Diverse Density: An alternative housing strategy

<p>Diverse Density proposes an alternative housing strategy to the idealistic top-down process of housing development. The term ‘Top – down’ refers to a situation in which decisions are made by a few people in authority rather than by the people who are affected by the decisions (Cambridge). Problems/Position/Question: New Zealand’s urban housing is in a period of flux. Pressures of densification have permitted the intervention of medium density housing development schemes but these are not always successful. These typically top-down processes often result in internally focused design schemes that do not adhere to their specific context. The subsequent design outcomes can cause detrimental impacts to the local, urban and architectural conditions. With vast quantities of council regulations, building restrictions and design guidelines clouding over the housing sector, commonly referred to as ‘red tape’, occupant participation in the housing development sector is dwindling. A boundless separation between top-down and traditional housing processes has occurred and our existing neighbourhoods and historic architectural character are taking on the brunt of the problem. The thought-provoking, alternative housings strategies of key research theorists Alejandro Aravena and John Habraken frame positions that challenge contemporary densification methods with an alternative strategy. This position is addressed by endeavoring to answer; How can demands for denser housing achieve dynamic design responses that adhere to changes in occupancy, function and local site conditions? Aim: The aim of this thesis is to challenge New Zealand’s current housing densification methods by proposing an alternative densification strategy. Explicit devotion will be attributed to opposing top-down building developments. Secondly, this thesis aims to test a speculative site-specific housing model. The implementation of a Christchurch housing scenario will situate an investigative study to test the strategy and its ability to stimulate greater diversity, site responsiveness, functional adaptability and occupancy permutation. The post-earthquake housing conditions of Christchurch provide an appropriate scenario to test and implement design-led investigations. Objectives: The primary objectives of this design-led research investigation it to challenge the idealistic top-down method of developing density with a new method to: - Develop contextual architectural cohesion - Encourage residential diversity - Reinvigorate architectural autonomy - Respond to, and recognise, existing site conditions - Develop a housing model that: - Adapts to occupant functionality preferences - Caters to occupancy diversity - Achieves contextual responsiveness The proposition is addressed through a speculative design-led scenario study. A well-established Christchurch urban environment is adopted to implement and critique the envisioned alternative strategy. Development of the designs responsiveness, adaptability, and functionality produce a prototype housing model that actively adheres to its particular context. Implication: The implications of this research would be an alternative densification strategy to perceive the advancement of punctual assessment of building compliance. With accelerated building processes, the research may have implications for addressing New Zealand’s housing crisis whilst simultaneously providing diverse, personable and responsive architectural solutions. A more dynamic, up-to-date and responsive housing development sector would be informed.</p>

Diverse Density: An alternative housing strategy

<p>Diverse Density proposes an alternative housing strategy to the idealistic top-down process of housing development. The term ‘Top – down’ refers to a situation in which decisions are made by a few people in authority rather than by the people who are affected by the decisions (Cambridge). Problems/Position/Question: New Zealand’s urban housing is in a period of flux. Pressures of densification have permitted the intervention of medium density housing development schemes but these are not always successful. These typically top-down processes often result in internally focused design schemes that do not adhere to their specific context. The subsequent design outcomes can cause detrimental impacts to the local, urban and architectural conditions. With vast quantities of council regulations, building restrictions and design guidelines clouding over the housing sector, commonly referred to as ‘red tape’, occupant participation in the housing development sector is dwindling. A boundless separation between top-down and traditional housing processes has occurred and our existing neighbourhoods and historic architectural character are taking on the brunt of the problem. The thought-provoking, alternative housings strategies of key research theorists Alejandro Aravena and John Habraken frame positions that challenge contemporary densification methods with an alternative strategy. This position is addressed by endeavoring to answer; How can demands for denser housing achieve dynamic design responses that adhere to changes in occupancy, function and local site conditions? Aim: The aim of this thesis is to challenge New Zealand’s current housing densification methods by proposing an alternative densification strategy. Explicit devotion will be attributed to opposing top-down building developments. Secondly, this thesis aims to test a speculative site-specific housing model. The implementation of a Christchurch housing scenario will situate an investigative study to test the strategy and its ability to stimulate greater diversity, site responsiveness, functional adaptability and occupancy permutation. The post-earthquake housing conditions of Christchurch provide an appropriate scenario to test and implement design-led investigations. Objectives: The primary objectives of this design-led research investigation it to challenge the idealistic top-down method of developing density with a new method to: - Develop contextual architectural cohesion - Encourage residential diversity - Reinvigorate architectural autonomy - Respond to, and recognise, existing site conditions - Develop a housing model that: - Adapts to occupant functionality preferences - Caters to occupancy diversity - Achieves contextual responsiveness The proposition is addressed through a speculative design-led scenario study. A well-established Christchurch urban environment is adopted to implement and critique the envisioned alternative strategy. Development of the designs responsiveness, adaptability, and functionality produce a prototype housing model that actively adheres to its particular context. Implication: The implications of this research would be an alternative densification strategy to perceive the advancement of punctual assessment of building compliance. With accelerated building processes, the research may have implications for addressing New Zealand’s housing crisis whilst simultaneously providing diverse, personable and responsive architectural solutions. A more dynamic, up-to-date and responsive housing development sector would be informed.</p>

Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral Image

To solve the small sample size (SSS) problem in the classification of hyperspectral image, a novel classification method based on diverse density and sparse representation (NCM_DDSR) is proposed. In the proposed method, the dictionary atoms, which learned from the diverse density model, are used to solve the noise interference problems of spectral features, and an improved matching pursuit model is presented to obtain the sparse coefficients. Airborne hyperspectral data collected by the push-broom hyperspectral imager (PHI) and the airborne visible/infrared imaging spectrometer (AVIRIS) are applied to evaluate the performance of the proposed classification method. Results illuminate that the overall accuracies of the proposed model for classification of PHI and AVIRIS images are up to 91.59% and 92.83% respectively. In addition, the kappa coefficients are up to 0.897 and 0.91.

MILDMS: Multiple Instance Learning via DD Constraint and Multiple Part Similarity

As a subject area of symmetry, multiple instance learning (MIL) is a special form of a weakly supervised learning problem where the label is related to the bag, not the instances contained in it. The difficulty of MIL lies in the incomplete label information of instances. To resolve this problem, in this paper, we propose a novel diverse density (DD) and multiple part similarity combination method for multiple instance learning, named MILDMS. First, we model the target concepts optimization with a DD function constraint on positive and negative instance space, which can greatly improve the robustness to label noise problem. Next, we combine the positive and negative instances in the bag (generated by hand-crafted and convolutional neural network features) with multiple part similarities to construct an MIL kernel. We evaluate the proposed approach on the MUSK dataset, whose results MUSK1 (91.9%) and MUSK2 (92.2%) show our method is comparable to other MIL algorithms. To further demonstrate generality, we also present experimental results on the PASCAL VOC 2007 and 2012 (46.5% and 42.2%) and COREL (78.6%) that significantly outperforms the state-of-the-art algorithms including deep MIL and other non-deep MIL algorithms.

Automatic landmark discovery for learning agents under partial observability

In the reinforcement learning context, a landmark is a compact information which uniquely couples a state, for problems with hidden states. Landmarks are shown to support finding good memoryless policies for Partially Observable Markov Decision Processes (POMDP) which contain at least one landmark. SarsaLandmark, as an adaptation of Sarsa(λ), is known to promise a better learning performance with the assumption that all landmarks of the problem are known in advance. In this paper, we propose a framework built upon SarsaLandmark, which is able to automatically identify landmarks within the problem during learning without sacrificing quality, and requiring no prior information about the problem structure. For this purpose, the framework fuses SarsaLandmark with a well-known multiple-instance learning algorithm, namely Diverse Density (DD). By further experimentation, we also provide a deeper insight into our concept filtering heuristic to accelerate DD, abbreviated as DDCF (Diverse Density with Concept Filtering), which proves itself to be suitable for POMDPs with landmarks. DDCF outperforms its antecedent in terms of computation speed and solution quality without loss of generality. The methods are empirically shown to be effective via extensive experimentation on a number of known and newly introduced problems with hidden state, and the results are discussed.

Exploring Outdoor Solar Potential in High-Density Living: Analyzing Direct Sunlight Duration for Urban Agriculture in Seoul’s Residential Complexes

Urban agriculture has become a favored activity in many cities around the world. This study explores how urban agriculture’s potential can be maximized in Seoul, South Korea, a city characterized by high-density residential complexes. It selects six existing residential complexes with representative site typologies and diverse density levels. The study’s aim is to assess the impact of various typology and density settings on percentages of ground-level surface with direct sunlight above certain thresholds during warmer seasons when crops can grow. DIVA-for-Rhino is used for simulation. The findings suggest that parallel typologies and lower density levels offer the best performance, while other combinations show mixed results. This study could benefit citizens and policymakers to facilitate urban agriculture practices around the world by suggesting feasible solutions for high-density residential developments.