published_or_final_version / Urban Planning and Design / Master / Master of Science in Urban Planning

published_or_final_version / Geography and Geology / Doctoral / Doctor of Philosophy

published_or_final_version / Geography and Geology / Master / Master of Arts

The spatial planning of land use is the process of allocating different uses or activities to specific areas in a region and is the core content of land use planning systems. Land use planning is increasingly becoming complex because of the multifaceted problems it faces, such as guaranteeing economic growth, maintaining social equity, and preserving the environment. These objectives present conflicting demands from various land use groups and interest groups. The increased inclusion of objectives leads to different demands on the expected results. Moreover, the increased complexity of land use planning problems is influenced by the involvement and definition of multiple objectives. These objectives may be unstructured, nonlinear, and difficult to handle. Within this context, computer-based techniques have been developed to assist planners in decision making. Among all of the techniques, multi-objective optimization (MOO) approaches are the most well-known techniques in addressing multi-objective problems in land use planning. MOO approaches have successfully accomplished significant achievements. However, literature shows that some spatially-related environmental objectives, such as carbon emission, non-point source pollution, and soil erosion, are missing because of the difficulty in evaluating, analyzing, and measuring such complex land use objectives. / The land use planning process in China is divided into a series of land use plans at different levels. Among these plans, the municipal overall land use plan and the urban master plan are involved in managing the land use resources in a city. The municipal overall land use plan administers the urban and non-urban areas in an administrative scope, whereas the urban master plan focuses only on the development of urban areas. These two types of land use plans are conducted by two different government departments. These plans are usually inconsistent, particularly in terms of space. / Considering the spatial inconsistency between the municipal overall land use plan and urban master plan in China, a MOO-based two-level spatial planning of land use is conducted. The spatial planning aims at managing and coordinating the land use at different geographic extents and involves spatial layouts and structures of land use at different levels. In spatial planning, the geographic information system (GIS) and remote sensing (RS) are used to evaluate, analyze, and measure environmental, economic, and social issues with regard to the spatial land use change. The quantitative relationships between these objectives and spatial land use allocation are then used as rules in the MOO process to simulate environmental conditions under different spatial land use allocation scenarios. / Shenzhen, a rapidly developing city in China, is selected as the case study area to validate the proposed approach. The objectives and constraints in the spatial planning of land use are defined at two different levels based on the land use principles, local and national policies in China, and characteristics of Shenzhen. At the first level, nine objectives are proposed, namely, maximizing economic benefit, maximizing ecosystem services