ABSTRACT Recently developed urban air quality sensor networks are used to monitor air pollutant concentrations at a fine spatial and temporal resolution. The measurements are however limited to point support. To obtain areal coverage in space and time, interpolation is required. A spatio-temporal regression kriging approach was applied to predict nitrogen dioxide (NO2) concentrations at unobserved

Cellular automata (CA) models are in growing use for land-use change simulation and future scenario prediction. It is necessary to conduct model assessment that reports the quality of simulation results and how well the models reproduce reliable spatial patterns. Here, we review 347 CA articles published during 1999–2018 identified by a Scholar Google search using ‘cellular automata’, ‘land’ and ‘urban’

Land-Use Mix (LUM) refers to the strategy of integrating complementary functions within a building or area. While LUM has become a dominant approach in urban planning, its actual benefits and vision for spatial planning remain unclear. To clarify this issue, this study discerns the spatial features of land-use patterns depending on the compatibilities among land-use categories. Accordingly, this study

The stochastic perturbation of urban cellular automata (CA) model is difficult to fine-tune and does not take the constraint of known factors into account when using a stochastic variable, and the simulation results can be quite different when using the Monte Carlo method, reducing the accuracy of the simulated results. Therefore, in this paper, we optimize the stochastic component of an urban CA model

Road intersection data have been used across a range of geospatial analyses. However, many datasets dating from before the advent of GIS are only available as historical printed maps. To be analyzed by GIS software, they need to be scanned and transformed into a usable (vector-based) format. Because the number of scanned historical maps is voluminous, automated methods of digitization and transformation

Short-term traffic forecasting on large street networks is significant in transportation and urban management, such as real-time route guidance and congestion alleviation. Nevertheless, it is very challenging to obtain high prediction accuracy with reasonable computational cost due to the complex spatial dependency on the traffic network and the time-varying traffic patterns. To address these issues

ABSTRACT Detecting movement patterns with complicated spatial or temporal characteristics is a challenge. The past decade has witnessed the success of deep learning in processing image, voice and text data. However, its application in movement pattern detection is not fully exploited. To address the research gap, this paper develops a deep learning approach to detect regional dominant movement patterns

This paper reports on a controlled experiment evaluating how different cartographic representations of risk affect participants’ performance on a complex spatial decision task: route planning. The specific experimental scenario used is oriented towards emergency route-planning during flood response. The experiment compared six common abstract and metaphorical graphical symbolizations of risk. The results

Pedestrian networks play an important role in various applications, such as pedestrian navigation services and mobility modeling. This paper presents a novel method to extract pedestrian networks from crowdsourced tracking data based on a two-layer framework. This framework includes a walking pattern classification layer and a pedestrian network generation layer. In the first layer, we propose a multi-scale

(2020). GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science: Vol. 34, Artificial intelligence techniques for geographic knowledge discovery, pp. 625-636.

Terrain feature detection is a fundamental task in terrain analysis and landscape scene interpretation. Discovering where a specific feature (i.e. sand dune, crater, etc.) is located and how it evolves over time is essential for understanding landform processes and their impacts on the environment, ecosystem, and human population. Traditional induction-based approaches are challenged by their inefficiency

Point cloud classification, which provides meaningful semantic labels to the points in a point cloud, is essential for generating three-dimensional (3D) models. Its automation, however, remains challenging due to varying point densities and irregular point distributions. Adapting existing deep-learning approaches for two-dimensional (2D) image classification to point cloud classification is inefficient

Quantifying aspects of urban design on a massive scale is crucial to help develop a deeper understanding of urban designs elements that contribute to the success of a public space. In this study, we further develop the Street-Frontage-Net (SFN), a convolutional neural network (CNN) that can successfully evaluate the quality of street frontage as either being active (frontage containing windows and

ABSTRACT Defining and identifying duplicate records in a dataset is a challenging task which grows more complex when the modeled entities themselves are hard to delineate. In the geospatial domain, it may not be clear where a mountain, stream, or valley ends and begins, a problem carried over when such entities are catalogued in gazetteers. In this paper, we take two gazetteers, GeoNames and SwissNames3D

