An algorithmic definition of the axial map

The fewest-line axial map, often simply referred to as the 'axial map, is one of the primary tools of space syntax. Its natural language definition has allowed researchers to draw consistent maps that present a concise description of architectural space; it has been established that graph measures obtained from the map are useful for the analysis of pedestrian movement patterns and activities related to such movement: for example, the location of services or of crime. However, the definition has proved difficult to translate into formal language by mathematicians and algorithmic implementers alike. This has meant that space syntax has been criticised for a lack of rigour in the definition of one of its fundamental representations. Here we clarify the original definition of the fewest-line axial map and show that it can be implemented algorithmically. We show that the original definition leads to maps similar to those currently drawn by hand, and we demonstrate that the differences between the two may be accounted for in terms of the detail of the algorithm used. We propose that the analytical power of the axial map in empirical studies derives from the efficient representation of key properties of the spatial configuration that it captures

스캔 도면을 활용한 이동약자용 실내 그래프 데이터베이스 구축

학위논문(박사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2021.8. 박슬아.사람들의 실내 활동이 다양해지면서 건물의 규모가 커지고 구조가 복잡해지고 있다. 이러한 실내 환경의 변화는 교통약자의 이동성 보장에 대한 사회적 관심을 증가시켰으며, 교통약자 맞춤형 실내 라우팅 서비스에 대한 수요 또한 증가시켰다. 특히 많은 이동 제약을 가지는 이동약자 대상 서비스의 경우에는, 최적 경로를 계획하는 과정에서 개인의 선호나 경험이 반영된 개인화된 서비스로 범위가 확장되고 있다. 이러한 배경에서, 스키마가 유연하고 데이터의 가공 및 처리가 효율적인 데이터베이스의 구축이 필요하다. 본 연구에서는 스캔한 도면 이미지를 활용한 이동약자용 실내 그래프데이터베이스 구축 기법을 제안하였다. 먼저, 국내외 실내 공간 관련 표준 및 설계 기준들의 검토를 통해 이동약자의 통행과 관련된 실내 공간 및 객체, 영향 요인들을 도출하여 개념적 데이터 모델을 설계하였다. 또한, 실내의 각 공간과 시설물의 기하정보와 위상정보를 기반으로 이동약자의 접근성 및 통행 가능성을 정량화하기 위한 접근성 지수를 설계하였다. 다음으로, 스캔 도면을 입력하여 이동약자용 실내 그래프 데이터베이스 구축을 위한 프로세스를 제안하였다. 제안한 프로세스는 전이학습 기반 접근 방식을 통해 스캔 도면에서 공간의 구조 정보를 추출하고, 토폴로지 추출 및 접근성 평가를 통해 이동약자용 네트워크 모델을 생성하며, 생성한 네트워크 모델을 그래프 데이터베이스로 자동 변환하는 과정을 포함한다. 구체적으로, 제안 프로세스는 수정된 ResNet 기반의 모델을 새롭게 라벨링한 도면으로 미세 조정하여 사용함으로써 실내 구조맵을 생성한다. 이후 추출된 객체들의 공간 관계를 기반으로 각 공간을 노드와 링크로 표현한 실내 네트워크 모델을 구축한다. 각 공간의 접근성 정보는 제안된 접근성 지수와 임계값을 사용하여 생성된 후 데이터베이스에 저장되어, 이동약자를 위한 접근 가능한 그래프 추출에 활용될 수 있다. 본 연구에서는 제안한 기법을 서울대학교 도면 데이터 셋에 적용하여 이동약자용 실내 그래프 데이터베이스를 구축하고 평가하였다. 구축한 실내 그래프 데이터베이스를 활용하여 다층 경로 계획과 실내외 연계 경로 계획의 2가지 시나리오에 따라 최적 경로를 도출하였다. 그 결과, 일반 보행자의 최적 경로와 비교하여 이동약자용 최적 경로는 가까운 계단이 아닌 엘리베이터를 통한 수직 이동을 포함하였을 뿐만 아니라 접근 불가능한 공간을 회피하도록 도출되었다. 즉, 제안한 기법을 통해 이동약자 측면에서 통행 장애 정보를 포함하여 실내 환경을 적절하게 묘사하는 데이터베이스의 구축이 가능함을 확인할 수 있었다. 또한, 출입로로 명명된 관계 생성만으로 스케일이나 좌표 변환 없이 실내외 연계 경로 계획이 가능하였는데, 이는 독립적인 데이터 간 연계 사용에 적합한 그래프 데이터베이스의 특성을 반영한 결과로 판단할 수 있다. 본 연구의 주요 기여는 스캔한 도면을 사용하여 이동약자용 실내 그래프 데이터베이스를 구축하기 위한 프로세스를 개발한 것이다. 구체적으로, 이동약자의 이동에 초점을 두고 설계한 데이터 모델을 기반으로 한 데이터베이스 구축이 가능하므로 이동약자용 실내 길안내 서비스에 활용될 수 있다. 또한, 토폴로지 구축 및 그래프 데이터베이스로의 변환을 위한 하위 프로시져를 개발하였으며, 제안 프로세스는 해당 프로시져들로 구성되어 도면 입력을 통해 이동약자용 실내 그래프 데이터베이스 구축을 가능하게 한다. 해당 하위 프로시져들은 자동으로 수행될 수 있어 데이터베이스 구축 시 소요되는 시간과 비용을 절감할 수 있다. 또한, 다양한 정형 및 비정형 데이터의 연계에 적합한 그래프 데이터베이스의 특징에 의해, 제안한 프로세스를 통해 구축한 실내 데이터베이스는 기존 공간 모델의 기능을 포함하면서 다양한 유형의 길안내 서비스에 활용될 수 있을 것으로 기대된다.Changes to the indoor environment have increased social interest in ensuring the mobility of people with disabilities. Therefore, the demand for customized indoor routing services for people with mobility disabilities (PWMD), who have many travel restrictions, is increasing. These services have progressed from spatial routing to personalized routing, which reflects personal preferences and experiences in planning an optimal path. In this regard, it is necessary to generate a database for PWMD with a flexible schema suitable for the efficient manipulation and processing of data. This study aims to propose a technique of generating an indoor graph database for PWMD using scanned floor plans. First, a conceptual data model was developed by deriving relevant indoor features and influential factors, considering various international regulations on indoor environments. Also, the accessibility index was designed based on the data model to quantify the difficulties in accessing spaces based on each indoor spaces geometric