value, minimizing soil erosion, minimizing non-point source pollution, minimizing carbon emission, maximizing compatibility, minimizing change cost, maximizing accessibility, and minimizing landslide susceptibility. The objectives of spatial planning of land use at the urban level are subsequently proposed, as follows: maximizing housing capacity, maximizing employment capacity, minimizing changing cost, minimizing pollution from industrial lands, maximizing mixed land uses, maximizing green space, maximizing accessibility, maximizing compatibility, and maximizing spatial equity. The proposed spatial-related objectives are quantified by GIS. / Results indicated that the MOO-based two-level spatial planning can create trade-offs among the conflicting objectives, and a set of Pareto solutions is provided as options for decision makers or planners. Moreover, the MOO-based two-level spatial planning can generate a consistent land use planning system for Shenzhen. / 土地利用空間規劃通過對土地的空間位置及結構的調配以達到土地資源的合理利用.一個良好的土地利用規劃需滿足各方面的要求及目標,例如確保經濟增長的同時,保證社會公平,同時也保護環境的可持續發展.但由於社會各界對土地利用有不同的利益訴求,導致規劃中出現眾多相互衝突但不矛盾的目標.此外,土地利用規劃目標常常是空間相關的,這些空間相關目標具有非結構性,非線性的特點,使得整個土地利用規劃過程更加複雜.在這種情況下,電腦支援技術成為實際規劃中不可或缺的工具之一.在所有技術中,多目標優化模型已經被廣泛的應用于解決土地利用規劃中的多目標問題.多目標優化模型已經在土地利用規劃領域取得了大量有意義的成果.然後,現有研究往往關注土地利用結構,而忽視複雜而難以定義的土地空間問題,例如城市熱島,非點源污染,土壤侵蝕. / 在中國,土地利用規劃系統由一系列不同空間層次的規劃組成.在中國土地利用規劃系統中,市(地)級土地利用總體規劃和城市總體規劃主要關注某個城市的土地資源配置佈局問題.市(地)級土地利用總體規劃管理整個市(地)行政範圍內的土地資源,包括城市用地及非城市用地;然而城市總體規劃僅關注建成區及規劃區範圍內的土地資源.同時,這兩種規劃由不同的政府部門設定並統籌實施,這導致了兩規劃在空間範圍的不一致性. / 考慮到市(地)級土地利用總體規劃和城市總體規劃在空間上的不一致性,本研究提出了一個基於多目標優化的兩層土地利用空間規劃模型,並主要關注土地資源的空間佈局.在空間規劃中,地理資訊系統及遙感技術被用來評價,分析和衡量在土地利用變化下的環境目標.不同目標與空間土地利用變化之間的量化模型將作為多目標優化模型中的規則,用以類比不同土地利用情景下的各種目標實現情況. / 深圳作為一個高速發展並經歷土地利用迅速變化的城市被選為本研究的案例研究區.深圳市土地利用空間規劃的目標和限制條件被分為兩個層次,一個市(地)級土地利用總體規劃層次和另一個城市總體規劃層次.這些目標和限制條件基於已有的土地利用規劃方法,中國土地利用相關法律法規及深圳市發展背景來確定.在市(地)級土地利用總體規劃層次,定義了九個目標,分別是經濟利益最大化,生態服務價值最大化,土壤侵蝕最小,非點源污染最小,碳排放最小,地塊之間相容性最大,土地利用變化最小,可達性最大及滑坡危害最小. 在城市總體規劃層次,定義了如下目標:住房最大化,就業最大化,綠地最大化,可達性最大,城市土地利用地塊見相容性最大,公平性最大.所有這些目標可通過統計模型,地理資訊系統技術實現其量化. / 研究結果表明,本研究所提出的兩層次土地利用規劃能夠協調各個相互衝突的目標,並且為決策者和規劃師提供一系列Pareto解.同時,基於多目標優化的兩層次空間土地利用規劃能夠為深圳提供一個保持一致性的土地利用系統. / Zhang, Wenting. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2014. / Includes bibliographical references (leaves 169-184). / Abstracts also in Chinese. / Title from PDF title page (viewed on 16, November, 2016). / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.

Chan, King-Chong. / Thesis (M.Phil.) -- Chinese University of Hong Kong, 1991. / Bibliography: leaves 150-163. / Abstract --- p.i / Acknowledgements --- p.ii / Figures --- p.vii / Tables --- p.x / Chapter Chapter 1 --- Introduction --- p.i / Chapter 1.1 --- Background --- p.2 / Chapter 1.2 --- Objectives --- p.5 / Chapter 1.3 --- Hypotheses --- p.6 / Chapter 1.4 --- Organization of the Thesis --- p.6 / Chapter Chapter 2 --- Literature Review --- p.8 / Chapter 2.1 --- Land Use and Land Cover --- p.10 / Chapter 2.2 --- Informational Classes and Spectra I Classes --- p.11 / Chapter 2.3 --- Simple Per-Pixel Classification Method --- p.12 / Chapter 2.4 --- Scene Noise and Boundary Effect --- p.14 / Chapter 2.5 --- Using Filtered Data --- p.16 / Chapter 2 .6 --- Textura1 Classifier --- p.18 / Chapter 2.7 --- Contextual Classifier --- p.22 / Chapter 2.8 --- Geographic Information System (GIS) --- p.24 / Chapter 2.9 --- Expert System and Artificial Intelligence (AI) --- p.25 / Chapter 2.10 --- Concluding Remarks --- p.27 / Chapter Chapter 