Detection Of Spatial Patterns Research Articles

Automated spatial omics landscape analysis approach reveals novel tissue architectures in ulcerative colitis

The utility of spatial omics in leveraging cellular interactions in normal and diseased states for precision medicine is hampered by a lack of strategies for matching disease states with spatial heterogeneity-guided cellular annotations. Here we use a spatial context-dependent approach that matches spatial pattern detection to cell annotation. Using this approach in existing datasets from ulcerative colitis patient colonic biopsies, we identified architectural complexities and associated difficult-to-detect rare cell types in ulcerative colitis germinal-center B cell follicles. Our approach deepens our understanding of health and disease pathogenesis, illustrates a strategy for automating nested architecture detection for highly multiplexed spatial biology data, and informs precision diagnosis and therapeutic strategies.

Exploring Effective Detection and Spatial Pattern of Prickly Pear Cactus (Opuntia Genus) from Airborne Imagery before and after Prescribed Fires in the Edwards Plateau

Over the past century, prickly pear (PP) cactus (e.g., genus Opuntia; subgenus Platyopuntia) has increased on semi-arid rangelands. Effective detection of cacti abundance and spatial pattern is challenging due to the inherent heterogeneity of rangeland landscapes. In this study, high-resolution multispectral imageries (0.21 m) were used to test object-based (OB) feature extraction, random forest (RF) machine learning, and spectral endmember (n-D) classification methods to map PP and evaluate its spatial pattern. We trained and tested classification methods using field-collected GPS location, plant cover, and spectrometry from 288 2 m radius polygons before a prescribed burn and 480 samples after the burn within a 69.2-ha burn unit. The most accurate classification method was then used to map PP distribution and quantify abundance before and after fire. As a case study, we assessed the spatial pattern of mapped PP cover, considering topoedaphic setting and burn conditions. The results showed that the endmember classification method, spectral angle mapper (SAM), outperformed the RF and OB classifications with higher kappa coefficients (KC) (0.93 vs. 0.82 and 0.23, respectively) and overall accuracies (OA) (0.96 vs. 0.91 and 0.49) from pre-fire imagery. KC and OA metrics of post-fire imagery were lower, but rankings among classification methods were similar. SAM classifications revealed that fire reduced PP abundance by 46.5%, but reductions varied by soil type, with deeper soils having greater decreases (61%). Kolmogorov-Smirnov tests indicated significant changes before and after fire in the frequency distribution of PP cover within deeper soils (D = 0.64, p = 0.02). A two-way ANOVA revealed that the interaction of season (pre- vs. post-fire) and soils significantly (p < 0.00001) influenced the spatial pattern of PP patches. Fire also reduced the size and shape of PP patches depending on the topoedaphic settings. This study provides an innovative and effective approach for integrating field data collection, remote sensing, and endmember classification methods to map prickly pear and assess the effects of prescribed fire on prickly pear spatial patterns. Accurate mapping of PP can aid in the design and implementation of spatially explicit rangeland management strategies, such as fire, that can help reduce and mitigate the ecological and economic impacts of prickly pear expansion.

Characterizing spatio-temporal patterns of coastal navigation to inform mitigation strategies for conflict between ocean users and the protected humpback whale (Megaptera novaeangliae) in Bahía de Banderas, Mexico