Spatial interpolation is a traditional geostatistical operation that aims at predicting the attribute values of unobserved locations given a sample of data defined on point supports. However, the continuity and heterogeneity underlying spatial data are too complex to be approximated by classic statistical models. Deep learning models, especially the idea of conditional generative adversarial networks

ABSTRACT The temporal nature of humans interaction with Points of Interest (POIs) in cities can differ depending on place type and regional location. Times when many people are likely to visit restaurants (place type) in Italy, may differ from times when many people are likely to visit restaurants in Lebanon (i.e. regional differences). Geosocial data are a powerful resource to model these temporal

Building footprints are among the most predominant features in urban areas, and provide valuable information for urban planning, solar energy suitability analysis, etc. We aim to automatically and rapidly identify building footprints by leveraging deep learning techniques and the increased availability of remote sensing datasets at high spatial resolution. The task is computationally challenging due

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the

With large amounts of digital map archives becoming available, automatically extracting information from scanned historical maps is needed for many domains that require long-term historical geographic data. Convolutional Neural Networks (CNN) are powerful techniques that can be used for extracting locations of geographic features from scanned maps if sufficient representative training data are available

One of the major challenges in conducting epidemiological studies of air pollution and health is the difficulty of estimating the degree of exposure accurately. Fine particulate matter (PM2.5) concentrations vary in space and time, which are difficult to estimate in rural, suburban and smaller urban areas due to the sparsity of the ground monitoring network. Satellite retrieved aerosol optical depth

The aim of site planning based on multiple viewshed analysis is to select the minimum number of viewpoints that maximize visual coverage over a given terrain. However, increasingly high-resolution terrain data means that the number of terrain points will increase rapidly, which will lead to rapid increases in computational requirements for multiple viewshed site planning. In this article, we propose

Geographical information systems (GIS) practitioners worldwide enjoy a growing array of free and open source software (FOSS) options. This software has expanded the accessibility of GIS in economically developing countries while fostering local technical expertise. This article reviews FOSS GIS uptake and advocacy in South America, especially how it relates to a climate of political friendliness toward

Agricultural terraces are important for agricultural production and soil-and-water conservation. They comprise treads and risers that require manual construction and maintenance. If managed improperly, risers will collapse, causing soil loss, gully erosion, and cultivation threats. However, mapping terrace risers remains a challenge. This study presents a novel approach to automatically map terrace

Migratory birds have evolved diverse migration strategies in response to a variety of factors, but information about the detailed migration patterns of Asian songbirds is not yet available. To understand the short-distance migration pattern of declining Yellow-throated Buntings (Emberiza elegans) in East Asia, we analyzed stable isotopes from the outermost tail feathers (rectrices) of individual buntings

Cost surfaces are a crucial aspect of route optimization and least cost path (LCP) calculations and are used in awide range of disciplines including computer science, landscape ecology, and energy-infrastructure modeling. Linear features present akey weakness to traditional routing calculations along cost surfaces because they cannot identify whether moving from acell to its adjacent neighbors constitutes

The amount of spatiotemporal data collected by gadgets is rapidly growing, resulting in increasing costs to transfer, process and store it. In an attempt to minimize these costs several algorithms were proposed to reduce the trajectory size. However, to choose the right algorithm depends on a careful analysis of the application scenario. Therefore, this paper evaluates seven general purpose lossy compression

Address matching is a crucial step in geocoding, which plays an important role in urban planning and management. To date, the unprecedented development of location-based services has generated a large amount of unstructured address data. Traditional address matching methods mainly focus on the literal similarity of address records and are therefore not applicable to the unstructured address data. In

When a marine study area is large, the environmental variables often present spatially stratified non-homogeneity, violating the spatial second-order stationary assumption. The stratified non-homogeneous surface can be divided into several stationary strata with different means or variances, but still with close relationships between neighboring strata. To give the best linear-unbiased estimator for

International communication and global cooperation have greatly accelerated the worldwide spread of dengue fever, increasing the impact of imported cases on dengue outbreaks in non-naturally endemic areas. Existing studies mostly focus on describing the quantitative relationship between imported cases and local transmission but ignore the space-time diffusion mode of imported cases under the influence

