The ‘cottage effect’ in citizen science? Spatial bias in aquatic monitoring programs

Citizen science aquatic monitoring programs often rely on opportunistic, incidental contributions, which can lead to spatial bias, the uneven geographical distribution of sample sites. It is less known how this spatial bias compares to professional monitoring activities, or how geospatial biases (e.g. terrain slope, population density, road density) influence aquatic citizen science and professional lake monitoring programs. This paper compares sample sites in Ontario’s volunteer Lake Partner Program, against those identified by a stratified random sampling method currently used by the Province of Ontario, Ministry of Natural Resources and Forestry. Exploration of spatial bias within each sampling method was conducted using Kernel Density Estimation, a nonparametric approach to interpolating the spatial trend of a given variable. Results indicate that two distinct patterns of sampling clusters exist between the two datasets, suggesting a ‘cottage effect’ in which volunteers are more likely to sample accessible locations associated with recreation and summer home ownership. Although professional monitoring programs are not exempt from spatial bias, our research suggests that citizen science lake monitoring programs in Ontario are more influenced by natural and demographic biases related to the location, accessibility, size and general attractiveness of lakes.     

Observations of dugongs at Aldabra Atoll,western Indian Ocean: lagoon habitat mapping and spatial analysis of sighting records

Until recently, it was thought that dugongs (Dugong dugon) were extinct in the Seychelles. However, a collection of sightings at Aldabra Atoll, a World Heritage Site in the Seychelles, has renewed interest in dugong distribution in the western Indian Ocean. This article consolidates the records of dugong sightings held in the Aldabra Research Station library and explores their spatial patterning. The locations of sightings (2001–2009) are plotted onto a high-resolution benthic habitat map of the Aldabra lagoon created by classifying a QuickBird satellite remote-sensing image in January 2009. A spatial cluster detection procedure is applied to point records of sightings to reveal a statistically significant cluster of sightings in the north-west of the lagoon, at Bras Monsieur Clairemont, suggesting a mutual co-existence of dugongs and seagrass beds. A habitat suitability model combines the point data set of dugong sightings within the continuous benthic habitat map and identifies the central western area as containing the most suitable habitat for dugong inside the Aldabra lagoon.     

Habitat modeling for health sovereignty: Increasing indigenous access to medicinal plants in northern Maine,USA

Medicinal plants and fungi play important roles in the health of Maliseet people of northern Maine, USA. A critical aspect of exercising choice in health care for this community is the ability to locate and have access to these plants. Habitat suitability modeling is a form of geospatial technology that can enhance health sovereignty by identifying locations in which populations of medicinal plants can be conserved or established. However, use of this technology within indigenous communities has been limited. Focusing on the medicinal plant muskrat root, Acorus americanus (Raf.) Raf., we generate a habitat suitability model for eastern Aroostook County, Maine (1,055,653.659 ha) that also takes community needs into consideration. Drawing on participatory ethnographic data as well as environmental characteristics, our model combines ecological and sociocultural parameters to identify previously unknown populations of A. americanus that are accessible to tribal elders. Our model successfully predicted 95% of A. americanus locations in our field validation data set of ∼71,000 ha. Results suggest that approximately 0.6% of our study area contains suitable habitat to plant muskrat root that could also meet tribal members' gathering needs for the future. Increasing the number of potential collection sites gives communities options for gathering, thereby enhancing health sovereignty. Broadly, our work suggests that, when done in partnership with communities, different forms of geospatial technology can be beneficial tools for efforts to promote health sovereignty.     

A Cartographic Analysis Using Spatial Filter Logistic Model Specifications for Implementing Mosquito Control in Kenya

Negative spatial autocorrelation (NSA), the tendency for dissimilar neighboring values to cluster on a map, may go undetected in statistical analyses of immature Anopheles gambiae s.l., a leading malaria mosquito vector in Sub-Saharan Africa. Unquantified NSA generated from an inverse variance-covariance matrix may generate misspecifications in an An. gambiae s.l. habitat model. In this research, we used an eigenfunction decomposition algorithm based on a modified geographic connectivity matrix to compute the Moran's I statistic, to uncover hidden NSA in a dataset of georeferenced An. gambiae s.l. habitat explanatory predictor variables spatiotemporally sampled in Malindi and Kisumu, Kenya. The Moran's I statistic was decomposed into orthogonal synthetic map patterns. Global tests revealed that |z_MC|s generated were less than 1.11 for the presence of latent autocorrelation. The algorithm captured NSA in the An. gambiae s.l. habitat data by quantifying all non-normal random variables, space-time heterogeneity, and distributional properties of the spatial filters.     