Characterizing specific pressures on natural resources is a key component of developing sustainable management strategies. Here we explored the spatiotemporal distribution of marine vessels in a potentially high conflict area between humpback whales and tourism activities, namely Bahía de Banderas, Mexico, from 2017 to 2023. Using high-resolution satellite imagery, we identified high-use areas and their relationship to the presence of urban areas, vessel departure points, and the coastline before, during, and after restrictions put in place during the peak of the COVID-19 pandemic. High-resolution satellite imagery (3m/px - 5m/px) retrieved through the Planet Application Program was used to perform manual detection and annotation of vessels and spatial patterns of marine navigation were characterized through point pattern analyses The spatial distribution of all marine vessel observations in the study area was not random; four high-use coastal areas closer to urban areas were identified, three of which overlap the current marine protected area established to protect whale nursing grounds. Furthermore, as the COVID-19 pandemic began, coastal navigation decreased due to lockdown before picking up again at a higher level than the pre-COVID baseline due to social distancing restrictions. While these changes were not statistically significant, they potentially indicate a behavioral change caused by COVID-19 social distancing recommendations during 2021; the amplified demand for marine vessels appears to have increased the absolute number of vessel chartering and decreased the number of passengers onboard commercial vessels. Our preliminary work provides a reproducible remote sensing methodology that can support future mitigation strategies.

Detection of spatial and temporal precipitation patterns using remotely sensed data in the Paranapanema River Basin, Brazil from 2000 to 2021

Precipitation is the major input of the hydrological cycle in tropical regions. Changes in the spatial and temporal patterns of precipitation should be investigated in order to provide in-time information for both water and land use planning. Climate and land use changes have been influencing modification in the water cycle, demanding adaptations and increasing the vulnerability of water-dependent systems. This study investigated spatial and temporal changes in precipitation patterns in the Paranapanema River Basin (PPRB), Brazil. The PPRB region is an important agricultural and hydroelectric power generation hub and has been suffering from water crises in recent years, and more intensely in the last 5–10 years. The analysis used remote sensing precipitations data from September 2000 to August 2021 (summing up twenty-one hydrological years) at 0.1° resolution. Exploratory Spatial and Temporal Data Analysis (ESTDA) were applied to verify spatial local autocorrelation and hot/cold spots clusters, and temporal trends, homogeneity, and change points in the time series at Hydrological Planning Unit (HPU) scale level. The significant results were discussed based on statistical tests and land use cover change data. There is a strong presence of precipitation spatial patterns in the PPRB. Also, the PPRB presented modifications in the precipitation regime over the analyzed period, with significant change points around 2015—2017. Further monitoring is recommended in order to confirm these results in the long term, however, the provided information can already be used as an award to local and regional water bodies installed in the river basin, supporting informative water management.

Deciphering Small-Scale Seasonal Surface Dynamics of Rock Glaciers in the Central European Alps Using DInSAR Time Series

The Essential Climate Variable (ECV) Permafrost is currently undergoing strong changes due to rising ground and air temperatures. Surface movement, forming characteristic landforms such as rock glaciers, is one key indicator for mountain permafrost. Monitoring this movement can indicate ongoing changes in permafrost; therefore, rock glacier velocity (RGV) has recently been added as an ECV product. Despite the increased understanding of rock glacier dynamics in recent years, most observations are either limited in terms of the spatial coverage or temporal resolution. According to recent studies, Sentinel-1 (C-band) Differential SAR Interferometry (DInSAR) has potential for monitoring RGVs at high spatial and temporal resolutions. However, the suitability of DInSAR for the detection of heterogeneous small-scale spatial patterns of rock glacier velocities was never at the center of these studies. We address this shortcoming by generating and analyzing Sentinel-1 DInSAR time series over five years to detect small-scale displacement patterns of five high alpine permafrost environments located in the Central European Alps on a weekly basis at a range of a few millimeters. Our approach is based on a semi-automated procedure using open-source programs (SNAP, pyrate) and provides East-West displacement and elevation change with a ground sampling distance of 5 m. Comparison with annual movement derived from orthophotos and unpiloted aerial vehicle (UAV) data shows that DInSAR covers about one third of the total movement, which represents the proportion of the year suited for DInSAR, and shows good spatial agreement (Pearson R: 0.42–0.74, RMSE: 4.7–11.6 cm/a) except for areas with phase unwrapping errors. Moreover, the DInSAR time series unveils spatio-temporal variations and distinct seasonal movement dynamics related to different drivers and processes as well as internal structures. Combining our approach with in situ observations could help to achieve a more holistic understanding of rock glacier dynamics and to assess the future evolution of permafrost under changing climatic conditions.

