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1.
ABSTRACT

The objective of this paper is to investigate uncertainties surrounding relationships between spatial autocorrelation (SA) and the modifiable areal unit problem (MAUP) with an extensive simulation experiment. Especially, this paper aims to explore how differently the MAUP behaves for the level of SA focusing on how the initial level of SA at the finest spatial scale makes a significant difference to the MAUP effects on the sample statistics such as means, variances, and Moran coefficients (MCs). The simulation experiment utilizes a random spatial aggregation (RSA) procedure and adopts Moran spatial eigenvectors to simulate different SA levels. The main findings are as follows. First, there are no substantive MAUP effects for means. However, the initial level of SA plays a role for the zoning effect, especially when extreme positive SA is present. Second, there is a clear and strong scale effect for the variances. However, the initial SA level plays a non-negligible role in how this scale effect deploys. Third, the initial SA level plays a crucial role in the nature and extent of the MAUP effects on MCs. A regression analysis confirms that the initial SA level makes a substantial difference to the variability of the MAUP effects.  相似文献   

2.
The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natural clustering structure. Statistically evaluating the significance of the degree of homogeneity within a single spatial cluster is difficult. To overcome this limitation, this study develops a permutation test approach Specifically, the homogeneity of a spatial cluster is measured based on the local variance and cluster member permutation, and two-stage permutation tests are developed to determine the significance of the degree of homogeneity within each spatial cluster. The proposed permutation tests can be integrated into the existing spatial clustering algorithms to detect homogeneous spatial clusters. The proposed tests are compared with four existing tests (i.e., Park’s test, the contiguity-constrained nonparametric analysis of variance (COCOPAN) method, spatial scan statistic, and q-statistic) using two simulated and two meteorological datasets. The comparison shows that the proposed two-stage permutation tests are more effective to identify homogeneous spatial clusters and to determine homogeneous clustering structures in practical applications.  相似文献   

3.
ABSTRACT

We argue that the use of American Community Survey (ACS) data in spatial autocorrelation statistics without considering error margins is critically problematic. Public health and geographical research has been slow to recognize high data uncertainty of ACS estimates, even though ACS data are widely accepted data sources in neighborhood health studies and health policies. Detecting spatial autocorrelation patterns of health indicators on ACS data can be distorted to the point that scholars may have difficulty in perceiving the true pattern. We examine the statistical properties of spatial autocorrelation statistics of areal incidence rates based on ACS data. In a case study of teen birth rates in Mecklenburg County, North Carolina, in 2010, Global and Local Moran’s I statistics estimated on 5-year ACS estimates (2006–2010) are compared to ground truth rate estimates on actual counts of births certificate records and decennial-census data (2010). Detected spatial autocorrelation patterns are found to be significantly different between the two data sources so that actual spatial structures are misrepresented. We warn of the possibility of misjudgment of the reality and of policy failure and argue for new spatially explicit methods that mitigate the biasedness of statistical estimations imposed by the uncertainty of ACS data.  相似文献   

4.
In the context of OpenStreetMap (OSM), spatial data quality, in particular completeness, is an essential aspect of its fitness for use in specific applications, such as planning tasks. To mitigate the effect of completeness errors in OSM, this study proposes a methodological framework for predicting by means of OSM urban areas in Europe that are currently not mapped or only partially mapped. For this purpose, a machine learning approach consisting of artificial neural networks and genetic algorithms is applied. Under the premise of existing OSM data, the model estimates missing urban areas with an overall squared correlation coefficient (R 2) of 0.589. Interregional comparisons of European regions confirm spatial heterogeneity in the model performance, whereas the R 2 ranges from 0.129 up to 0.789. These results show that the delineation of urban areas by means of the presented methodology depends strongly on location.  相似文献   

5.
Abstract

This paper reports on software to construct alternative weight matrices and to compute spatial autocorrelation statistics, namely the Moran coefficient and the Geary coefficient using Arc/Info’s data structure. As such it is an addition to recent efforts in linking GIS with exploratory spatial data analysis. The software is interfaced with Arc/Info via the Arc Macro Language (AML) so that it can be run in the ARC environment. This allows the user to perform exploratory analysis within GIS which may provide insights in subsequent spatial analysis and modelling.  相似文献   

