Analysis of anomalies in ADS-B and its GPS data

Traditionally, the surveillance component of the air traffic management system has been based on radar, which consists of two separate systems: primary radar and secondary radar, which both enable the measurement of the aircraft range and bearing to the radar station. Primary radar is based on signals emitted by a ground station simply being reflected off an object and detected by a ground-based receiver. Secondary radar also emits signals, but relies upon a transponder onboard the aircraft to emit a signal itself, modulated among others by a four-digit aircraft identity (Mode A), aircraft altitude (Mode C) and/or 24-bit unique address (Mode S). Typical accuracies of secondary radar are of the order of 0.03 NM in range and 0.07° in azimuth. However, no position integrity report is provided. Air traffic density is expected to significantly increase in the future. In order to maintain or enhance air travel efficiency, while maintaining safety, more accurate surveillance systems, with the required integrity, will be required. Automatic dependent surveillance–broadcast (ADS-B) is a new aviation surveillance system, envisioned to overcome the limitations of radar and to enhance surveillance performance and thereby increase airspace capacity. However, its high dependence on external systems such as onboard navigation and communication systems also increases the number of potential points of failure. It is important to understand and mitigate these failure modes before the system can reliably be implemented. The present study emerged as an exploratory research as part of a safety assessment framework development for the ADS-B system. It reviews the ADS-B failure modes, data collection and analysis of ADS-B and its corresponding onboard GPS data. The study identifies a set of failures common to certain aircraft models, with consistent error patterns. A key failure mode was found to be associated with the navigation data from the onboard GPS. We discuss the identified failure modes and investigate the nature and causes of these failures. The findings highlight some of the deficiencies of the current ADS-B system, which will need to be addressed before the ADS-B system can reliably be implemented.     

An integrated land vehicle navigation system based on context awareness

In the complex urban environments, land vehicle navigation purely relying on GNSS cannot satisfy user needs due to the loss of satellite signals caused by obstructions such as buildings, tunnels, and trees. To solve this problem, we introduce a GPS-/MSINS-/magnetometer-integrated urban navigation system based on context awareness. In this system, the data from the Micro Strapdown Inertial Navigation System (MSINS) are used to analyze and detect the context knowledge of vehicles, whose sensor errors can be compensated by the heuristic drift reduction algorithm for different motion situations. When GPS is available, the vehicle position can be estimated by unscented Kalman Filter, whereas in the case of GPS outages, the vehicle attitude is provided by an attitude and heading reference system and the motion constraints-aided algorithm is used to complete the positioning. In the experiment validation, the integrated navigation system is set up by low-cost inertial sensors. The result shows that the proposed system can achieve high accuracy when GPS is available. For most of the time without GPS, the system can guarantee the positioning precision of 10 m and compensate the errors of MSINS effectively, which fully satisfies positioning needs in complex urban environments.     

Knowledge Transfer for Large‐Scale Urban Growth Modeling Based on Formal Concept Analysis

Cellular automata (CA) are useful for studies on urban growth and land‐use changes. Although various methods have been developed to define transition rules, modeling urban growth of large areas remains a tough challenge owing to heterogeneous geographical features. To address the problem, we present a novel method based on the combination of Formal Concept Analysis (FCA) and knowledge transfer techniques. FCA is used to solicit association rules among cities within a large area. This method can provide a theoretical basis for the knowledge transfer process. A cutting‐edge algorithm called TrAdaBoost is then integrated with the commonly‐used Logistic‐CA as the modeling framework. The proposed method is applied to the urban growth modeling of Guangdong Province, a large region with 21 cities in China, from 2005 to 2008. Compared with traditional methods, this method can achieve better results at the provincial and local levels, according to the experiments. The combination of FCA and knowledge transfer is expected to provide a useful tool for calibrating large‐scale urban CA models.     

Classifying Human Activity Patterns from Smartphone Collected GPS data: A Fuzzy Classification and Aggregation Approach

Smartphones have emerged as a promising type of equipment for monitoring human activities in environmental health studies. However, degraded location accuracy and inconsistency of smartphone‐measured GPS data have limited its effectiveness for classifying human activity patterns. This study proposes a fuzzy classification scheme for differentiating human activity patterns from smartphone‐collected GPS data. Specifically, a fuzzy logic reasoning was adopted to overcome the influence of location uncertainty by estimating the probability of different activity types for single GPS points. Based on that approach, a segment aggregation method was developed to infer activity patterns, while adjusting for uncertainties of point attributes. Validations of the proposed methods were carried out based on a convenient sample of three subjects with different types of smartphones. The results indicate desirable accuracy (e.g. up to 96% in activity identification) using of this method. Two examples are provided in the Appendix to illustrate how the proposed methods could be applied in environmental health studies. Researchers could tailor this scheme to fit a variety of research topics.     

