Spatial Information

ture maps spatially from the last 54% layers at each training step. Interestingly, the test accuracy only drops 2.66% and the training process is nearly identical to the standard VGG- 16. This observation generalizes to various CNN architec- tures: removing spatial information from the last 30% lay- ers gives a surprisingly little performance decrease within 1% across architectures and datasets, and the performance decrease is still within 7% even if the last 50% layers are manipulated. This indicates that spatial information is over- rated for standard CNNs and not necessary to reach com- petitive performances. Finally, our investigation on the de- tection task shows that although the unavailability of spatial information at later layers does harm the localization of the model, the impact is not as fatal as expected; at the same time, the classification ability of the model is not affected.

spatial information. In practice, the spatial information may suffer from several uncertainties owing to steering angle mismatch, random perturbations in array sensor positions, and mutual coupling between array sensors. These three spatial uncertainties are referred to as the SRM uncertainties, where SRM is an abbreviation for the steering angle mismatch, random perturbations in array sensor positions, and mutual coupling between array sensors. Each of the SRM uncertainties results in a mismatch between the presumed and the actual direction vectors for the desired signals. The performance of an antenna array beamformer is significantly degraded by even a small mismatch [6].

Semantic similarity measurement is the practice of estimating the relatedness of the concept based on the likeness of their meaning or their semantic content. Today’s increasing interest on the geospatial information system, leads to development of the query system which needs to provide efficient geo-spatial information retrieval. The properties and spatial relations between the geo-spatial concepts must be taken into account for retrieving geo-spatial information efficiently. This paper provides the survey on various models such as geometric model, network model, transformation model, hybrid model, etc, for discovering the semantic similarity between the geo-spatial concepts. It also focuses the drawbacks of each semantic similarity model and depicts how the hybrid model works well when compared with other models in retrieving the Geo-spatial information efficiently. The main objective of this paper is to propose Hybrid semantic similarity model using Manhattan distance and by considering contexts of the Geo-spatial concepts. The Manhattan distance method is used to estimate the semantic distance between the Geo-spatial concepts and to retrieve the Geo-spatial information efficiently. The position of the locations is identified by using the map similarity.

Later, Sathiamoorthy [20] represented the BDIP and BVLC features using low and higher order statistics i.e., mean standard deviation, skewness and kurtosis and reported that his approach is superior to [19]. Afterward, Sathiamoorthy [21] represented the BDIP and BVLC features using a histogram of 57 bins for each respectively, which is robustness to noise and considerably better than the BDIP and BVLC moments presented in [19,20,21]. However, the method of representing the BDIP and BVLC feature in [21] fails to capture the spatial information of edge and valleys and texture characteristics. As the spatial information plays a notable role in texture analysis, image retrieval and classification, in this paper, a novel scheme for texture feature characterization and discrimination is presented and it captures the global distribution of local spatial correlations between the identical BDIP values as well as for BVLC values also. The Canberra distance measure [21, 22] is used as a measure of similarity. The precision and recall [22] is employed to measure the performance of the proposed novel representation of BDIP and BVLC.

The overall technical framework of power grid spatial information security management is divided into data layer, logic layer and presentation layer [4]. The data layer is a data object (management policy data and system log data) generated by the EPGIS data objects (basic geographic data and grid graphical data) and data protection tools that need to be protected. The logical layer is established on the basis of the data access layer Of the various functional components to achieve the various functions of data security; presentation layer is displayed to the user's application tools, the system's manifestations will be EPGIS platform C / S client combination, including acquisition, processing, application of protective tools And configuration tools. Specific technical architecture as follows:

still remains elusive [see Michelsen (Michelsen, 1999) for a comprehensive account of the current hypotheses]. One reason is probably to be found in the striking variability of the multiple dance signals. The body contacts between dancers and followers most likely convey meaningful stimulation to potential recruits (e.g. von Frisch, 1967; Bozic and Valentincic, 1991; Rohrseitz and Tautz, 1999), and the same can be said about chemical cues, both environmental (von Frisch, 1967) and semiochemicals (Thom et al., 2007), coupled to a dancer’s wagging movements. Three dimensional fields of particle oscillations produced by the dancers’ vibrating wings (Michelsen et al., 1987; Kirchner and Towne, 1994; Michelsen, 2003) and substrate-borne vibrations caused by their wagging movements (Tautz, 1996) also appear to be a source of meaningful stimulation. However, the question of how a dancer’s behaviour is mapped to that of its followers remains open, most probably as a result of the use of sub-optimal methods to trace the behaviour of dancers and followers, both inside and outside the hive. Furthermore, the study of a dancer’s manoeuvres and a follower’s response to the dance have long been addressed either independently (e.g. Towne and Gould, 1988; Riley et al., 2005) or without pondering the relative influence of simultaneous, guiding cues (e.g. von Frisch and Lindauer, 1961), e.g. olfactory cues that followers use to pinpoint their targets. [An exception here is the work by Esch (Esch, 2001) and colleagues, which simultaneously addressed the encoding of distance information in the dance and the distribution of field searches by recruits.] In addition, the influence of a follower’s experience on the process of decoding information in the dance remains obscure. Currently, unemployed foragers appear to follow no more than a few waggle phases before resuming their flights to natural goals, and they do so by following those seemingly indicating familiar sites (Biesmeijer and Seeley, 2005). Such a small number of dancing events provides spatial information only roughly to a human observer (De Marco and Menzel, 2008), thereby posing the question of how informative (Haldane and Spurway, 1954) such a ‘sample’ can be to the followers. This is probably the reason why a distinction between experienced and novice foragers would prove fruitful for a deeper understanding of the dance communication system.