3 --- Methodology --- p.30 / Chapter 3.1 --- Spectral Class Composition Method (SCCM) --- p.32 / Chapter 3.1.1 --- The Concept of the Spectral Class Composition Method --- p.32 / Chapter 3.1.2 --- Unsupervised Classification Process --- p.39 / Chapter 3.1.3 --- Training Process --- p.39 / Chapter 3.1.4 --- Proportion Counting --- p.40 / Chapter 3.1.5 --- Number of Spectral Class --- p.41 / Chapter 3.1.6 --- Window Size --- p.42 / Chapter 3.1.7 --- Transect Process --- p.43 / Chapter 3.1.8 --- Classification Task --- p.45 / Chapter 3.1.9 --- Summary --- p.47 / Chapter 3.2 --- Research Design --- p.49 / Chapter 3.2.1 --- Study Area --- p.49 / Chapter 3.2.2 --- Data and Instruments Used --- p.51 / Chapter 3.2.3 --- C1assification Scheme --- p.51 / Chapter 3.2.4 --- Accuracy Assessment --- p.52 / Chapter Chapter 4 --- Results and Discussion I--- Examining the Relationship Between Land Use and Spectral Classes --- p.55 / Chapter 4. 1 --- Unsupervised Classification --- p.57 / Chapter 4.1.1 --- Unsupervised Classification Process --- p.57 / Chapter 4.1.2 --- Unsupervised Classification Results --- p.58 / Chapter 4.1.3 --- Difference Between Spectral Class Maps --- p.65 / Chapter 4.2 --- Training Process --- p.68 / Chapter 4.2.1 --- Definition of Training Process --- p.68 / Chapter 4.2.2 --- Selection of Training Sites --- p.69 / Chapter 4.2.3 --- Spectral Class Composition Data Extracted from the Training Sites --- p.70 / Chapter 4.2.4 --- Spectral Heterogeneous Characteristics of Land Use Types --- p.73 / Chapter 4.2.5 --- Different Number of Spectral Classes --- p.77 / Chapter 4.2.6 --- Similar Composition Results in Some Land Use Types --- p.80 / Chapter 4.2.7 --- Using Spectra1 Class Composition Data as Rules of Classification --- p.81 / Chapter 4.3 --- Proportion Counting --- p.83 / Chapter 4.3.1 --- Window-Based Proportion Counting Process --- p.83 / Chapter 4.3.2 --- Transect Process --- p.85 / Chapter 4.3.3 --- Variation of Spectra I Class Proportion within a Land Use Type --- p.91 / Chapter 4.3.4 --- Variation of Spectral Class Proportion among Land Use Types --- p.95 / Chapter 4.4 --- Summary --- p.103 / Chapter Chapter 5 --- Resu1ts and Discussion II --- Classification and Accuracy Assessment --- p.104 / Chapter 5.1 --- Rule-Based Land Use Classification --- p.106 / Chapter 5.1.1 --- Derivation of Rules for Classification --- p.106 / Chapter 5.1.2 --- Using Rules for Classification --- p.106 / Chapter 5.1.3 --- Modification of the Rules --- p.109 / Chapter 5.1.4 --- C1assification Resu11s --- p.109 / Chapter 5.2 --- Accuracy Assessment --- p.118 / Chapter 5.2.1 --- Accuracy Assessment Process --- p.118 / Chapter 5.2.2 --- Analysis of Error Matrices --- p.123 / Chapter 5.2.3 --- Comparison Between Spectral Class Composition Method and Simple Per-Pixel Method --- p.126 / Chapter 5.2.4 --- Discussion on the Resui.ts of Producer's and User's Accuracy --- p.130 / Chapter 5.2.5 --- Discussion on Number of Spectral Classes --- p.132 / Chapter 5.2.6 --- Discuss i on on Window Size --- p.134 / Chapter 5 .3 --- Summary --- p.136 / Chapter Chapter 6 --- Conclusion --- p.138 / Chapter 6.1 --- Summary --- p.139 / Chapter 6.2 --- Limitations and Problems --- p.142 / Chapter 6.3 --- Contribution --- p.147 / Chapter 6.4 --- Further Research --- p.148 / Bibliography --- p.150

Wong Sai Kit Frankie. / "Architecture Department, Chinese University of Hong Kong, Master of Architecture Programme 2001-2002, design report."