6.
ABSTRACT

Habitat selection analysis is a widely applied statistical framework used in spatial ecology. Many of the methods used to generate movement and couple it with the environment are strongly integrated within GIScience. The choice of movement conceptualisation and environmental space can potentially have long-lasting implications on the spatial statistics used to infer movement–environment relationships. The aim of this study was to explore how systematically altering the conceptualisation of movement, environmental space and temporal resolution affects the results of habitat selection analyses using both real-world case studies and a virtual ecologist approach. Model performance and coefficient estimates did not differ between the finest conceptualisations of movement (e.g. vector and move), while substantial differences were found for the more aggregated representations (e.g. segment and area). Only segments modelled the expected movement–environment relationship with increasing linear feature resistance in the virtual ecologist approach and altering the temporal resolution identified inversions in the movement–environment relationship for vectors and moves. The results suggest that spatial statistics employed to investigate movement–environment relationships should advance beyond conceptualising movement as the (relatively) static conceptualisation of vectors and moves and replace these with (more) dynamic aggregations of longer-lasting movement processes such as segments and areal representations.  相似文献   

7.
Daily solar radiation estimates of four up‐to‐date solar radiation models (Solar Analyst, r.sun, SRAD and Solei‐32), based on a digital elevation model (DEM), have been evaluated and compared in a Mediterranean environment characterized by a complex topography. The models' estimates were evaluated against 40 days of radiometric data collected in 14 stations. Analyzed sky conditions ranged from completely overcast conditions to clear skies. Additionally, the role of the spatial resolution of the DEM has been evaluated through the use of two different resolutions: 20 and 100 m. Results showed that, under clear‐sky conditions, the daily solar radiation variability in the study area may be reasonably estimated with mean bias errors under 10% and root mean square error values of around 15%. On the other hand, results proved that the reliability of the estimates substantially decreases under overcast conditions for some of the solar radiation models. Regarding the role of the DEM spatial resolution, results suggested that the reliability of the estimates for complex topography areas under clear‐sky conditions improves using a higher spatial resolution.  相似文献   

8.
Abstract

Error and uncertainty in spatial databases have gained considerable attention in recent years. The concern is that, as in other computer applications and, indeed, all analyses, poor quality input data will yield even worse output. Various methods for analysis of uncertainty have been developed, but none has been shown to be directly applicable to an actual geographical information system application in the area of natural resources. In spatial data on natural resources in general, and in soils data in particular, a major cause of error is the inclusion of unmapped units within areas delineated on the map as uniform. In this paper, two alternative algorithms for simulating inclusions in categorical natural resource maps are detailed. Their usefulness is shown by a simplified Monte Carlo testing to evaluate the accuracy of agricultural land valuation using land use and the soil information. Using two test areas it is possible to show that errors of as much as 6 per cent may result in the process of land valuation, with simulated valuations both above and below the actual values. Thus, although an actual monetary cost of the error term is estimated here, it is not found to be large.  相似文献   

9.
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10.
《The Journal of geography》2012,111(5):169-180
ABSTRACT

Research in the cognition and learning sciences has demonstrated that the human brain contains basic structures whose functions are to perform a variety of specific spatial reasoning tasks and that children are capable of learning basic spatial concepts at an early age. There has been a call from within geography to recognize research on spatial cognition in a meaningful way in primary school curriculum. This article utilizes the spatial thinking taxonomy proposed by Gersmehl and Gersmehl (2006) to examine to the extent to which spatial thinking concepts are being practiced in U.S. schools. The National Geography Standards and forty-nine state social studies or geography standards are examined. Using standards as a measure of geography content, it is concluded that while some of spatial thinking concepts appear often in curriculum, others are largely absent. Designing geography standards that address the findings of spatial cognition research may serve as a means of improving geography instruction.  相似文献   

11.
A locational error model for spatial features in vector-based geographical information systems (GIS) is proposed in this paper. Using error in points as the fundamental building block, a stochastic model is constructed to analyse point, line, and polygon errors within a unified framework, a departure from current practices which treat errors in point and line separately. The proposed model gives, as a special case, the epsilon band model a true probabilistic meaning. Moreover, the model can also be employed to derive accuracy standards and cartographic estimates in GIS.  相似文献   

