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.
This paper describes the novel development and application of a multi-scale geographically weighted discriminant analysis (MSGWDA). This is applied to a case study of survey data of attitudes to a proposed motorbike / scooter ban in Han Noi, Vietnam. It uses discriminant analysis to examine attitudes to the ban in relation to travel purposes, distances, respondent age and so on. The main part of the paper focuses on describing the novel MSGWDA approach, and the results indicate the varying scales of relationship between the different input variables and the categorical responses variable. The paper also reflects on the pervasive logic of the approaches used to fit multiscale geographically weighted bandwidths (for example in regression). These have historically been based on the iterative back-fitting approaches used in GAMs, but risk missing potentially important variable interactions amongst un-evaluated bandwidths because of the sequence of their application. It is argued that although pragmatic in the 1990s, it may be possible to apply more deterministic approaches with increased memory and readily accessible computing power in order to better navigate such highly dimensional search spaces.
Data related to households or addresses needs be published in an aggregated form to obfuscate sensitive information about individuals. Usually, the data is aggregated to the level of existing administrative zones, but these often do not correspond to formal models of privacy or a desired level of anonymity. Therefore, automatic privacy-preserving spatial clustering methods are needed. To address this need, we present algorithms to partition a given set of locations into k-anonymous clusters, meaning that each cluster contains at least k locations. We assume that the locations are given as a set T ⊆ V of terminals in a weighted graph G = (V, E) representing a road network. Our approach is to compute a forest in G, i.e., a set of trees, each of which corresponds to a cluster. We ensure the k-anonymity of the clusters by constraining the trees to span at leastterminals each (plus an arbitrary number of non-terminal nodes called Steiner nodes). By minimizing the total edge weight of the forest, we ensure that the clusters reflect the proximity among the locations. Although the problem is NP-hard, we were able to solve instances of several hundreds of terminals using integer linear programming. Moreover, we present an efficient approximation algorithm and show that it can be used to process large and fine-grained data sets.
While eXplainable Artificial Intelligence (XAI) has significant potential to glassbox Deep Learning, there are challenges in applying it in the domain of Geospatial Artificial Intelligence (GeoAI). A land use case study highlights these challenges, which include the difficulty of selecting reference data/models, the shortcomings of gradients to serve as explanation, the limited semantics and knowledge scope in the explanation process of GeoAI, and underlying GeoAI processes that are not amenable to XAI. We conclude with possibilities to achieve Geographical XAI (GeoXAI).
Racial geography in US urban areas is extensively studied with the emphasis on assessing the extent of racial segregation. However, the used methodology has not changed for at least two decades; it relies on calculating ratios of population counts in the entire city and its subdivisions – census aggregation areas. This has a number of limitations; the two most important are: assessment of segregation depends on the subdivisions used, segregation can only be calculated for regions with census subdivisions. Here we present a different conceptualization of racial geography, which leads to a new method called racial landscape (RL). We use block-level census data to construct a high-resolution grid where each cell represents single race inhabitants. The result is a spatial, racial pattern; a degree of spatial autocorrelation of this pattern is a measure of segregation that does not require using subdivisions. We shortly describe the RL method and its application to Cook County, IL. We also describe here its implementation in the R computational environment.
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.
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.
Engagement with natural areas has increased during the Covid-19 pandemic, and this may well form one of the enduring legacies of this time. A better understanding of human interactions with urban greenspace, and how patterns of use have changed, including inequalities of use, will be crucial for decision makers to adequately manage and direct resources within these natural spaces as we recover from the pandemic. Current evidence on use of natural spaces is limited and does not easily support site-specific analysis or with fine spatio-temporal distinctions. Coupled with difficulties on primary data gathered throughout the pandemic, there is a general knowledge gap on how changing behaviour has reshaped the use of natural areas and what inequalities have arisen in this dynamic. Through the case study of Glasgow’s open spaces, with a specific focus on one urban park, we apply new forms of urban big data from mobile devices to show how the use of greenspace has changed through the restrictions imposed during Covid-19 pandemic. The research findings will help park managers, urban planners, and policymakers better design the recovery and renewal of our cities after the pandemic.
The use of machine learning models (ML) in spatial statistics and urban analytics is increasing. However, research studying the generalisability of ML models from a geographical perspective had been sparse, specifically on whether a model trained in one context can be used in another. The aim of this research is to explore the extent to which standard models such as convolutional neural networks being applied on urban images can generalise across different geographies, through two tasks. First, on the classification of street frontages and second, on the prediction of real estate values. In particular, we find in both experiments that the models do not generalise well. More interestingly, there are also differences in terms of generalisability within the first case study which needs further exploration. To summarise, our results suggest that in urban analytics there is a need to systematically test out-of-geography results for this type of geographical image-based models.
Polycentricity is a critical characteristic of the spatial organization of cities. Many indices have been proposed to measure the degree of morphological polycentricity or functional polycentricity. However, selecting a proper set of polycentricity indices for cities in a particular region or country still needs prior expert knowledge. This study demonstrates that whole graph embedding, as a novel and efficient computational tool, can model the city polycentricity in an integrated manner without much prior knowledge. The new method can further support visual analytics and classification very well.
