新世纪版《国家普通地图集》的内容策划与结构设计

新世纪版《国家普通地图集》是国家科技基础性工作专项《国家大地图集》系列编研项目之一,它以相对平衡的程度详细表示了水系、地貌、居民地、交通、境界、土质植被等基础地理要素的空间分布。为了适应生态文明建设和新型城镇化发展的要求,增设了地表覆盖图组和城市图组,构建了普通地图集的新型结构模式,本文主要对地图集的编研理论与内容策划、选题原则与顾及因素、结构设计与图幅编排进行了探讨和研究。     

基于GIS与地理探测器的岩溶槽谷石漠化空间分布及驱动因素分析

岩溶区土地石漠化已成为中国西部继沙漠化和水土流失后的第三大生态问题,近年来岩溶槽谷区石漠化表现出增加趋势。通过获取槽谷区石漠化、岩性、坡度、海拔、降雨量、土地利用、人口密度和第一产业生产总值等数据,利用GIS空间分析功能和地理探测器模型,探讨了岩溶槽谷区石漠化空间分布特征及驱动因子。主要结论为：① 岩溶槽谷区总石漠化面积为21323.7 km ²,占研究区土地面积的8.3%,其中轻度、中度和重度石漠化面积分别是11894.8 km ²、8615.8 km ²和813.1 km ²,分别占石漠化面积的55.8%、40.4%和3.8%;② 从石漠化的空间分布来看,槽谷区石漠化主要发生在连续性灰岩中,轻度、中度和重度石漠化面积分别为占槽谷区相应石漠化类型面积的22.1%、22.4%和1.9%;槽谷区石漠化主要发生在15°~25°的坡度范围,轻度、中度和重度石漠化面积分别为占槽谷区相应石漠化类型面积的27.1%、18.2%和2.3%;从海拔来看,主要分布于400~800 m范围内,轻度、中度和重度石漠化面积分别为占槽谷区相应石漠化类型面积的24.9%、18.4%和0.2%;从土地利用类型来看,主要发生于山地旱地中;从人口密度来看,集中分布于100~200人/km ²中;从第一产业生产总值来看,集中分布于25亿~50亿元中;③ 地理探测器的因子探测器揭示了岩性（q = 0.58）、土地利用（q = 0.48）和坡度（q = 0.42）3个因子是槽谷区石漠化形成的主要驱动因子,交互式探测器进一步揭示了岩性与土地利用类型（q = 0.85）、坡度与土地利用类型的组合（q = 0.75）共同驱动槽谷区石漠化的形成。     

贫困村空间分布及影响因素分析——以乌蒙山连片特困区为例

针对目前贫困分布研究以大尺度为主而缺乏对小尺度的关注和致贫因素分析忽略个体效应或背景效应的不足,基于空间贫困视角,以乌蒙山片区为研究区域、贫困村为研究对象,运用空间点模式分析方法探究贫困村空间分布特征,并设计多层线性回归模型从贫困村和县域两个层面综合定量剖析贫困影响因素。研究发现：① 贫困村的空间聚集特征较为明显,总体分布呈现大分散小集中、散点-极核-轴带-团块并存的空间格局。② 贫困村的贫困程度受多层因素的显著影响。其中,村级影响因素为：人口密度、通路率、劳动力比例、遭受自然灾害频次、安全饮用水比例;县级影响因素为：人均地方生产总值、高中阶段毛入学率、植被覆盖率。③ 农村贫困来源于贫困村与县域的双重作用。因此在精准扶贫工作中,政府及相关部门可针对不同尺度对象有针对性地施策,合理配置扶贫资金。     

长江沿江地区化工产业空间格局演化及影响因素

基于企业数据和空间计量方法,探讨了长江沿江地区2000—2013年化工产业空间格局演化及影响因素。结果表明：化工产业主要分布在上海市和江苏沿江地区;中上游部分地区化工产业呈现明显的增长势头,由3个主要热点区发展到20余个不同规模大小的分布热点,成为化工产业新兴“增长极”;城市主城区化工产业减少而远郊区县增加,分布热点变化也反映了化工产业“郊区化”“园区化”的过程;相对低端基础的行业呈现集聚趋势,其中肥料业集聚明显;相对高端精细的行业呈现扩散趋势,其中合成材料业分散化明显;地区化工产业增长受到环境规制、外商投资、交通区位等因素显著影响,外商投资和交通区位具有显著的促进作用,而环境规制呈现显著的抑制效果。     