Spatial information plays an important role in many social, environmental, economic and political decisions and is increasingly acknowledged as a national resource essential for wider societal benefits. Natural Resource Management (NRM) is one area where spatial information can be used for improved planning and decision- making processes. Traditionally, state government organisations and mapping agencies have been the custodians of spatial information necessary for catchment management. Recent developments in Information Communication Technology (ICT) tools and spatial technology have provided community groups and grass-root citizens with no prior experience in spatial technology with a new opportunity to collect and manage spatial information. With these opportunities, regional NRM bodies in Australia are collecting a significant amount of property and catchment scale spatial information. The access and sharing of spatial information between state government agencies and regional NRM bodies is therefore emerging as an important issue for sub-national spatial data infrastructure (SDI) development.

Catchment management is an approach to managing natural resources using river catchment systems as the unit of management (Commonwealth of Australia 2000). It involves integrating and managing ecological, economic and social aspects of land, water and biodiversity resources around an identified catchment system. Catchment management issues are characterised by multiple stakeholders and multiple goals which cut across traditional as well as administrative boundaries (Love et al 2006). Catchment management requires an integrated management approach as different institutions and individuals need to work together towards sustainable catchment outcomes (Paudyal and McDougall 2008). Effective institutional arrangements and technical solutions are needed to bring different organisations together for catchment management. Spatial information is recognised as an essential resource that supports the economic, social and environmental interests of a nation, and is one of the most critical elements underpinning decision-making for many discipline (Clinton 1994; Gore 1998; Rajabifard et al 2003a) including catchment management. With different organisations under different jurisdictions working towards catchment management, the access, use and sharing of spatial information to support multi-stakeholder decision-making processes and policy development continues to be problematic. The development of a Spatial Data Infrastructure (SDI) facilitates the exchange and sharing of spatial information between stakeholders within the spatial community (Feeney et al 2001; McDougall 2006). Current SDI initiatives focus on SDI development at different administrative/political levels ranging from local to state/provincial, national, regional and global levels (Chan and Williamson 1999; Rajabifard and Williamson 2001). However, catchment management does not follow the rules of administrative/political hierarchies as it has its own spatial extent and coverage. Therefore, to successfully address catchment management objectives, SDI frameworks must carefully consider the institutional arrangements and the needs of the various stakeholders across these catchment environments (Paudyal and McDougall 2008).

In areas of access and pricing larger councils seem to have made significant progress. Local governments’ ability to integrate and educate users across their organisations appears to have cemented spatial data as a critical business data set and GIS as a corporate tool. Most local governments rely on their internal spatial data sets more than the external data that they may acquire, so the benefits for involvement in data sharing must be substantial and clearly articulated to gain their involvement. Initial responses indicate that councils receive regular requests for spatial data with many requests being ambiguous and often from the same state government agency. Issues such as cost recovery, liability, privacy, copyright, training and resources are common to many local governments dealing with spatial information management but are increasingly not considered as critical issues.

6.2. Sparse Data Example. Crimes and other types of events may be quite sparse in a given geographical region. Conse- quently, it becomes di ffi cult to determine the probability that an event will occur in the area. It is challenging for density estimation methods that do not incorporate the spatial information to distinguish between invalid regions and areas that have not had any crimes but are still likely to have events. Using the same predefined probability density from Section 1 in Figure 1(b), we demonstrate how our methods maintain these invalid regions for sparse data. The 40 events selected are shown in Figure 3(b). The density estimates for current methods and our methods are given in Figure 3. We used a variance σ = 15 for the Gaussian Kernel Density Estimate.