12.
ABSTRACT

Online mapping providers offer unprecedented access to spatial data and analytical tools; however, the number of analytical queries that can be requested is usually limited. As such, Volunteered Geographic Information (VGI) services offer a viable alternative, provided that the quality of the underlying spatialtheir data is adequate. In this paper, we evaluate the agreement in travel impedance between estimates from MapQuest Open, which embraces OpenStreetMap (OSM) data–a is based on VGI datasetfrom OpenStreetMap (OSM), and estimates from two other popular commercial providers, namely Google Maps? and ArcGIS? Online. Our framework is articulated around three components, which simulates potentialcalculates shortest routes, estimates their travel impedance using a routing service Application Program Interface (API), and extracts the average number of contributors for each route. We develop an experimental setup with a simulated dataset for the state of North Carolina. Our results suggest a strong correlation of travel impedance among all three road network providers. and that travel impedanceThe agreement is the greatest in areas with a denser road network and the smallest for routes of shorter distances. Most importantly, tTravel estimates from MapQuest Open are nearly identical to both commercial providers when the average number of OSM contributors along the route is larger. The latter finding contributes to a growing body of literature on Linus’s law, recognizing that a larger group of contributors holds the potential to validate and correct inherent errors to the source dataset.  相似文献   

13.
Spatial data uncertainty models (SDUM) are necessary tools that quantify the reliability of results from geographical information system (GIS) applications. One technique used by SDUM is Monte Carlo simulation, a technique that quantifies spatial data and application uncertainty by determining the possible range of application results. A complete Monte Carlo SDUM for generalized continuous surfaces typically has three components: an error magnitude model, a spatial statistical model defining error shapes, and a heuristic that creates multiple realizations of error fields added to the generalized elevation map. This paper introduces a spatial statistical model that represents multiple statistics simultaneously and weighted against each other. This paper's case study builds a SDUM for a digital elevation model (DEM). The case study accounts for relevant shape patterns in elevation errors by reintroducing specific topological shapes, such as ridges and valleys, in appropriate localized positions. The spatial statistical model also minimizes topological artefacts, such as cells without outward drainage and inappropriate gradient distributions, which are frequent problems with random field-based SDUM. Multiple weighted spatial statistics enable two conflicting SDUM philosophies to co-exist. The two philosophies are ‘errors are only measured from higher quality data’ and ‘SDUM need to model reality’. This article uses an automatic parameter fitting random field model to initialize Monte Carlo input realizations followed by an inter-map cell-swapping heuristic to adjust the realizations to fit multiple spatial statistics. The inter-map cell-swapping heuristic allows spatial data uncertainty modelers to choose the appropriate probability model and weighted multiple spatial statistics which best represent errors caused by map generalization. This article also presents a lag-based measure to better represent gradient within a SDUM. This article covers the inter-map cell-swapping heuristic as well as both probability and spatial statistical models in detail.  相似文献   

14.
Abstract

Recent developments in theory and computer software mean that it is now relatively straightforward to evaluate how attribute errors are propagated through quantitative spatial models in GIS. A major problem, however, is to estimate the errors associated with the inputs to these spatial models. A first approach is to use the root mean square error, but in many cases it is better to estimate the errors from the degree of spatial variation and the method used for mapping. It is essential to decide at an early stage whether one should use a discrete model of spatial variation (DMSV—homogeneous areas, abrupt boundaries), a continuous model (CMSV—a continuously varying regionalized variable field) or a mixture of both (MMSV—mixed model of spatial variation). Maps of predictions and prediction error standard deviations are different in all three cases, and it is crucial for error estimation which model of spatial variation is used. The choice of model has been insufficiently studied in depth, but can be based on prior information about the kinds of spatial processes and patterns that are present, or on validation results. When undetermined it is sensible to adopt the MMSV in order to bypass the rigidity of the DMSV and CMSV. These issues are explored and illustrated using data on the mean highest groundwater level in a polder area in the Netherlands.  相似文献   

15.
The overuse of cesarean sections (C-sections) in the United States is a contested issue. The rate of C-section births in 2015 at 32 percent was over double the World Health Organization recommendation of 10 to 15 percent. We employed spatial statistical methods and data visualization techniques to assess the temporal and spatial trends in C-section rates by county across the United States. Although the national rate of C-section remained stable at the beginning and end of this study period, an increase in rates from 1997 to 2009 was reflected simultaneously in national, state, and individual county rates. Local indicators of spatial dependence did not show spatial clustering as being connected to, or driving, the change, yet the visualization methods used here show details on individual county deviance from local temporal trends. By highlighting counties that do not follow the trends of their neighbors, we identify exceptional locations that could help further the study of the determinants of changing C-section rates in the United States. Key Words: cesarean sections, exploratory spatial data analysis, medical geography, spatial statistics.  相似文献   