山东省产业生态化时空分异特征与影响因素——基于17地市时空面板数据

本文综合构建了产业生态化评价指标体系,并利用熵值法、耦合协调分析、探索性空间数据分析方法、空间计量等多种计量方法对2005—2016年山东省17地级市产业生态化水平时空分异特征与影响因素进行了综合研究,结果表明：① 产业生态化依赖于产业化物质消耗型规模增长,但不以结构优化为基础的产业化势必忽视经济要素的横向转移反馈作用与集群效应,导致资源配置的低效现象;② 产业生态化水平逐年递增反映出产业系统与生态环境系统由拮抗向良性共轭方向发展,但其增长率较低说明了产业轻型化与清型化进程滞缓,其质量提升之路漫长而艰巨;③ 产业生态化呈现东中西递减的空间梯度分异特征,东部沿海地区始终是热点区,西部地区尤其是鲁西南地区始终是冷点区;④ 产业生态化水平空间相关性较为显著,经济发展水平、产业结构、外商投资等因素负向溢出效应比较明显,而政府调控以及环境规制强度等因素正向溢出效应突出。     

山区农村土地利用转型与小农经济变迁耦合机理

通过山区土地利用转型与小农经济变迁的耦合关系分析,可以揭示山区人地关系的变化过程和机理。随着工业化、城镇化的快速推进,山区农村人地关系发生深刻变化,小农经济变迁驱动土地利用转型,土地利用转型进一步促进小农经济变迁。在阐述土地利用转型及小农经济变迁内涵和特征基础上,结合实证探讨二者间的耦合机理,以期为乡村振兴战略的实施提供理论支撑和现实参考。研究表明：① 山区农村土地利用转型与小农经济变迁相互影响,耦合演进。② 山区农村土地利用转型重点体现在农村宅基地、耕地及林地三类用地上。③ 中国小农经济已发生四次变迁,而山区小农经济变迁在第四次中表现较为明显,其阶段特征主要为农村人口向城镇迁移,土地流转频繁,土地规模经营趋势增强。④ 山区小农经济变迁引发耕地空间形态及功能转型,山区耕地转型进一步促进小农经济变迁。⑤ 山区小农经济变迁及农户生计策略非农化转变促使农户对宅基地结构及功能的需求发生变化;闲置废弃宅基地综合整治可显化农村土地资产价值,增加农民土地财产性收入,促进小农经济变迁。⑥ 通过云南省砚山县耕地利用转型案例剖析,验证了本文提出的山区农村土地利用转型与小农经济变迁的耦合机理。     

Spatial associations between NDVI and environmental factors in the Heihe River Basin

Journal of Geographical Sciences - The Heihe River Basin is located in the arid and semi-arid regions of Northwest China. Here, the terrestrial ecosystem is vulnerable, making it necessary to...     

Simulating runoff generation and its spatial correlation with environmental factors in Sancha River Basin: The southern source of the Wujiang River

Journal of Geographical Sciences - Runoff generation is an important part of water retention service, and also plays an important role on soil and water retention. Under the background of the...     

Farmland Abandonment Research Progress: Influencing Factors and Simulation Model

Farmland abandonment is a global problem and considered one of the most important areas in land use change research. Farmland abandonment research currently focuses on understanding the factors that affect farmland abandonment and developing scientific models to simulate farmland abandonment. The study reviewed the natural and political factors driving farmland abandonment and summarized the main models for farmland abandonment simulation together with their advantages and disadvantages. We discuss the main ecological effects of farmland abandonment and propose farmland abandonment research directions. The study found that: (1) the influence of labor cost change and ageing labor force on farmland abandonment needs further investigation, (2) simulation models for farmland abandonment must include the decision-making mechanism of individual farmers and focus on macro large-scale abandonment prediction models, and (3) the influence of farmland abandonment on landscape culture must be investigated in detail.     

Temporal Variations and Driving Factors of Cultivated Land Use Intensity in Shandong Province from 1980 to 2015

Based on the cost-income data of agricultural products and relevant statistical data, taking major grain crops and economic crops in the process of cultivated land use as study cases, we studied characteristics of the temporal variation of cultivated land use intensity and its composition in Shandong Province from 1980 to 2015, and then analyzed its main driving factors. The results showed that: (1) The total intensity of major crops in Shandong Province showed a rising trend from 919.73 Yuan ha^-1 in 1980 to 3285.06 Yuan ha^-1 in 2015, and the average annual intensity of economic crops was higher than that of grain crops. The labor cost and material cost of major crops both showed an increasing trend, but the material input was much higher than labor input for grain crops, while the labor input was much higher than material input for economic crops. (2) The labor intensity of major crops in Shandong Province showed a decreasing trend from 501.75 man-day ha^-1 in 1980 to 161.93 man-day ha^-1 in 2015. The labor intensity of grain crops was lower than that of economic crops and its decline rate was fast. On the contrary, the capital intensity showed an increasing trend from 518.33 Yuan ha^-1 in 1980 to 1159.95 Yuan ha^-1 in 2015. In the internal composition of capital intensity, the proportion of yield-increasing inputs such as seed, farmyard manure, fertilizer, pesticide and drainage and irrigation decreased gradually, while the proportion of labor-saving inputs such as agricultural machinery increased significantly. (3) The cultivated land use intensity in Shandong Province had significant negative correlations with the amount of agricultural labor and cultivated land area per capita. The primary direct driving factor was the net income per unit cost of major crops, but the time response lagged by 1-3 years. The main indirect driving factor was the reform of agricultural policy.