To extent these findings, Hakala (1999) examined the conditions under which readers focus on spatial information other than simply the location o f the protagonist under relatively natural reading conditions. To test this hypothesis Hakala (1999) generated ten passages describing a protagonist moving through a spatial environment (see Table 8). Each passage began with an introduction that described the initial spatial location o f the protagonist and a spatial description of the particular target locations followed by filler material and a relocation sentence that shifted the protagonist back to a target location without explicitly rementioning the location. Following each passage, participants were presented with a probe word to be named. The probe was either the final spatial location o f the protagonist or a neutral word that was close to the spatially associated probe in the text’s surface structure but did not contain spatial information. Prior to the start o f the experiment, half the participants were told that, while reading each text, they were to pay attention to the spatial details of the text while the remaining half were told that answering the comprehension question was the most important component o f the experiment.

A key area in which spatial information and new spatial technologies such as GIS have potential impact are better control of spread of disease. Medical entomology research focussing on mosquitoes in the Northern Territory may yield some longer term benefits in controlling disease outbreaks there. Spatial information is also central to more accurate research of the impact of air pollution across urban populations in particular. Some impact modelling has been carried out at the National Centre for Epidemiology and Population Health which shows anticipated changes in the regional distribution of disease, particularly as a consequence of climate change. Another area of research focuses on spatial correlation and clustering, although almost all instances in which disease clusters have previously been suspected have subsequently been attributed to chance.

This paper applies ISO-Space to the annotation of non-textual data such as maps and figures or even some textual data presented in a tabular form be- cause spatial information is very often carried by such data. In annotating such data, one difficulty was how to anchor such basic entities as PLACE and PATH to parts of the data, since pictures and figures, unlike texts, cannot be tokenized. Another difficulty arose from the understanding of various symbols or conventional cues in visual data. A non-location en- tity MOTION of ISO-Space, for instance, is seldom mentioned explicitly, but only expressed implicitly on your web site - copy and paste the code below: <a href="http://www.planetware.com/map/ vatican-city-map-scv-vat ce.htm"> <img src ="http://www.planetware.com/i/map/SCV /vatican-city-map.jpg" width="1200"

Indeed, the lack of reliable sensorimotor effects in our studies could be because, given the cognitive demands of constructing and maintaining a situation model, readers do not encode or monitor information that is not central to the task, instead opting to compute spatial relations within a situation model as needed (see also Radvansky & Copeland, 2000; Zwaan & van Oostendorp, 1993). When experien- cing through vision spatial information, which is three-dimensional and nonlinear, spatial information is continuously and near-simultaneously integrated into an ongoing mental representation. By compari- son, incorporating information from spatial descrip- tions into a situation model requires considerable effort. When spatial information is experienced through language, which is sequential and linear, it requires effort to be decoded into a spatial mental rep- resentation (see Levelt, 1989, on the linearization problem). Indeed, participants take more time to learn the locations of objects when these are provided through simple linguistic statements than through vision (Klatzky, Lippa, Loomis, & Golledge, 2002). Since linguistically presented spatial information is hard to represent, it may be disregarded if it is not critical to the causal chain of events in stories. Under normal reading instructions (not emphasiz- ing the environment in the story), readers are not very much focused on constructing precise spatial representations: They process spatial information relatively fast and are relatively poor at verifying spatial inferences (Zwaan & van Oostendorp, 1993). Instead, during story comprehension, readers primarily focus on and track causal infor- mation (Bloom, Fletcher, van den Broek, Reitz, & Shapiro, 1990), which may be more instrumental to story coherence than spatial information. In our studies, at the end of the room ’ s description,

6.2 There are two distinct categories of spatial information products and services provided by public sector organisations. Firstly, it is those products which are regarded as generally available from the organisation. These are usually listed in the organisation’s manual of records prepared in terms of section 14 of the Promotion of Access to Information Act, 2000 (Act No. 2 of 2000) and revised annually. The second category would consist of products and services which the organisation customises, on specific request, for the client and which are not generally available (also referred to as value-added products and services).

The advent of spatial technology and web services enables more inclusive and open models of spatial services where grass-root citizens and community groups with no prior experience in spatial technologies can participate (Paudyal et al. 2009). More recently, the application and diffusion of the internet and information communication technology (ICT) has greatly expanded the opportunities for distributed working and the sharing of information and expertise. The use of social media for improving location enabled information sharing between emergency management agencies and the affected community is a very successful example of a collaborative network project. Confidence in collaborative networking has been growing. For instance, ‘Web 2.0’ applications have generated a wide range of proposals for employing ‘user-generated content’ and greater collaboration in a number of sectors, from social networking to corporate communication and information exchange. There is great scope for collaborative networking for spatial information sharing and SDI development.