Exploring Crowd Travel Demands Based on the Characteristics of Spatiotemporal Interaction between Urban Functional Zones

As a hot research topic in urban geography, spatiotemporal interaction analysis has been used to detect the hotspot mobility patterns of crowds and urban structures based on the origin-destination (OD) flow data, which provide useful information for urban planning and traffic management applications. However, existing methods mainly focus on the detection of explicit spatial interaction patterns (such as spatial flow clusters) in OD flow data, with less attention to the discovery of underlying crowd travel demands. Therefore, this paper proposes a framework to discover the crowd travel demands by associating the dynamic spatiotemporal interaction patterns and the contextual semantic features of the geographical environment. With urban functional zones (UFZs) as the basic units of human mobility in urban spaces, this paper gives a case study in Wuhan, China, to detect and interpret the human mobility patterns based on the characteristics of spatiotemporal interaction between UFZs. Firstly, we build the spatiotemporal interaction matrix based on the OD flows of different UFZs and analyze the characteristics of the interaction matrix. Then, hotspot poles, defined as the local areas where people gather significantly, are extracted using the Gi-statistic-based spatial hotspot detection algorithm. Next, we develop a frequent interaction pattern mining method to detect the frequent interaction patterns of the hotspot poles. Finally, based on the detected frequent interaction patterns, we discover the travel demands of crowds with semantic features of corresponding urban functional zones. The characteristics of crowd travel distance and travel time are further discussed. Experiments with floating car data, road networks, and POIs in Wuhan were conducted, and results show that the underlying travel demands can be better discovered and interpreted by the proposed framework and methods in this paper. This study helps to understand the characteristics of human movement and can provide support for applications such as urban planning and facility optimization.

GIS-based modeling of landslide susceptibility zonation by integrating the frequency ratio and objective–subjective weighting approach: a case study in a tropical monsoon climate region

Accurate detection of landslide spatial patterns is vital in susceptibility, hazard, and risk disaster mapping. Geographic Information System (GIS)-based quantitative approaches provide a rigorous procedure for gaining deep insight into natural and anthropogenic landslides from different scales. This study aims to implement a comprehensive solution for retrieving the landslide susceptibility index. For that purpose, a landslide inventory was performed in a tropical monsoon climate region, with a magnitude of elevation spanning from −65 m to 1,900 m above the sea, considering 15 fundamental causative factors belonging to the groups of topography, hydrology, geology, land cover conditions and anthropogenic activities, and weather. The frequency ratio (FR) was implemented to rank subclasses in each causative factor. For factor weight estimation, different approaches were applied, including the subjective-based analytic hierarchy process (AHP), objective-based Shannon entropy (SE), and a synergy of both methods (AHP–SE), built on these two approaches. Out of the 271 identified landslide locations, 70% (196 points) were used for training and the remaining 30% (71 points) were applied for validation. The results showed that the integrated AHP–SE outperformed the two individual approaches, with the area under the receiver operating characteristic curve (AUC) reaching 0.876, following SE (AUC = 0.848) and AHP (AUC = 0.818). In the synergy approach, the climate pattern under tropical monsoons was confirmed as the most crucial landslide-predisposing factor. The research contributes to a novel discussion by integrating knowledge-based consultation and statistical data analysis of accurate geospatial data, incorporating significant explanatory factors toward a reliable landslide-prone zonation over space and time dimensions.

SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics

Spatially resolved transcriptomics (SRT)-specific computational methods are often developed, tested, validated, and evaluated in silico using simulated data. Unfortunately, existing simulated SRT data are often poorly documented, hard to reproduce, or unrealistic. Single-cell simulators are not directly applicable for SRT simulation as they cannot incorporate spatial information. We present SRTsim, an SRT-specific simulator for scalable, reproducible, and realistic SRT simulations. SRTsim not only maintains various expression characteristics of SRT data but also preserves spatial patterns. We illustrate the benefits of SRTsim in benchmarking methods for spatial clustering, spatial expression pattern detection, and cell-cell communication identification.

