Doctoral Dissertation Research: An Empirically Parameterized Road Extension Model for the Support of Land Cover Change Modeling

空间上明确的土地覆盖变化模型和变化预测是环境规划和生物多样性保护的重要工具。在这些模型中，与道路的接近被普遍认为是土地覆盖变化的一个关键解释变量。因此，除非对道路基础设施进行类似的建模和预测，否则土地变化预测将受到严重限制。很少有人尝试对道路发展进行建模，所有这些都需要指定关键参数，这些参数不是通过经验得出的，而是通过反复试验设定的。这项博士论文研究项目将开发一个经验性的参数化道路延伸模型，以规划保留现有空间布局的道路网络的发展。为了实现这一目标，博士生将回答以下问题：(1)如何量化表征道路网络的空间布局？(2)S网络的空间布局与区域特征之间是什么关系？(3)如何将这种关系纳入道路延伸模拟，以复制网络格局？(4)该模型是否改善了道路延伸模拟和土地变化预测，如果是，提高了多少？学生将测量道路网络的拓扑和几何属性以对模式进行分类。将利用道路网模式与区域特征之间的经验关系，通过限制新道路的终点和路线来控制道路发展的预测。预计道路模型将通过给出个别道路的位置来预测道路延伸，同时保持整个公路网的模式。本项目将要开发的新模型将使用1986年至2000年玻利维亚低地公路网增长的数据进行验证。本项目将模拟当前或计划中的发展战略下的道路延伸。将通过该项目开发的模型将加强对未来人类土地利用地点和强度的预测，并有助于确定土地用于生物多样性保护规划的优先顺序。该项目还将为解决土地利用和道路基础设施发展之间的相互作用奠定理论和实践基础。通过在模拟道路延伸时采用土地覆盖特征，该项目将承认道路是土地变化的内生因素。动态集成道路延伸模型的土地变化模型将包含道路和土地系统之间的反馈。这项研究的结果将作为IDRISI地理信息系统和图像处理系统的一部分，纳入未来发布的土地变化生态可持续模型。由于IDRISI是一个非营利性系统，旨在以低成本服务于相关的研究、教育和政府领域，因此这些社区的数千名用户将可以使用道路延伸模型。作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立研究生涯。

Spatially explicit land-cover change modeling and change projection are critical tools for environmental planning and biodiversity conservation. In these models, proximity to roads is universally identified as a critical explanatory variable in land cover change. Land-change projections therefore are severely limited unless the road infrastructure is similarly modeled and projected. Few attempts have been made to model road development and all require the specification of critical parameters that are not empirically derived but set by trial and error. This doctoral dissertation research project will develop an empirically parameterized road-extension model to project the development of a road network that preserves the existing spatial arrangement. To pursue this objective, the doctoral candidate will answer the following questions: (1) How can the spatial arrangement of a road network be quantitatively characterized? (2)What is the relationship between the network''s spatial arrangement and regional characteristics? (3)How can this relationship be incorporated into road extension simulation to replicate the network pattern? (4)Does the model improve road extension simulation and land change projection, and if so, by how much? The student will measure topological and geometric attributes of a road network to classify patterns. Empirical relationships between the road network patterns and regional characteristics will be used to control the projection of road development through confining location of endpoints and routes of new roads. The road model is expected to project the road extension by giving the location of individual roads while maintaining the pattern of the entire road network. The new model to be developed in this project will be validated using data on the growth of the road network in lowland Bolivia from 1986 to 2000.This project will simulate the road extension under the current or planned development strategies. The model to be developed through this project will enhance predictions regarding the location and the intensity of future human land use and help to prioritize land for biodiversity conservation planning. The project also will lay a foundation for theoretically and practically addressing the interplay between land use and road infrastructure development. By embracing land-cover characteristics in modeling road extension, the project will acknowledge roads as an endogenous factor of land change. A land-change model that dynamically integrates the road-extension model will incorporate the feedback between the road and land systems. The results of the research will be incorporated into a future release of the Land Change Modeler for Ecological Sustainability as part of the IDRISI GIS and Image Processing System. Because IDRISI is a non-profit system intended to serve related research, education, and government fields at low cost, the road-extension model thus will be made available to thousands of users in these communities. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.