A hybrid model for capturing implicit spatial knowledge

This paper proposes a machine learning-based approach for capturing rules embedded in users’ movement paths while navigating in Virtual Environments (VEs). It is argued that this methodology and the set of navigational rules which it provides should be regarded as a starting point for designing adaptive VEs able to provide navigation support. This is a major contribution of this work, given that the up-to-date adaptivity for navigable VEs has been primarily delivered through the manipulation of navigational cues with little reference to the user model of navigation

Spatial Knowledge and Landscape Cognition

City of canals, bikes, tulips, and stroopwafels. You\u27re here and it\u27s time to get going, time to see the city.But how do you get where you\u27re going, how do you find your way home. Follow a map? Ask for directions? Wander aimlessly until you give up and decide to pay for a cab? This report looks at how we place ourselves in our surroundings, how we communicate spatial relationships and what implications our spatial knowledge has on our understanding of the world

Effects of hyperlinks on navigation in virtual environments

Hyperlinks introduce discontinuities of movement to 3-D virtual environments (VEs). Nine independent attributes of hyperlinks are defined and their likely effects on navigation in VEs are discussed. Four experiments are described in which participants repeatedly navigated VEs that were either conventional (i.e. obeyed the laws of Euclidean space), or contained hyperlinks. Participants learned spatial knowledge slowly in both types of environment, echoing the findings of previous studies that used conventional VEs. The detrimental effects on participants' spatial knowledge of using hyperlinks for movement were reduced when a time-delay was introduced, but participants still developed less accurate knowledge than they did in the conventional VEs. Visual continuity had a greater influence on participants' rate of learning than continuity of movement, and participants were able to exploit hyperlinks that connected together disparate regions of a VE to reduce travel time

Navigating large-scale ‘‘desk-top’’ virtual buildings: effects of orientation aids and familiarity

Two experiments investigated components of participants’ spatial knowledge when they navigated large-scale ‘‘virtual buildings’’ using ‘‘desk-top’’ (i.e., nonimmersive) virtual environments (VEs). Experiment 1 showed that participants could estimate directions with reasonable accuracy when they traveled along paths that contained one or two turns (changes of direction), but participants’ estimates were significantly less accurate when the paths contained three turns. In Experiment 2 participants repeatedly navigated two more complex virtual buildings, one with and the other without a compass. The accuracy of participants’ route-finding and their direction and relative straight-line distance estimates improved with experience, but there were no significant differences between the two compass conditions. However, participants did develop significantly more accurate spatial knowledge as they became more familiar with navigating VEs in general

The Effects of Finger-Walking in Place (FWIP) on Spatial Knowledge Acquisition in Virtual Environments

Spatial knowledge, necessary for efficient navigation, comprises route knowledge (memory of landmarks along a route) and survey knowledge (overall representation like a map). Virtual environments (VEs) have been suggested as a power tool for understanding some issues associated with human navigation, such as spatial knowledge acquisition. The Finger-Walking-in-Place (FWIP) interaction technique is a locomotion technique for navigation tasks in immersive virtual environments (IVEs). The FWIP was designed to map a human’s embodied ability overlearned by natural walking for navigation, to finger-based interaction technique. Its implementation on Lemur and iPhone/iPod Touch devices was evaluated in our previous studies. In this paper, we present a comparative study of the joystick’s flying technique versus the FWIP. Our experiment results show that the FWIP results in better performance than the joystick’s flying for route knowledge acquisition in our maze navigation tasks

Spatial Knowledge and Urban Planning (Editorial)

Urban planning is simultaneously shaped by and creates new (spatial) knowledge. The changes in planning culture that have taken place in the last decades - especially the so-called communicative turn in planning in the 1990s - have brought about an increased attention to a growing range of stakeholders of urban development, their interests, logics, and participation in planning as well as the negotiation processes between these stakeholders. However, while this has also been researched in breadth and depth, only scant attention has been paid to the knowledge (claims) of these stakeholders. In planning practice, knowledge, implicit and explicit, has been a highly relevant topic for quite some time: It is discussed how local knowledge can inform urban planning, how experimental knowledge on urban development can be generated in living labs, and what infrastructures can process "big data" and make it usable for planning, to name a few examples. With the thematic issue on "Spatial Knowledge and Urban Planning" we invited articles aiming at exploring the diverse understandings of (spatial) knowledge, and how knowledge influences planning and how planning itself constitutes processes of knowledge generation. The editorial gives a brief introduction to the general topic. Subsequently, abstracts of all articles illustrate what contents the issue has to offer and the specific contribution of each text is carved out. In the conclusion, common and recurring themes as well as remaining gaps and open questions at the interface of spatial knowledge and urban planning are discussed

The arable farmer as the assessor of within-field soil variation

Feasible, fast and reliable methods of mapping within-field variation are required for precision agriculture. Within precision agriculture research much emphasis has been put on technology, whereas the knowledge that farmers have and ways to explore it have received little attention. This research characterizes and examines the spatial knowledge arable farmers have of their fields and explores whether it is a suitable starting point to map the within-field variation of soil properties. A case study was performed in the Hoeksche Waard, the Netherlands, at four arable farms. A combination of semi-structured interviews and fieldwork was used to map spatially explicit knowledge of within-field variation. At each farm, a field was divided into internally homogeneous units as directed by the farmer, the soil of the units was sampled and the data were analysed statistically. The results show that the farmers have considerable spatial knowledge of their fields. Furthermore, they apply this knowledge intuitively during various field management activities such as fertilizer application, soil tillage and herbicide application. The sample data on soil organic matter content, clay content and fertility show that in general the farmers’ knowledge formed a suitable starting point for mapping within-field variation in the soil. Therefore, it should also be considered as an important information source for highly automated precision agriculture systems

Linear building pattern recognition via spatial knowledge graph

Building patterns are important urban structures that reflect the effect of the urban material and social-economic on a region. Previous researches are mostly based on the graph isomorphism method and use rules to recognize building patterns, which are not efficient. The knowledge graph uses the graph to model the relationship between entities, and specific subgraph patterns can be efficiently obtained by using relevant reasoning tools. Thus, we try to apply the knowledge graph to recognize linear building patterns. First, we use the property graph to express the spatial relations in proximity, similar and linear arrangement between buildings; secondly, the rules of linear pattern recognition are expressed as the rules of knowledge graph reasoning; finally, the linear building patterns are recognized by using the rule-based reasoning in the built knowledge graph. The experimental results on a dataset containing 1289 buildings show that the method in this paper can achieve the same precision and recall as the existing methods; meanwhile, the recognition efficiency is improved by 5.98 times.Comment: in Chinese languag