Teaching Spatial Thinking and Geospatial Technologies Through Citizen Mapping and Problem-Based Inquiry in Grades 7-12

Our project introduced students in grades 7 through 12 to spatial thinking and geospatial technologies in the context of challenges in their community. We used a mix of levels of inquiry to advance learning from teacher- to student-guided through a citizen mapping group activity. Student-suggested problem-based topics included parks and community gardens, crime, housing, and youth employment opportunities. Qualitative methods were used to evaluate students’ knowledge of spatial thinking and geospatial technologies, including map interpretation, a case study, daily exit slips, and interviews. Overall, the students’ awareness of their community, spatial thinking, and geospatial technologies increased as a result of participation.     

Quantification of spatial gradation of slope positions

Transition between slope positions (e.g., ridge, shoulder slope, back slope, foot slope, and valley) is often gradual. Quantification of spatial transitions or spatial gradations between slope positions can increase the accuracy of terrain parameterization for geographical or ecological modeling, especially for digital soil mapping at a fine scale. Current models for characterizing the spatial gradation of slope positions based on a gridded DEM either focus solely on the parameter space or depend on too many rules defined by topographic attributes, which makes such approaches impractical. The typical locations of a slope position contain the characteristics of the slope position in both parameter space and spatial context. Thus, the spatial gradation of slope positions can be quantified by comparing terrain characteristics (spatial and parametrical) of given locations to those at typical locations. Based on this idea, this paper proposes an approach to quantifying the spatial gradation of slope positions by using typical locations as prototypes. This approach includes two parts: the first is to extract the typical locations of each slope position and treat them as the prototypes of this position; and the second is to compute the similarity between a given location and the prototypes based on both local topographic attributes and spatial context. The new approach characterizes slope position gradation in both the attribute domain (i.e., parameter space) and the spatial domain (i.e., geographic space) in an easy and practicable way. Applications show that the new approach can quantitatively describe spatial gradations among a set of slope positions. Comparison of spatial gradation of A-horizon sand percentages with the quantified spatial gradation of slope positions indicates that the latter reflects slope processes, confirming the effectiveness of the approach. The comparison of a soil subgroup map of the study area with the maximum similarity map derived from the approach also suggests that the quantified spatial gradation of slope position can be used to aid geographical modeling such as digital soil mapping.     

A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena

Volunteered geographic information (VGI) contains valuable field observations that represent the spatial distribution of geographic phenomena. As such, it has the potential to provide regularly updated low-cost field samples for predictively mapping the spatial variations of geographic phenomena. The predictive mapping of geographic phenomena often requires representative samples for high mapping accuracy, but samples consisting of VGI observations are often not representative as they concentrate on specific geographic areas (i.e. spatial bias) due to the opportunistic nature of voluntary observation efforts. In this article, we propose a representativeness-directed approach to mitigate spatial bias in VGI for predictive mapping. The proposed approach defines and quantifies sample representativeness by comparing the probability distributions of sample locations and the mapping area in the environmental covariate space. Spatial bias is mitigated by weighting the sample locations to maximize their representativeness. The approach is evaluated using species habit suitability mapping as a case study. The results show that the accuracy of predictive mapping using weighted sample locations is higher than using unweighted sample locations. A positive relationship between sample representativeness and mapping accuracy is also observed, suggesting that sample representativeness is a valid indicator of predictive mapping accuracy. This approach mitigates spatial bias in VGI to improve predictive mapping accuracy.     

Scale dependence in habitat selection: the case of the endangered brown bear (Ursus arctos) in the Cantabrian Range (NW Spain)

Animals select habitat resources at multiple spatial scales. Thus, explicit attention to scale dependency in species–habitat relationships is critical to understand the habitat suitability patterns as perceived by organisms in complex landscapes. Identification of the scales at which particular environmental variables influence habitat selection may be as important as the selection of variables themselves. In this study, we combined bivariate scaling and Maximum entropy (Maxent) modeling to investigate multiscale habitat selection of endangered brown bear (Ursus arctos) populations in northwest Spain. Bivariate scaling showed that the strength of apparent habitat relationships was highly sensitive to the scale at which predictor variables are evaluated. Maxent models on the optimal scale for each variable suggested that landscape composition together with human disturbances was dominant drivers of bear habitat selection, while habitat configuration and edge effects were substantially less influential. We found that explicitly optimizing the scale of habitat suitability models considerably improved single-scale modeling in terms of model performance and spatial prediction. We found that patterns of brown bear habitat suitability represent the cumulative influence of habitat selection across a broad range of scales, from local resources within habitat patches to the landscape composition at broader spatial scales.     

Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks

ABSTRACT

The investigation of human activity patterns from location-based social networks like Twitter is an established approach of how to infer relationships and latent information that characterize urban structures. Researchers from various disciplines have performed geospatial analysis on social media data despite the data’s high dimensionality, complexity and heterogeneity. However, user-generated datasets are of multi-scale nature, which results in limited applicability of commonly known geospatial analysis methods. Therefore in this paper, we propose a geographic, hierarchical self-organizing map (Geo-H-SOM) to analyze geospatial, temporal and semantic characteristics of georeferenced tweets. The results of our method, which we validate in a case study, demonstrate the ability to explore, abstract and cluster high-dimensional geospatial and semantic information from crowdsourced data.     

Identifying American Beer Geographies: A Multiscale Core-Cluster Analysis of U.S. Breweries

The recent growth in the U.S. brewing industry is remarkable, and the prevailing number of breweries has not been seen since the late nineteenth century. Several studies have shown that beer-producing facilities are spatially uneven across the United States. These previous studies used spatial units, however, such as metropolitan statistical areas, that might bias conclusions. Using a multiscale core-cluster approach, we explicitly identify where significant agglomerations of brewers are located. Our approach offers two refinements to standard cluster detection methods. First, instead of using fixed spatial boundaries, our method allows us to measure the concentration of brewery point locations across a spectrum of spatial scales. Additionally, our approach enables us to account for important underlying factors that influence the location of beer production. We use point data for all U.S. breweries in 2014. Our results show that the localization of beer production is significant and strongest at small spatial scales and diminishes rapidly with increasing distance, after controlling for population. We map the results to show the spatial variation in brewery agglomeration across the United States.     

Multiple Regressive Pattern Recognition Technique: An Adapted Approach for Improved Georesource Estimation

Multiple Regressive Pattern Recognition Technique (MRPRT) is an adapted approach for improved geologic resource estimation. We developed and tested this approach for the Platinum (Pt) bearing region near Goodnews Bay, Alaska, which presents an example of a complex depositional environment. We applied geospatial and pattern recognition methods to assess the spatial distribution of offshore Pt in the Goodnews Bay area from point data collected by various agencies. We used the coefficient of correlation (r) and the Nash–Sutcliffe efficiency (E) to quantitatively assess the degree of accuracy of the estimated Pt distribution. We split the study area, based on trend analysis, into two regions: inside the Bay and outside the Bay. We could not obtain appreciable estimates from the geospatial and pattern recognition methods. Using MRPRT, we were able to improve r from 0.57 to 0.93 and the E from 28.31 to 92.91 inside the Bay. We achieved improvement in r from 0.55 to 0.61 and E from 28.46 to 34.52 outside the Bay. The reasons for a non-significant improvement outside the Bay have been discussed. The results indicate that the proposed MRPRT has wide application potential in georesource estimation where input data is often scarce.     

Analysing telemetry data with a GIS-based vector structure

Abstract

This paper proposes a unique method for plotting field bearing locations, of the kind typically taken by wildlife biologists on free ranging species, directly on a computer-compatible habitat map. We show how to use a GIS data base to identify differential habitat use directly from the polygon formed by each set of bearings. A geometric algorithm is developed to interpret the bearings accurately. The technique avoids the most difficult errors associated with using point locations, namely those due to animal movement, and distance from receiver to transmitter, and is especially useful for habitat preference studies.     

The Scale Matcher: a procedure for assessing scale compatibility of spatial data and models

It is becoming easier to combine environmental data and models to provide information for problem-solving by environmental policy analysts, decision-makers, and land managers. However, the scale dependencies of each of these (data, model, and problem) can mean that the resulting information is misleading or even invalid. This paper describes the development of a systematic framework (dubbed the ‘Scale Matcher’) for identifying and matching the scale requirements of a problem with the scale limitations of spatial data and models.

