Geocarto International国际地球制图

Geocarto International(英文缩写 GEOCARTO INT),ISSN 1010-6049,eISSN 1752-0762,中文译名:国际地球制图 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。

2026 年数据 · 影响因子
3.900
JCR 分区
Q2
CAS 分区
B4
近一年发文量
0
本站 PubMed 收录统计

发文量统计区间:2025-10-01 至 2026-09-30,按本站收录文献的发表日期统计。

ISSN: 1010-6049 · eISSN: 1752-0762 · 缩写: GEOCARTO INT ·中文: 国际地球制图

期刊介绍

选择期刊介绍栏目

期刊简介

Geocarto International 是一本国际性地球科学与遥感领域期刊,聚焦遥感技术在地球观测、资源调查与环境监测中的应用。主要发表遥感方法、地理信息系统、空间数据分析及地球科学交叉研究论文,读者群包括遥感科学家、地理信息研究者、环境监测人员及相关工程技术人员。

研究方向

期刊涵盖遥感传感器与平台、图像处理与分类、地理信息系统集成、土地利用与覆盖变化、环境与灾害监测、资源勘探等方向。论文类型以原创研究为主,兼有方法综述和案例应用,强调空间信息获取与分析技术的创新及实际应用价值。

期刊特色

研究取向偏重遥感与地理空间技术的应用创新,论文通常结合具体区域或专题数据,注重方法可操作性和结果验证。适合从事遥感应用、地理信息科学、环境遥感及地球系统研究的学者和技术人员投稿与阅读。

投稿难度

投稿难度中等偏上,期刊对遥感应用创新性和数据可靠性有一定要求。建议作者突出方法在真实场景中的验证效果,清晰说明数据来源与处理流程,并注意与已有研究的对比分析,以提升录用可能性。

历年影响因子趋势

JCR 数据年份影响因子JCR 分区
20213.450Q2
20223.800Q2
20233.300Q2
20243.500Q2
20253.900Q2

Geocarto International 最新收录文献

  1. JCR分区: Q2 CAS分区: B4 影响因子: 3.9

    1. Environmental Public Health Applications Using Remotely Sensed Data.

    作者:
    Mohammad Z Al-Hamdan, William L Crosson, Sigrid A Economou, Maurice G Estes, Sue M Estes, Sarah N Hemmings, Shia T Kent, Mark Puckett, Dale A Quattrochi, Douglas L Rickman, Gina M Wade, Leslie A McClure
    日期:
    2014-01-01

    We describe a remote sensing and GIS-based study that has three objectives: (1) characterize fine particulate matter (PM), insolation and land surface temperature using NASA satellite observations, EPA ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes; and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.

  2. JCR分区: Q2 CAS分区: B4 影响因子: 3.9

    2. Geomasking sensitive health data and privacy protection: an evaluation using an E911 database.

    作者:
    William B Allshouse, Molly K Fitch, Kristen H Hampton, Dionne C Gesink, Irene A Doherty, Peter A Leone, Marc L Serre, William C Miller
    日期:
    2010-10-01

    Geomasking is used to provide privacy protection for individual address information while maintaining spatial resolution for mapping purposes. Donut geomasking and other random perturbation geomasking algorithms rely on the assumption of a homogeneously distributed population to calculate displacement distances, leading to possible under-protection of individuals when this condition is not met. Using household data from 2007, we evaluated the performance of donut geomasking in Orange County, North Carolina. We calculated the estimated k-anonymity for every household based on the assumption of uniform household distribution. We then determined the actual k-anonymity by revealing household locations contained in the county E911 database. Census block groups in mixed-use areas with high population distribution heterogeneity were the most likely to have privacy protection below selected criteria. For heterogeneous populations, we suggest tripling the minimum displacement area in the donut to protect privacy with a less than 1% error rate.

  3. JCR分区: Q2 CAS分区: B4 影响因子: 3.9

    3. Using remotely sensed data for census surveys and population estimation in developing countries: examples from Nigeria.

    作者:
    P O Adeniyi
    日期:
    1987

    "Using examples from Nigeria, this paper demonstrates how remotely sensed data can be used to acquire some of the basic data requirements for census surveys and to estimate population. The result obtained shows that visual identification of settlements on Landsat MSS and TM is more accurate and economical than equivalent digital classification techniques. Black and white aerial photographs were used to estimate the population of a model town and to establish EAs [enumeration areas]. The population estimation method employed can be used to obtain intercensal population estimates for the rapidly growing central places, while the established EAs for the study area have created a permanent base for future census surveys and census cross-validation, population estimation and other social surveys."

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