demonstrate that visual information has only limited expressiveness with respect to salience.
The urban real estate market is shaped by spatially varying environmental and social determinants, such as the valuation of green spaces, proximity to transport, and distance to central business districts. Among all the spatially varying relationships between prices and housing characteristics, some tend to vary at a global spatial scale, whereas others vary at a local spatial scale. This study applies a random model to specify multi-scale spatial heterogeneity in the rental housing market by utilizing residential rent data in the Tokyo metropolitan area from 2017. The results show that spatially varying determinants impact rental housing prices at the global, moderate, and local scales. Further, we find that the estimation is flexible because the random model determines the spatial scale of each regression coefficient.
Virtual reality technology has the potential to be a revolutionary addition to the field of Geographic Information Science. The application of virtual reality to GIScience has been discussed for decades, however adoption has been limited until recently. Virtual reality GIScience represents an interdiscip- linary approach, incorporating fields such as video game development. In this paper, we introduce Locative Reality, a virtual reality software that presents users with immersive 360° video experiences of forest environments. It incorporates spatial information into the virtual environment so that data generated by virtual research can be directly linked to real-world locations. The implications for the field of GIScience include virtual research tools and educational experiences, accessible to anyone anywhere in virtual reality.
Accurate and up-to-date land use maps are important to the study of human-environment interactions, urban morphology, environmental justice, etc. Traditional land use mapping approaches involve several surveys and expert knowledge of the region to be mapped. While traditional approaches generate accurate and authoritative maps, it is expensive and takes a long time to develop a new version of map. Besides, such maps have region-specific spatial embedding, making them difficult to benchmark and compare against other land use maps. This work introduces a scalable POI-based land use modeling approach to generate global land use maps at multiple spatial scales and different semantic granularities. In addition, our land use maps adhere to a unified land use categories and can be compared for accuracy and precision.