Testing block subdivision algorithms on block designs

Integrated land use–transportation models predict future transportation demand taking into account how households and firms arrange themselves partly as a function of the transportation system. Recent integrated models require parcels as inputs and produce household and employment predictions at the parcel scale. Block subdivision algorithms automatically generate parcel patterns within blocks. Evaluating block subdivision algorithms is done by way of generating parcels and comparing them to those in a parcel database. Three block subdivision algorithms are evaluated on how closely they reproduce parcels of different block types found in a parcel database from Montreal, Canada. While the authors who developed each of the algorithms have evaluated them, they have used their own metrics and block types to evaluate their own algorithms. This makes it difficult to compare their strengths and weaknesses. The contribution of this paper is in resolving this difficulty with the aim of finding a better algorithm suited to subdividing each block type. The proposed hypothesis is that given the different approaches that block subdivision algorithms take, it’s likely that different algorithms are better adapted to subdividing different block types. To test this, a standardized block type classification is used that consists of mutually exclusive and comprehensive categories. A statistical method is used for finding a better algorithm and the probability it will perform well for a given block type. Results suggest the oriented bounding box algorithm performs better for warped non-uniform sites, as well as gridiron and fragmented uniform sites. It also produces more similar parcel areas and widths. The Generalized Parcel Divider 1 algorithm performs better for gridiron non-uniform sites. The Straight Skeleton algorithm performs better for loop and lollipop networks as well as fragmented non-uniform and warped uniform sites. It also produces more similar parcel shapes and patterns.     

Exploring the spatially varying innovation capacity of the US counties in the framework of Griliches’ knowledge production function: a mixed GWR approach

Griliches’ knowledge production function has been increasingly adopted at the regional level where location-specific conditions drive the spatial differences in knowledge creation dynamics. However, the large majority of such studies rely on a traditional regression approach that assumes spatially homogenous marginal effects of knowledge input factors. This paper extends the authors’ previous work (Kang and Dall’erba in Int Reg Sci Rev, 2015. doi: 10.1177/0160017615572888) to investigate the spatial heterogeneity in the marginal effects by using nonparametric local modeling approaches such as geographically weighted regression (GWR) and mixed GWR with two distinct samples of the US Metropolitan Statistical Area (MSA) and non-MSA counties. The results indicate a high degree of spatial heterogeneity in the marginal effects of the knowledge input variables, more specifically for the local and distant spillovers of private knowledge measured across MSA counties. On the other hand, local academic knowledge spillovers are found to display spatially homogenous elasticities in both MSA and non-MSA counties. Our results highlight the strengths and weaknesses of each county’s innovation capacity and suggest policy implications for regional innovation strategies.     

Geographical impacts on social networks from perspectives of space and place: an empirical study using mobile phone data

Space and place are two fundamental concepts in geography. Geographical factors have long been known as drivers of many aspects of people’s social networks. But whether and how space and place affect social networks differently are still unclear. The widespread use of location-aware devices provides a novel source for distinguishing the mechanisms of their impacts on social networks. Using mobile phone data, this paper explores the effects of space and place on social networks. From the perspective of space, we confirm the distance decay effect in social networks, based on a comparison between synthetic social ties generated by a null model and actual social ties derived from real-world data. From the perspective of place, we introduce several measures to evaluate interactions between individuals and inspect the trio relationship including distance, spatio-temporal co-occurrence, and social ties. We found that people’s interaction is a more important factor than spatial proximity, indicating that the spatial factor has a stronger impact on social networks in place compared to that in space. Furthermore, we verify the hypothesis that interactions play an important role in strengthening friendships.     

A support vector machine classifier based on a new kernel function model for hyperspectral data

The kernel function is a key factor to determine the performance of a support vector machine (SVM) classifier. Choosing and constructing appropriate kernel function models has been a hot topic in SVM studies. But so far, its implementation can only rely on the experience and the specific sample characteristics without a unified pattern. Thus, this article explored the related theories and research findings of kernel functions, analyzed the classification characteristics of EO-1 Hyperion hyperspectral imagery, and combined a polynomial kernel function with a radial basis kernel function to form a new kernel function model (PRBF). Then, a hyperspectral remote sensing imagery classifier was constructed based on the PRBF model, and a genetic algorithm (GA) was used to optimize the SVM parameters. On the basis of theoretical analysis, this article completed object classification experiments on the Hyperion hyperspectral imagery of experimental areas and verified the high classification accuracy of the model. The experimental results show that the effect of hyperspectral image classification based on this PRBF model is apparently better than the model established by a single global or local kernel function and thus can greatly improve the accuracy of object identification and classification. The highest overall classification accuracy and kappa coefficient reached 93.246% and 0.907, respectively, in all experiments.     

Spatial representation in the social interaction potential metric: an analysis of scale and parameter sensitivity

The social interaction potential (SIP) metric measures urban structural constraints on social interaction opportunities of a metropolitan region based on the time geographic concept of joint accessibility. Previous implementations of the metric used an interaction surface based on census tracts and the locations of their centroids. This has been shown to be a shortcoming, as the metric strongly depends on the scale of the zoning system in the region, making it difficult to compare the SIP metric between metropolitan regions. This research explores the role of spatial representation in the SIP metric and identifies a suitable grid-based representation that allows for comparison between regions while retaining cost-effectiveness with respect to computational burden. We also report on findings from an extensive sensitivity analysis investigating the SIP metric’s input parameters such as a travel flow congestion factor and the length of the allowable time budget for social activities. The results provide new insights on the role of the modifiable areal unit problem in the computation of time geographic measures of accessibility.     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.