characteristics. Next, a three-stage process was proposed: retrieving the structure of spaces from scanned floor plans through a transfer learning-based approach, retrieving topology and assessing accessibility for creating an indoor network model for PWMD, and converting the network model into a graph database. Specifically, an indoor structure map is created by fine-tuning the modified Resnet-based model with newly annotated floor plans for extracting structure information. Also, based on the spatial relationship of the extracted features, the indoor network model was created by abstracting indoor spaces with nodes and links. The accessibility of each space is determined by the proposed indices and thresholds; thereby, a feasible network for PWMD could be derived. Then, a process was developed for automatically converting an indoor network model, including accessibility property, into a graph database. The proposed technique was applied to the Seoul National University dataset to generate an indoor graph database for PWMD. Two scenario-based routing tests were conducted using the generated database to verify the utility of results: multi-floor routing and integrated indoor-outdoor routing. As a result, compared with the path for general pedestrians, the optimal path for PWMD was derived by avoiding inaccessible spaces, including vertical movement using elevators rather than the nearest stairs. In other words, applying the proposed technique, a database that adequately described an indoor environment in terms of PWMD with sufficient mobile constraint information could be constructed. Moreover, an integrated indoor-outdoor routing could be conducted by only creating an entrance-labeled relationship, without scale and coordinate transformation. This result reflects the usability of the generated graph database and its suitability regarding the incorporation of multiple individual data sources. The main contribution lies in the development of the process for generating an indoor graph database for PWMD using scanned floor plans. In particular, the database for PWMD routing can be generated based on the proposed data model with PWMD-related features and factors. Also, sub-procedures for topology retrieval and graph database conversion are developed to generate the indoor graph database by the end-to-end process. The developed sub-procedures are performed automatically, thereby reducing the required times and costs. It is expected that the target database of the proposed process can be generated considering utilization for various types of routing since the graph database is easily integrated with multiple types of information while covering the existing spatial models function.1. Introduction 1 1.1 Objectives and contributions 1 1.2 Related works 7 1.2.1 Indoor environment conceptualization 7 1.2.2 Indoor data construction 11 1.2.3 Accessibility assessment 19 1.3 Research scope and flow 22 2. Conceptual modeling 26 2.1 Relevant features and factors 28 2.2 Proposed data model 30 2.3 Space accessibility for PWMD 36 2.3.1 Influential factors within indoor environments 37 2.3.2 Accessibility index 41 3. Indoor graph database for PWMD from scanned floor plans 43 3.1 Retrieving structure of indoor spaces 43 3.1.1 Pre-trained model for detecting indoor geometry 45 3.1.2 Dataset with new annotation 47 3.1.3 Transfer learning-based approach 52 3.2 Generating the indoor network model for PWMD 56 3.2.1 Definition of nodes and links in the network model 60 3.2.2 The classification rule of space polygons 63 3.2.3 Connection between general spaces and doors 68 3.2.4 Node-link generation for horizontal transition spaces 71 3.2.5 Vertical link generation 75 3.2.6 Connectivity and accessibility information generation 79 3.3 Indoor graph database for PWMD 80 3.3.1 Graph representation of indoor environments 80 3.3.2 Conversion of network model into graph database 83 3.4 Entire process 87 4. Experiment and results 89 4.1 Experimental setup and test data 89 4.2 Evaluation for retrieved information 92 4.2.1 Results of structure retrieval 92 4.2.2 Results of topology retrieval 99 4.3 Generated indoor graph database for PWMD 128 4.3.1 Results of the indoor graph database for PWMD 128 4.3.2 Query-based routing 136 5. Conclusion 147 References 150 Appendix 166 국문초록 178박