When facing a specific emergent geographical environment observation task (GeoTask), people need to be able to handle reliable and comprehensive disaster information in the shortest possible time. The lack of effective cognition of multi-sensor collaborated observation capability is a hindrance to performance. By adopting the GIS object field concept as the bottom framework, we propose a sensor observation

Augmented Reality (AR) sandtables facilitate the shaping of sand to form a surface that is transformed into a digital terrain map which is projected back onto the sand. Although a mature technology, there are still few instances of sandtables being used in surface analysis. Fundamentally there has not been any reported formal assessment of how well sandtables perform in an educational context compared

In the cadastral system of Victoria, the legal extent of properties is defined by a wide range of property boundaries referencing physical objects. These boundary types are typically represented in 2D plans. However, to address the challenges of 2D plans in complex buildings, there has been a growing trend towards the adoption of 3D models. These 3D models have been mainly used for visualization purposes

The shortest-path algorithm is one of the most important algorithms in geographical information systems. Bellman’s principle of optimization (BPO) is implicit in the shortest-path problem; that is, any involved node must be located in the simple paths between source and destination nodes. Unfortunately, BPO has never been explicitly used to exclude irrelevant nodes in existing methods, potentially

As early as the fourth century BCE, the Nabateans established the Incense Road to facilitate the transport of aromatic substances (frankincense and myrrh) from the Arabian Peninsula to the Mediterranean basin. An important part of this road was the segment between Petra and Gaza. Although studied before, the accurate route of parts of this segment is still vague since evidence of Roman milestones are

(2020). Real-time GIS for smart cities. International Journal of Geographical Information Science: Vol. 34, No. 2, pp. 311-324.

The accurate measurement of human activity with high spatial and temporal granularity is crucial for understanding the structure and function of the built environment. With increasing mobile ownership, the Wi-Fi ‘probe requests’ generated by mobile devices can act as a cheap, scalable and real-time source of data for establishing such measures. The two major challenges we face in using these probe

City dashboards have become a common smart city technology, emerging as a key means of sharing and visualising urban data for the benefit of the public and city administrations. Operating as the front-end of many cities’ data stores, dashboards display and benchmark indicators relating to city operations, characteristics, and trends, displayed through interactive visual representations of spatial and

Public transit vehicles such as buses operate within shared transportation networks subject to dynamic conditions and disruptions such as traffic congestion. The operational delays caused by these conditions can propagate downstream through scheduled transit routes, affecting system performance beyond the initial delay. This paper develops an approach to measuring and assessing vehicle delay propagation

Natural disasters such as flooding, wildfires, and mudslides are rare events, but they affect citizens at unpredictable times and the impact on human life can be significant. Citizens located close to events can provide detailed, real-time data streams capturing their event response. Instead of visualizing individual updates, an integrated spatiotemporal map yields ‘big picture’ event information.

ABSTRACT This article discusses the prospects for automated discovery of explanatory models directly from geospatial data. Rather than taking an approach based on machine learning, which generally leads to models that cannot be understood by humans or related to domain theory, the approach described here suggests we can instead construct models from fragments of domain understanding—such as commonly

The ease of access to sites for activities is referred to as ‘accessibility,’ and the degree of accessibility is inherently the outcome of land use patterns and transportation networks. Therefore, accessibility measures (AMs) are required to evaluate and plan for land use and transportation. Geographic information system tools greatly aid in the calculation of AMs. Since the inception of time geography

Moving objects databases store and process objects with a focus on their spatiotemporal behaviour. To achieve this, the model of the data must be suitable to efficiently store and process moving objects. Currently, a unit-based model is widely used, where each moving object is divided into one or more time intervals, during which the object behaves uniformly. This model is also used for a data type

We develop a new geographical cellular automata (CA) modeling framework, named UrbanCA, through reconstructing the essential CA structure and incorporating nonspatial, spatial, and heuristic approaches. The new UrbanCA is featured by 1) the improvement of the CA modeling framework by reformulating relationships among CA components, 2) the development of two scaling parameters to adjust the effects

The physical and social processes in urban systems are inherently spatial and hence data describing them contain spatial autocorrelation (a proximity-based interdependency on a variable) that need to be accounted for. Standard k-fold cross-validation (KCV) techniques that attempt to measure the generalisation performance of machine learning and statistical algorithms are inappropriate in this setting