Scalable and model-free detection of spatial patterns and colocalization.

The expeditious growth in spatial omics technologies enables the profiling of genome-wide molecular events at molecular and single-cell resolution, highlighting a need for fast and reliable methods to characterize spatial patterns. We developed SpaGene, a model-free method to discover spatial patterns rapidly in large-scale spatial omics studies. Analyzing simulation and a variety of spatially resolved transcriptomics data showed that SpaGene is more powerful and scalable than existing methods. Spatial expression patterns identified by SpaGene reconstruct unobserved tissue structures. SpaGene also successfully discovers ligand–receptor interactions through their colocalization.

Statistical analysis of spatially resolved transcriptomic data by incorporating multiomics auxiliary information.

Effective control of false discovery rate is key for multiplicity problems. Here, we consider incorporating informative covariates from external datasets in the multiple testing procedure to boost statistical power while maintaining false discovery rate control. In particular, we focus on the statistical analysis of innovative high-dimensional spatial transcriptomic data while incorporating external multiomics data that provide distinct but complementary information to the detection of spatial expression patterns. We extend OrderShapeEM, an efficient covariate-assisted multiple testing procedure that incorporates one auxiliary study, to make it permissible to incorporate multiple external omics studies, to boost statistical power of spatial expression pattern detection. Specifically, we first use a recently proposed computationally efficient statistical analysis method, spatial pattern recognition via kernels, to produce the primary test statistics for spatial transcriptomic data. Afterwards, we construct the auxiliary covariate by combining information from multiple external omics studies, such as bulk and single-cell RNA-seq data using the Cauchy combination rule. Finally, we extend and implement the integrative analysis method OrderShapeEM on the primary P-values along with auxiliary data incorporating multiomics information for efficient covariate-assisted spatial expression analysis. We conduct a series of realistic simulations to evaluate the performance of our method with known ground truth. Four case studies in mouse olfactory bulb, mouse cerebellum, human breast cancer, and human heart tissues further demonstrate the substantial power gain of our method in detecting genes with spatial expression patterns compared to existing classic approaches that do not utilize any external information.

Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP) Using Passive Microwave and Infrared Data

Abstract Recent developments in “headline-making” deep neural networks (DNNs), specifically convolutional neural networks (CNNs), along with advancements in computational power, open great opportunities to integrate massive amounts of real-time observations to characterize spatiotemporal structures of surface precipitation. This study aims to develop a CNN algorithm, named Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP), that ingests direct satellite passive microwave (PMW) brightness temperatures (Tbs) at emission and scattering frequencies combined with infrared (IR) Tbs from geostationary satellites and surface information to automatically extract geospatial features related to the precipitable clouds. These features allow the end-to-end Deep-STEP algorithm to instantaneously map surface precipitation intensities with a spatial resolution of 4 km. The main advantages of Deep-STEP, as compared to current state-of-the-art techniques, are 1) it learns and estimates complex precipitation systems directly from raw measurements in near–real time, 2) it uses the automatic spatial neighborhood feature extraction approach, and 3) it fuses coarse-resolution PMW footprints with IR images to reliably retrieve surface precipitation at a high spatial resolution. We anticipate our proposed DNN algorithm to be a starting point for more sophisticated and efficient precipitation retrieval systems in terms of accuracy, fine spatial pattern detection skills, and computational costs.