Based on the models, a case study is performed on multi-temporal land use change analysis in the Special Economic Zone (SEZ), Shenzhen. The results show that all the proposed models outperform the traditional logistic regression model: multinomial logit model (MNLM), which overlooks the aforementioned issues. Compared with MNLM, GTWLM and ST-PLM increased the percentage of correctly predicted (PCP) values from 74.1% to 82.3% and 79.4%, respectively. McNamara's test shows that the differences between those models are significant. The kappa coefficients reveal that the GTWLM and ST-PLM are better than MNLM. In particular, the GSTLM yields a considerably higher PCP of 85.9%. The Kappa coefficients also indicate that the GSTLM is the most optimal model. Furthermore, the GTWLM allows the model parameters to vary across space and time, which provides deep insights into the spatio-temporal variations of the land use pattern. Assisted with the visual results, the spatio-temporal land use distribution patterns in Shenzhen are analyzed and the results presented thereafter. / Research focusing on land use change analysis is of tremendous importance in global change studies. Land use change modeling, which is a prerequisite to understanding the complexity of land use dynamics, is an effective way to describe the change patterns and delve into the causes for the changes. Despite the development of many models in the past, several important issues still remain to be addressed such as spatio-temporal non-stationarity, spatio-temporal correlation, and individual effect. The primary objective of this research is to make improvements on.the traditional logistic models to suit the characteristics and requirements of land use change modeling. Specifically, three enhancements have been made. The first enhancement deals with spatio-temporal non-stationarity, the second improvement aims to incorporate spatio-temporal autocorrelation, and the third includes individual effect. / Three spatio-temporal logit models for land use change analysis, namely, geographically and temporally weighted logit model (GTWLM), spatio-temporal panel logit model (ST-PLM) and generalized spatio-temporal logit model (GSTLM), are proposed accordingly to deal with the aforementioned issues. GTWLM, which considers spatio-temporal non-stationarity, includes temporal data in a spatio-temproal framework by proposing a spatio-temporal distance. ST-PLM incorporates the spatio-temporal correlation and individual effect in one model, where spatio-temporal correlation is considered in the random individual effect with an assumption that the correlation between such components is inversely proportional to the spatio-temporal distance. By integrating GTWLM and ST-PLM, the GSTLM explores spatio-temporal non-stationarity and correlations simultaneously, whilst considering their individual effects to construct an integrated model. / Liu, Biao. / Adviser: Huang Bo. / Source: Dissertation Abstracts International, Volume: 73-01, Section: A, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 124-132). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Land use and land cover (LULC) change information is essential in urban planning and management. With the rapid urbanization in China, many illegal land developments have emerged in some rapidly developing regions and have caused irreversible environmental problems, posing a threat to sustainable urban development. Short-interval monitoring of LULC change therefore is necessary in these regions to control and prevent illegal land developments at an early stage. Conventional optical remote sensing is limited by weather conditions and has difficulties collecting timely data in tropical regions characterized by frequent cloud cover. Radar remote sensing, not affected by clouds, is therefore a potential tool for collecting timely LULC information in these regions. Polarimetric SAR (PolSAR) is more suitable than single-polarization SAR for monitoring LULC change because it can discriminate different types of scattering mechanisms. The overall objective of this study is to conduct short-interval monitoring of LULC change using RADARSAT-2 PolSAR images. Classification methods that achieve high accuracy for PolSAR images are essential in monitoring LULC change. In this study, a new method, based on the integration of polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms, is proposed for LULC classification using RADARSAT-2 PolSAR data. A comparison between the proposed classification method and Wishart supervised classification which is commonly used for the classification of PolSAR data showed that the proposed method can significantly improve LULC classification accuracy. Polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms have been determined to contribute to the improvement achieved by the proposed classification method. Selection of appropriate incidence angle is important in LULC classification using PolSAR images because incidence angle influences the intensity and patterns of radar return. Based on the proposed classification method, the present study further investigates the influence of incidence angle on LULC classification using RADARSAT-2 PolSAR images. LULC classifications using incidence angles of 31.50 and 37.56° were conducted separately. The influence of incidence angle on the classification was investigated by comparing the results of the two independent classifications. The comparison showed that large incidence angle performs much better than small incidence angle in the classification of different vegetation types, whereas small incidence angle outperforms large incidence angle in reducing the confusion between urban/built-up areas and vegetation, that between vegetable and barren land, and that among barren land, water, and lawn. Considering that the detection of urban/built-up areas and barren land is