As one of the major concerns in cartographic generalization, road network generalization aims at maintaining the patterns of road networks across map scales. Previous methods define the pattern of road networks mainly from the perspectives of geometry and topology. However, for navigation purposes, traffic flow information is also important to generalize road networks. More specifically, road segments

As users increasingly rely on online social networks for their communication activities, personal location data processing through such networks poses significant risks to users’ privacy. Location tracks can be mined with other shared information to extract rich personal profiles. To protect users’ privacy, online social networks face the challenge of ensuring transparent communication to users of

An indoor navigation network, simplified from complex building structures, is of primary importance for indoor navigation. Methods for generating such networks are becoming increasingly important as buildings are becoming larger and more complex. However, studies based on the geometry and topology of indoor spaces do not sufficiently account for the key and decision nodes of human wayfinding. This

(2020). An introduction to R for spatial analysis and mapping (Second Edition) International Journal of Geographical Information Science: Vol. 34, No. 1, pp. 202-203.

The growing field of movement ecology uses high resolution movement data to analyze animal behavior across multiple scales: from individual foraging decisions to population-level space-use patterns. These analyses contribute to various subfields of ecology-inter alia behavioral, disease, landscape, resource, and wildlife-and facilitate facilitate novel exploration in fields ranging from conservation

Spatially explicit agent-based models (ABMs) have been widely utilized to simulate the dynamics of spatial processes that involve the interactions of individual agents. The assumptions embedded in the ABMs may be responsible for uncertainty in the model outcomes. To ensure the reliability of the outcomes in terms of their space-time patterns, model validation should be performed. In this paper, we

To assess micro-scale population dynamics effectively, demographic variables should be available over temporally consistent small area units. However, fine-resolution census boundaries often change between survey years. This research advances areal interpolation methods with dasymetric refinement to create accurate consistent population estimates in 1990 and 2000 (source zones) within tract boundaries

Wedding mobile phone sensor technology and human spatial behaviour has great potential. The ubiquity of Global Positioning Systems (GPS) technology has made gathering data about human mobility simpler, more precise, and with higher fidelity, providing minute-by-minute records of the locations of cohorts from dozens of participants. While this data provides a strong basis for Geographic Information

Recent availability of geo-localized data capturing individual human activity together with the statistical data on international migration opened up unprecedented opportunities for a study on global mobility. In this paper, we consider it from the perspective of a multi-layer complex network, built using a combination of three datasets: Twitter, Flickr and official migration data. Those datasets provide

Previous work suggests domestic poultry are important contributors to the emergence and transmission of highly pathogenic avian influenza throughout Asia. In Poyang Lake, China, domestic duck production cycles are synchronized with arrival and departure of thousands of migratory wild birds in the area. During these periods, high densities of juvenile domestic ducks are in close proximity to migratory

Advanced satellite tracking technologies enable biologists to track animal movements at fine spatial and temporal scales. The resultant data present opportunities and challenges for understanding animal behavioral mechanisms. In this paper, we develop a new method to elucidate animal movement patterns from tracking data. Here, we propose the notion of continuous behavior patterns as a concise representation

Global navigation satellite systems such as the Global Positioning System (GPS) is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distances recorded

The identification of regions is both a computational and conceptual challenge. Even with growing computational power, regionalization algorithms must rely on heuristic approaches in order to find solutions. Therefore, the constraints and evaluation criteria that define a region must be translated into an algorithm that can efficiently and effectively navigate the solution space to find the best solution

GIS-based multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping. However, the uncertainties that are associated with MCDA techniques may significantly impact the results. This may sometimes lead to inaccurate outcomes and undesirable consequences. This article introduces a new GIS-based MCDA approach. We illustrate the consequences of applying

Kruger National Park (KNP), South Africa, provides protected habitats for the unique animals of the African savannah. For the past 40 years, annual aerial surveys of herbivores have been conducted to aid management decisions based on (1) the spatial distribution of species throughout the park and (2) total species populations in a year. The surveys are extremely time consuming and costly. For many