Urban Growth Derived from Landsat Time Series Using Harmonic Analysis: A Case Study in South England with High Levels of Cloud Cover

Accurate detection of spatial patterns of urban growth is crucial to the analysis of urban growth processes. A common practice is to use post-classification change analysis, overlaying multiple independently derived land cover layers. This approach is problematic as propagation of classification errors can lead to overestimation of change by an order of magnitude. This paper contributes to the growing literature on change classification using pixel-based time series analysis. In particular, we have developed a method that identifies change in the urban fabric at the pixel level based on breaks in the seasonal and year-on-year trend of the normalised difference vegetation index (NDVI). The method is applied to a case study area in the south of England that is characterised by high levels of cloud cover. The study uses the Landsat data archive over the period 1984–2018. The performance of the method was assessed using 500 ground truth points. These points were randomly selected and manually assessed for change using high-resolution earth observation imagery. The method identifies pixels where a land cover change occurred with a user’s accuracy of change 45.3 ± 4.45% and outperforms a post-classification analysis of an otherwise more advanced land cover product, which achieved a user’s accuracy of 17.8 ± 3.42%. This method performs better where changes exhibit large differences in NDVI dynamics amongst land cover types, such as the transition from agricultural to suburban, and less so where small differences of NDVI are observed, such as changes in land cover within pixels that are densely built up already. The method proved relatively robust for outliers and missing data, for example, in the case of high levels of cloud cover, but does rely on a period of data availability before and after the change event. Future developments to improve the method are to incorporate spectral information other than NDVI and to consider multiple change events per pixel over the analysed period.

Detection of Spatial and Temporal Patterns of Liana Infestation Using Satellite-Derived Imagery

Lianas (woody vines) play a key role in tropical forest dynamics because of their strong influence on tree growth, mortality and regeneration. Assessing liana infestation over large areas is critical to understand the factors that drive their spatial distribution and to monitor change over time. However, it currently remains unclear whether satellite-based imagery can be used to detect liana infestation across closed-canopy forests and therefore if satellite-observed changes in liana infestation can be detected over time and in response to climatic conditions. Here, we aim to determine the efficacy of satellite-based remote sensing for the detection of spatial and temporal patterns of liana infestation across a primary and selectively logged aseasonal forest in Sabah, Borneo. We used predicted liana infestation derived from airborne hyperspectral data to train a neural network classification for prediction across four Sentinel-2 satellite-based images from 2016 to 2019. Our results showed that liana infestation was positively related to an increase in Greenness Index (GI), a simple metric relating to the amount of photosynthetically active green leaves. Furthermore, this relationship was observed in different forest types and during (2016), as well as after (2017–2019), an El Niño-induced drought. Using a neural network classification, we assessed liana infestation over time and showed an increase in the percentage of severely (>75%) liana infested pixels from 12.9% ± 0.63 (95% CI) in 2016 to 17.3% ± 2 in 2019. This implies that reports of increasing liana abundance may be more wide-spread than currently assumed. This is the first study to show that liana infestation can be accurately detected across closed-canopy tropical forests using satellite-based imagery. Furthermore, the detection of liana infestation during both dry and wet years and across forest types suggests this method should be broadly applicable across tropical forests. This work therefore advances our ability to explore the drivers responsible for patterns of liana infestation at multiple spatial and temporal scales and to quantify liana-induced impacts on carbon dynamics in tropical forests globally.

Can a Remote Sensing Approach with Hyperspectral Data Provide Early Detection and Mapping of Spatial Patterns of Black Bear Bark Stripping in Coast Redwoods?

The prevalence of black bear (Ursus americanus) bark stripping in commercial redwood (Sequoia sempervirens (D. Don) Endl.) timber stands has been increasing in recent years. This stripping is a threat to commercial timber production because of the deleterious effects on redwood tree fitness. This study sought to unveil a remote sensing method to detect these damaged trees early and map their spatial patterns. By developing a timely monitoring method, forest timber companies can manipulate their timber harvesting routines to adapt to the consequences of the problem. We explored the utility of high spatial resolution UAV-collected hyperspectral imagery as a means for early detection of individual trees stripped by black bears. A hyperspectral sensor was used to capture ultra-high spatial and spectral information pertaining to redwood trees with no damage, those that have been recently attacked by bears, and those with old bear damage. This spectral information was assessed using the Jeffries-Matusita (JM) distance to determine regions along the electromagnetic spectrum that are useful for discerning these three-health classes. While we were able to distinguish healthy trees from trees with old damage, we were unable to distinguish healthy trees from recently damaged trees due to the inherent characteristics of redwood tree growth and the subtle spectral changes within individual tree crowns for the time period assessed. The results, however, showed that with further assessment, a time window may be identified that informs damage before trees completely lose value.