The development of a scenario independent method for evaluating the evacuation complexity of a building

Over the past two decades, more than 30 evacuation models have been developed to reproduce people’s movement patterns in evacuation. However, evacuation models cannot assess whether one building is better than another in regards to evacuation wayfinding. There exist techniques that attempt to compare different buildings for evacuation complexity. However, these graph measures are primarily used to measure the relative accessibility of different locations in a spatial system and were not generated for the purpose of comparing the complexity of different buildings. Currently only one method exists, Donegan’s method [DT98] [PD96] [DT99], which can be applied to compare building for evacuation ability. However, this technique is severely limited to specific building layouts and only considers connectivity. Taking the Donegan’s method as a first step, this thesis extends this algorithm to obtain a new Distance Graph Method, which considers travel distance as well as being able to be applied to graphs with circuits. Then a further building complexity measures is presented, the Global Complexity (PAT) method. This is shown to be a valid measure which considers additional important factors such as wayfinding time, travel distance and the areas of compartments. The Distance Graph Method and Global Complexity (PAT) methods are based on a room graph representation which does not have the descriptive power to describe the actual routes taken during the wayfinding process. To resolve this drawback a further method is presented which utilises a ‘route-based graph’ that has the ability to represent the real route that an evacuee will take during the wayfinding process. Furthermore the Distance Graph Method and Global Complexity (PAT) methods assume a “worst state” calculation for the nodal information. This means for buildings with more than one exit these methods calculate a global building complexity according to a mathematical formula, which considers all exits separately. To address these problems, the final method, Complexity Time Measure, is presented, which is based around a number of wayfinding behaviour rules over a ‘route-based graph’ representation. This addresses the question: ‘If an occupant is positioned at a random location within a building, on average how long does the occupant need to spend to find an available exit?’ Hence, provides a means to compare complex buildings, with circuits, in relation to evacuation capability

Computational network design from functional specifications

Connectivity and layout of underlying networks largely determine agent behavior and usage in many environments. For example, transportation networks determine the flow of traffic in a neighborhood, whereas building floorplans determine the flow of people in a workspace. Designing such networks from scratch is challenging as even local network changes can have large global effects. We investigate how to computationally create networks starting from only high-level functional specifications. Such specifications can be in the form of network density, travel time versus network length, traffic type, destination location, etc. We propose an integer programming-based approach that guarantees that the resultant networks are valid by fulfilling all the specified hard constraints and that they score favorably in terms of the objective function. We evaluate our algorithm in two different design settings, street layout and floorplans to demonstrate that diverse networks can emerge purely from high-level functional specifications

Indoor navigation for the visually impaired : enhancements through utilisation of the Internet of Things and deep learning

Wayfinding and navigation are essential aspects of independent living that heavily rely on the sense of vision. Walking in a complex building requires knowing exact location to find a suitable path to the desired destination, avoiding obstacles and monitoring orientation and movement along the route. People who do not have access to sight-dependent information, such as that provided by signage, maps and environmental cues, can encounter challenges in achieving these tasks independently. They can rely on assistance from others or maintain their independence by using assistive technologies and the resources provided by smart environments. Several solutions have adapted technological innovations to combat navigation in an indoor environment over the last few years. However, there remains a significant lack of a complete solution to aid the navigation requirements of visually impaired (VI) people. The use of a single technology cannot provide a solution to fulfil all the navigation difficulties faced. A hybrid solution using Internet of Things (IoT) devices and deep learning techniques to discern the patterns of an indoor environment may help VI people gain confidence to travel independently. This thesis aims to improve the independence and enhance the journey of VI people in an indoor setting with the proposed framework, using a smartphone. The thesis proposes a novel framework, Indoor-Nav, to provide a VI-friendly path to avoid obstacles and predict the user s position. The components include Ortho-PATH, Blue Dot for VI People (BVIP), and a deep learning-based indoor positioning model. The work establishes a novel collision-free pathfinding algorithm, Orth-PATH, to generate a VI-friendly path via sensing a grid-based indoor space. Further, to ensure correct movement, with the use of beacons and a smartphone, BVIP monitors the movements and relative position of the moving user. In dark areas without external devices, the research tests the feasibility of using sensory information from a smartphone with a pre-trained regression-based deep learning model to predict the user s absolute position. The work accomplishes a diverse range of simulations and experiments to confirm the performance and effectiveness of the proposed framework and its components. The results show that Indoor-Nav is the first type of pathfinding algorithm to provide a novel path to reflect the needs of VI people. The approach designs a path alongside walls, avoiding obstacles, and this research benchmarks the approach with other popular pathfinding algorithms. Further, this research develops a smartphone-based application to test the trajectories of a moving user in an indoor environment