16.
Abstract

Kriging is an optimal method of spatial interpolation that produces an error for each interpolated value. Block kriging is a form of kriging that computes averaged estimates over blocks (areas or volumes) within the interpolation space. If this space is sampled sparsely, and divided into blocks of a constant size, a variable estimation error is obtained for each block, with blocks near to sample points having smaller errors than blocks farther away. An alternative strategy for sparsely sampled spaces is to vary the sizes of blocks in such away that a block's interpolated value is just sufficiently different from that of an adjacent block given the errors on both blocks. This has the advantage of increasing spatial resolution in many regions, and conversely reducing it in others where maintaining a constant size of block is unjustified (hence achieving data compression). Such a variable subdivision of space can be achieved by regular recursive decomposition using a hierarchical data structure. An implementation of this alternative strategy employing a split-and-merge algorithm operating on a hierarchical data structure is discussed. The technique is illustrated using an oceanographic example involving the interpolation of satellite sea surface temperature data. Consideration is given to the problem of error propagation when combining variable resolution interpolated fields in GIS modelling operations.  相似文献   

17.
Vaccination rates in Illinois schools are decreasing as more parents opt for nonmedical exemptions (NMEs). At the local scale, higher levels of exemptions affect herd immunity levels. Few studies have previously conducted or proposed methods to conduct local-scale spatial and temporal cluster pattern analysis. This study used vaccination exemption data from the Illinois School Board of Education’s annual Immunization School Survey for the 2003–2004 and 2013–2014 academic years. The Getis–Ord General G statistic was used to identify cluster detection by individual vaccine at the school level. The Getis–Ord Gi* statistic was used with two different parameter models to identify hot and cold spots. This study found that NMEs are highly clustered. More clusters of high and low NMEs were identified for the 2013–2014 academic year than for 2003–2004. The percentages of schools that were neither hot nor cold averaged 94.0 percent for the 2003–2004 school year and 78.7 percent for the 2013–2014 school year. NME rates in Illinois are rising. The increase in hot and cold spots is evidence that the polarity of vaccination choice is growing. As vaccination exemption rates continue to polarize U.S. society, it is essential for public health efforts to monitor and conduct local-level studies. Key Words: antivaccination, hot spot analysis, medical geography, nonmedical exemption, spatial statistics.  相似文献   

18.

Vector data are not uncommon in geography, and include examples such as transportation flows, particulate transport, and cartographic distortion. The directional and vector means and variances of these types of data are easily computed using a complex-arithmetic extension of the equations for scalar mean and variance. The January surface wind field over the contiguous United States provides an example with which to compare the information provided by scalar, directional and vector-based statistics. Spatial patterns of the mean and variance of January wind velocity (the wind vector) resemble patterns of wind speeds and directions but are not a simple superposition of the two, and one cannot necessarily infer the nature of the velocity field from separately computed salar and directional statistics. However, scalar and directional means and variances can lend insight into the features contributing to the velocity mean and variance. Scalar, directional, and vector-based analyses thus provide complementary methods with which to examine the spatial patterns of wind, or of any flow field that can be represented as a vector.  相似文献   

19.
Economic Notes     
Abstract

This article introduces the development and validation of the spatial thinking ability test (STAT). The STAT consists of sixteen multiple-choice questions of eight types. The STAT was validated by administering it to a sample of 532 junior high, high school, and university students. Factor analysis using principal components extraction was applied to identify underlying spatial thinking components and to evaluate the construct validity of the STAT. Spatial components identified through factor analysis only partly coincided with spatial concepts used to develop the questions that compose the STAT and with the components of spatial thinking hypothesized by other researchers.  相似文献   

20.
The analysis of local spatial autocorrelation for spatial attributes has been an important concern in geographical inquiry. In this paper, we propose a concept and algorithm of k-order neighbours based on Delaunay's triangulated irregular networks and redefine Getis and Ord's (1992) local spatial autocorrelation statistic as Gi(k) with weight coefficient wij(k) based on k-order neighbours for the study of local patterns in spatial attributes. To test the validity of these statistics, an experiment is performed using spatial data of the elderly population in Ichikawa City, Chiba Prefecture, Japan. The difference between the weight coefficients of the k-order neighbours and distance parameter to measure the spatial proximity of districts located in the city centre and near the city limits is found by Monte-Carlo simulation.  相似文献   

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