文章中文名称：精细化空间尺度下的国家级新区开发影响要素对比研究

文章英文名称：Comparative Study on the Influencing Factors of State-level New District Development on Refined Spatial Scale

作者信息：

陈会宴  北京清华同衡规划设计研究院有限公司技术创新中心城市未来研究部数据分析师

蔡玉蘅  北京清华同衡规划设计研究院有限公司技术创新中心城市未来研究部规划师

吴纳维  北京清华同衡规划设计研究院有限公司技术创新中心城市未来研究部副部长

褚峤  北京清华同衡规划设计研究院有限公司技术创新中心城市未来研究部规划师

李栋  北京清华同衡规划设计研究院有限公司技术创新中心副主任

摘要：在自然资源部成立的大背景下，国土空间规划的实施监测成为未来城市运营的重点。随着新增建设用地指标发放日趋慎重，对地方政府来说，如何更好地利用公共投资促进开发、提升土地的使用效率，成为了城镇化进入新时期后需要关注的问题。从规划到实施、近期建设时序与土地供应间的衔接、城市政府更精细化的建设资金投入决策，目前都较为缺乏相应的理论与实践来支持。为了探究包括公共服务设施在内的基础设施投入与城市新区用地开发的关联，本文选取了处于不同发展阶段、不同地区的国家级新区，包括浦东新区、滨海新区、两江新区、西咸新区、贵安新区和南沙新区作为研究对象，并基于2015 年、2016 年的土地开发百米网格数据，对涉及开发的影响因子进行对比研究。以6 个新区在2015—2016 年的土地开发面积增长率为因变量，选择与新区开发相关的公共服务设施、各级道路、城市主要功能区等要素的代理数量为自变量，运用自相关Logistic 回归分析模型，定量研究格网尺度下的不同新区影响因子及其作用效果的时空差异。

关键词：国家级新区，精细尺度，共同影响要素，特异影响要素，Autologistic 回归

Abstract ：Under the background of the establishing of Ministry of Natural Resources, implementing and monitoring territorial space planning become the focus for future city operation. With the more cautious attitude toward the release of new construction land, local governments often pay more attention to improving the efficiency of land use by making better use of public investment. To explore the relationship between infrastructure investment including public service facilities and land development in new areas, we selects Pudong New Area, Binhai New Area, Liangjiang New Area, Xixian New Area, Gui'an New District and Nansha New District as the research areas, which are various districts at different developmental stages. The research selected 100-meter grid as the basic researching unit, and then according to a land development threshold, the change of land developed from 2015 to 2016 was divided to developed grid and the undeveloped grid, and were defined by 1 and 0 respectively. We also collected some covariates as driving factors, in which the number and service area of public facilities and infrastructure and related agent variables were included. Considering the concept of spatial correlation, we applied autologistic regression model to explore driving factors of different new area, identifying their common factors and specific factors.

Keywords ：state-level new district, fine-grained scale, common factors, specific factors, autologistic regression