These results tell us several things about the relationship between spatial information and rhetorical structure as it applies to narrative discourse. First, spatial information predicts rhetorical structure as good as non-spatial types of linguistic information reported in other investigations and with many fewer features. For example, Sporleder and Lascarides (2005) rely on 72 different features falling into nine classes whereas we rely on 14 features in five classes. This suggests that spatial information is not only central to rhetorical stucture, like temporal components, but central to the task of prediction. Second, while the type of spatial information that predicts rhetorical structure is based on the primary figure and ground relationship, it is the qualitative semantic variations within these elements that is providing the discrimination. It is the organization of spatial relationships - (Verb and Preposition) and the perspective provided by the narrator (Figure, Ground and Frame) combined - rather than any individual elements. 4.2 Spatial Information Prediction

that an animal is able to link the landmarks associated with a particular location with its best estimate of the path integration coordinates of that location. For visual resetting to occur, the stored path integration coordinates of a visually defined location should come from the average path integration coordinates experienced in that particular location, and thus would change with the animal’s increasing experience of the terrain (Collett and Graham, 2004). Intriguingly, we found that the bees’ experience of the two-legged journey changed the encoding of spatial information in the waggle dance, i.e. bees flying inside the 90°-oriented tunnel modified the encoding of both direction and distance based on whether (or not) they had foraged inside the 0°-oriented tunnel, approximately 24·h before the experiments (Fig.·2H,I vs Fig.·2K,L; also see Results). Most likely, the increasing experience of the visual landmarks present in the foraging area and of the two different types of landscapes available during both segments of the two- legged outbound flight (but not of a particular route defined by subjective measures of both distances and directions) modulated the encoding of spatial information in the waggle dance. Indeed, the directional information perceived inside the tunnel was processed differentially in the context of the waggle dance only after experiencing the prospective path integration coordinates and visual cues throughout two successive days (Fig.·2K,L). This means that the difference observed in the dances of both experienced and naïve bees might be explained on the basis of the magnitude of the mismatch experienced at the beginning of the inbound flight between the current and stored path integration coordinates (see above); particularly since the bees were always exposed to the same landmark views at the beginning of their inbound flights. In this context, the higher the number of flights in which the bees are exposed to the two different types

Gestures are movements of the body, usually the hands, which are produced when engaging in effortful cognitive activity, such as speaking or problem solving (Alibali, 2005). Common spatial activities, such as giving directions, often include gestures (e.g., Lavergne & Kimura, 1987), and domains of science that require communicating complex spatial ideas, such as geology, often make use of gestures (Liben et al., 2010). One common function of gestures is that they are used to focus attention to spatial information (e.g., Atit, Shipley, & Tikoff, 2014; Lozano & Tversky, 2006; Roth 2000). For example, pointing and tracing ges- tures can be used to draw the listener’s attention to critical pieces of information within the conversational space (e.g., Heiser, Tversky, & Silverman, 2004; Lozano & Tversky, 2006). Geoscience experts, when asked to inter- pret and explain complex diagrams, such as a geologic map, use pointing and tracing gestures to focus their stu- dents’ attention to important pieces of information on the map (Atit et al., 2013). Another common function of ges- tures is that they are used to convey complex three- dimensional spatial relationships (e.g., Alles & Riggs, 2011; Atit, Gagnier, & Shipley, 2015). Three-dimensional ges- tures are well suited to portray continuous complex three- dimensional spatial relations because they can depict information about the space, shape, form, and position of an object simultaneously (Atit et al., 2014). Instructors use three-dimensional gestures when describing three- dimensional forms represented on two-dimensional dia- grams (Atit et al., 2013), and researchers have found that gesturing about three-dimensional spatial relationships bolsters students’ skill in reasoning about diagrams of three-dimensional spatial relations (Atit et al., 2015). These observations suggest that three-dimensional ges- tures could be used to help a student understand shape information on topographic maps by connecting the spatial relations within and between contour lines to the spatial relations of the structures in the world.

Capsule Networks (CapsNets) are recently introduced to overcome some of the shortcomings of traditional Convolu- tional Neural Networks (CNNs). CapsNets replace neurons in CNNs with vectors to retain spatial relationships among the features. In this paper, we propose a CapsNet architec- ture that employs individual video frames for human action recognition without explicitly extracting motion information. We also propose weight pooling to reduce the computational complexity and improve the classification accuracy by appro- priately removing some of the extracted features. We show how the capsules of the proposed architecture can encode temporal information by using the spatial features extracted from several video frames. Compared with a traditional CNN of the same complexity, the proposed CapsNet improves ac- tion recognition performance by 12.11% and 22.29% on the KTH and UCF-sports datasets, respectively.