important in monitoring illegal land developments, small incidence angle is more suitable than large incidence angle in monitoring illegal land developments. Change detection methods that achieve high accuracy for PolSAR data are also essential in monitoring LULC change. The current study proposes a new method for LULC change detection using RADARSAT-2 PolSAR images. The proposed change detection method combines change vector analysis (CVA) and post-classification comparison (PCC) to detect LULC changes using object-oriented image analysis. The classification of PolSAR images is based on the proposed classification method. Compared with the PCC based on Wishart supervised classification, the proposed change detection method can achieve much higher accuracy for LULC change detection. Further investigation indicated that CVA, PCC, and object-oriented image analysis all contribute to the higher accuracy achieved by the proposed change detection method. Short-interval monitoring of LULC change was carried out using a time series of RADARSAT-2 PolSAR images. The monitoring was based on monthly LULC change detection using the proposed change detection method and appropriate incidence angle. The influence of environmental factors on short-interval monitoring of LULC change was investigated by analyzing the monthly change detection results. Paddy harvesting and planting, seasonal crop growth, and change in soil moisture and surface roughness were found to exert significant influence on the short-interval monitoring of LULC change. High accuracy can be achieved for short-interval monitoring of construction sites and bulldozed land using RADARSAT-2 PolSAR images. However, paddy harvesting and growth still cause false alarms on the monitoring of these two LULC classes. The study indicated that conducting short-interval monitoring of LULC change using RADARSAT-2 PolSAR images is effective. High accuracy can be achieved for short-interval monitoring of construction sites and bulldozed land using the proposed change detection and classification methods, which can provide important information for the control and prevention of illegal land developments at an early stage. / published_or_final_version / Urban Planning and Design / Doctoral / Doctor of Philosophy

In Hong Kong, housing prices have increased significantly in recent years. Amongst all the reasons for such significant increase, low interest rate has been recognized as one of the major reasons. Many studies have provided empirical evidence to support the negative relationship between interest rate and housing prices. However, in the US, recent studies (Glaeser, Gottlieb and Gyourko, 2010; Kuttner, 2012) show that the observed effect of interest rate changes on housing prices is much less than that predicted by the standard user cost model. According to the Glaeser et al. (2010), there are three potential explanations for the low housing price sensitivity to interest rate fluctuation. First, increase in land (and therefore housing) supply elasticity can reduce the effects of the demand-side variables, including interest rate. Second, high risk premium for long term mortgage rate in the US makes housing prices less sensitive to changes in interest rates. Third, the long-term mortgage contracts in the US cannot reflect the impact of frequent short term interest rate fluctuation. Among these three potential hypotheses proposed to explain the lower than expected housing price sensitivity to interest rate changes, land supply elasticity is more relevant to Hong Kong. By focusing on Hong Kong’s housing market, this thesis examines the relationship between land supply elasticity and the sensitivity of housing prices towards interest rate changes. When demand shift due to interest rate change, land supply (and therefore housing supply) may respond accordingly to reduce the impact of interest rate change. The more elastic the supply is, the weaker the housing price sensitivity to interest rate may be. Alternatively, housing prices are more sensitive to interest rate change when land supply is inelastic. To be more precisely, this study provides an empirical test on whether land supply elasticity reduces housing price sensitivity towards interest changes. Two approaches are applied to provide clear pictures of housing price sensitivity. First, data from different housing subsectors with different land supply elasticity are used for the empirical tests. The results show that prices of large units in the Hong Kong Island are significantly more sensitive to interest rate change, compared to those of small units in the New Territories. This is consistent with the implication of our hypothesis because new land for building luxurious units in Hong Kong Island is limited while there are relatively more lands available in the New Territories for smaller mass residential units. Second, in Hong Kong, all new land supply comes from the government in the form of leasehold land. Hence, government’s land supply policy has a major impact on land supply elasticity. For example, there was a period of restricted land supply before the handover in 1997, which effectively reduced land supply elasticity. On the other hand, the Application List land sales system adopted by the Hong Kong government from 2000 to 2013 should increase the flexibility in land supply. Therefore, this study makes use of these policy changes as nature experiments to investigate the effect of land supply elasticity on housing price sensitivity towards interest fluctuation. The results show that housing prices are more sensitive to interest rate change during the land supply restriction period and more insensitive when the Application List was used for land sales. / published_or_final_version / Real Estate and Construction / Doctoral / Doctor of Philosophy

published_or_final_version / Architecture / Master / Master of Architecture