The Scale Matcher framework partitions the complex array of scale issues into more manageable components that can be individually quantified. First, the scale characteristics of data, model, and problem are separated into their scale components of extent, accuracy, and precision, and each is associated with suitable metrics. Second, a comprehensive set of pairwise matches between these components is defined. Third, a procedure is devised to lead the user through a process of systematically comparing or matching each scale component. In some cases, the matches are simple comparisons of the relevant metrics. Others require the combination of data variability and model sensitivity to be investigated by randomly simulating data and model imprecision and inaccuracy. Finally, a conclusion is drawn as to the scale compatibility of the Data–Model–Problem trio based on the overall procedure result. Listing the individual match results as a set of scale assumptions helps to draw attention to them, making users more aware of the limitations of spatial modelling.

Application of the Scale Matcher is briefly illustrated with a case study, in which the scale suitability of two sources of soil map data for identifying areas of vulnerability to groundwater pollution was tested. The Scale Matcher showed that one source of soil map data had unacceptable scale characteristics, and the other was marginal for addressing the problem of nitrate leaching vulnerability. The scale-matching framework successfully partitioned the scale issue into a series of more manageable comparisons and gave the user more confidence in the scale validity of the model output.     

Habitat suitability assessment of endangered plant Tetraena mongolica Maxim.

This paper uses Landsat TM images, GIS technology, Digital Elevation and Habitat Assessment Models to assess the habitat suitability of the endangered plant Tetraena mongolica in western Ordos Plateau of China by selecting terrain, soil, climate, and human activity factors as assessment indices. The results are as follows: natural factors such as climate and terrain are not restrictive factors for the survival and development of T. mongolica in the research region, whereas human activity causes habitat quality of T. mongolica to change intensively in quantity and distribution. The area of less suitable habitat increased by 23.87 km² compared to potential habitat suitability. Thus, in some areas, human activity may be a key factor causing the endangerment of T. mongolica. There were obvious differences of potential and practical habitat suitability between different habitat regions in the study area. The habitat quality was better in Wujiamiao, Dishan and Qipanjing regions, and worse in Wuda and Qianlishan regions.     

A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data

Kernel density estimation (KDE) is a classic approach for spatial point pattern analysis. In many applications, KDE with spatially adaptive bandwidths (adaptive KDE) is preferred over KDE with an invariant bandwidth (fixed KDE). However, bandwidths determination for adaptive KDE is extremely computationally intensive, particularly for point pattern analysis tasks of large problem sizes. This computational challenge impedes the application of adaptive KDE to analyze large point data sets, which are common in this big data era. This article presents a graphics processing units (GPUs)-accelerated adaptive KDE algorithm for efficient spatial point pattern analysis on spatial big data. First, optimizations were designed to reduce the algorithmic complexity of the bandwidth determination algorithm for adaptive KDE. The massively parallel computing resources on GPU were then exploited to further speed up the optimized algorithm. Experimental results demonstrated that the proposed optimizations effectively improved the performance by a factor of tens. Compared to the sequential algorithm and an Open Multiprocessing (OpenMP)-based algorithm leveraging multiple central processing unit cores for adaptive KDE, the GPU-enabled algorithm accelerated point pattern analysis tasks by a factor of hundreds and tens, respectively. Additionally, the GPU-accelerated adaptive KDE algorithm scales reasonably well while increasing the size of data sets. Given the significant acceleration brought by the GPU-enabled adaptive KDE algorithm, point pattern analysis with the adaptive KDE approach on large point data sets can be performed efficiently. Point pattern analysis on spatial big data, computationally prohibitive with the sequential algorithm, can be conducted routinely with the GPU-accelerated algorithm. The GPU-accelerated adaptive KDE approach contributes to the geospatial computational toolbox that facilitates geographic knowledge discovery from spatial big data.     

The Regionalization of Africa: Delineating Africa's Subregions Using Airline Data

Abstract

Current regionalizations of Africa have limitations in that they are attribute-based and regions are delineated according to national boundaries. Taking the world city network approach as starting point, it is possible to use relational data (i.e., information about the relationships between cities) rather than attribute data, and moreover, it becomes possible to ignore state boundaries by delineating the regions based on the location of the interaction structure between cities. This research uses airline data. A network analysis is performed on the number of passengers who fly between cities in Africa. A subregional map is created based on the results.