La influencia del sexo en el uso de los campus virtuales. Estudio de caso

A comparative analysis has been carried out in order to study the similarities and differences in the use and management of space and time by students, based on their gender. The study analyses the data records generated by students from three different degree courses in two universities, throughout four academic years. The methodology includes exploratory statistical analysis and learning analytics for the detection of spatial, temporal and behavioural patterns. Results show a consistent convergence in most cases, although they also show marginal behavioral trends, both for the days of the week, the hours of the day, and for the different contents of the virtual campuses. This is more evident in its spatial aspect, which highlights some clear differences in the processes of distribution and concentration of the events under study: at least in this case, women do not act in the same way as men. Ultimately, the study proposes new forms of synergy between educational work and the application of the Social Sciences’ disciplinary contents thus strengthening the transfer of knowledge from specific didactics on both educational curriculum and teacher training.

Support vector subset scan for spatial pattern detection

Discovery of localized and irregularly shaped anomalous patterns in spatial data provides useful context for operational decisions across many policy domains. The support vector subset scan (SVSS) integrates the penalized fast subset scan with a kernel support vector machine classifier to accurately detect spatial clusters without imposing hard constraints on the shape or size of the pattern. The method iterates between (1) efficiently maximizing a penalized log-likelihood ratio over subsets of locations to obtain an anomalous pattern, and (2) learning a high-dimensional decision boundary between locations included in and excluded from the anomalous subset. On each iteration, location-specific penalties to the log-likelihood ratio are assigned according to distance to the decision boundary, encouraging patterns which are spatially compact but potentially highly irregular in shape. SVSS outperforms competing methods for spatial cluster detection at the task of detecting randomly generated patterns in simulated experiments. SVSS enables discovery of practically-useful anomalous patterns for disease surveillance in Chicago, IL, crime hotspot detection in Portland, OR, and pothole cluster detection in Pittsburgh, PA, as demonstrated by experiments using publicly available data sets from these domains.

NREM sleep stages specifically alter dynamical integration of large-scale brain networks.

SummaryFunctional dissociations in the brain observed during non-rapid eye movement (NREM) sleep have been associated with reduced information integration and impaired consciousness that accompany increasing sleep depth. Here, we explored the dynamical properties of large-scale functional brain networks derived from transient brain activity using functional magnetic resonance imaging. Spatial brain maps generally display significant modifications in terms of their tendency to occur across wakefulness and NREM sleep. Unexpectedly, almost all networks predominated in activity during NREM stage 2 before an abrupt loss of activity is observed in NREM stage 3. Yet, functional connectivity and mutual dependencies between these networks progressively broke down with increasing sleep depth. Thus, the efficiency of information transfer during NREM stage 2 is low despite the high attempt to communicate. Critically, our approach provides relevant data for evaluating functional brain network integrity and our findings robustly support a significant advance in our neural models of human sleep and consciousness.

Exploring new ways of visitor tracking using big data sources: Opportunities and limits of passive mobile data for tourism

Passive mobile data (PMD) are event data recorded by mobile network operators (MNOs) in the course of a consumer's use of mobile phones connected to public voice and data networks. Increasingly, MNOs provide such data for research and applications in tourism, anonymised according to national regulations and aggregated based on the technical and economic interests of the MNO. Alongside mobility research, it is evident that tourism research has been one of the early adopters of this data source. Possible applications of PMD in tourism research include the identification of tourists, the detection of temporal and spatial distribution patterns, and the analysis of spatial and temporal relations. However, a number of drawbacks have been identified. These include the results of anonymisation and aggregation procedures, and, most of all, the inability to identify tourist activities properly, as opposed to everyday or other non-tourist types of mobility. This paper analyses and aggregates the results of different research projects on different spatial levels in Germany in order to build a conceptual framework for the specific strengths and weaknesses of the use of PMD in tourism research. The study found that, at the current state of research, PMD can measure the mobility of people in space and time but are not suitable for correctly identifying tourists and distinguishing them from non-tourists. Destination management organisations (DMOs) that are working with PMD should be aware of these barriers and adapt their research questions accordingly. However, PMD can be a powerful instrument, particularly because of its high temporal and spatial granularity. • Analyses passive mobile data can add new insights about tourism-related mobility. • Correct identification of tourism flows is the main challenge. • PMD measure mobility rather than tourism as a special form of mobility. • PMD should be handled in the context of reference frameworks. • Research should aim to identify tourist signals despite strict data-protection rules.

The capability of species-related forest stand characteristics determination with the use of hyperspectral data

The primary goal of this study was to bridge the gap between hyperspectral data classifications and their practical implementation for forest management. Information on the distributions and abundances of tree species can help forest managers understand the spatial coverage of tree species and manage the species composition by establishing a specific strategy. For this purpose, classification of tree species was applied to airborne hyperspectral imagery, and a determination of selected forest stand characteristics (i.e., main tree species, species cover proportions, and species mixture patterns) was made.We selected the Milicz Forest District of southwestern Poland as our study area. Sixteen airborne hyperspectral images were acquired with the HySpex VNIR-1800 and SWIR-384 sensors on 19 August 2015. We classified four deciduous tree species (Black alder, Pedunculate oak, Silver birch, and European beech) and coniferous species using a support vector machine (SVM) classifier. To properly evaluate the classification accuracy, the level of crown visibility from above was determined during ground measurements. The obtained information was used to create a reference set integrated with the Remote Sensing data.The classification accuracy was 91%. The main tree species found in the sample plots were correctly assessed based on the coverage area for 92% of the sample plots. The species cover proportions were estimated correctly for 75–94% of the sample plots with a tolerance threshold ≤10 percentage points depending on the species considered. Similarities of the species structures shown in the sample plots between the classification map and reference field data were found by Morisita's index (0.92). Spatial mixture pattern detection was performed for 316 forest stands. The results of the developed method were found to agree to field data with 69% accuracy. This study shows that airborne hyperspectral data serve as a reliable source for the precise description of forest characteristics, such as the main tree species, tree species cover proportions and mixture patterns.

Examining inclusive mobility through smartcard data: What shall we make of senior citizens' declining bus patronage in the West Midlands?

Free access to public transport is viewed as an effective means to enable inclusive mobility, but the recent decline in bus patronage by passengers entitled to free travel in the West Midlands and other UK regions presents a conundrum to transport research and policy. In this research, we analyse travel smartcard transactions recorded over nearly six years for 371,220 concessionary passengers resident in the West Midlands Combined Authority. Using sequence analysis, we identify six groups representing different temporal boarding profiles. We link these profiles to geo-spatial data and characterise them by means of spatial pattern detection and multinomial logit modelling. We find that, first, the decline in patronage occurs in three waves across the study period according to distinct activity patterns; second, formerly frequent (daily) passengers tend to abandon the bus and thus show the largest impact on the overall trend; third, the neighbourhood context of withdrawing passengers indicates social disadvantage, higher instance of ethnic minorities and lower car ownership rates, in other words higher risk of social exclusion. To discuss these in parts paradoxical results, we identify three causal domains - social, health-related and structural - and conclude with implications for policy promoting inclusive and healthy mobility in later life.