Identifying Urban Functional Regions by LDA Topic Model with POI Data

鉴定(生物学) 计算机科学 兴趣点 资源(消歧) 功能数据分析 城市规划 数据挖掘 地理 人工智能 机器学习 工程类 计算机网络 植物 生物 土木工程
作者
Yuhao Huang,Lijun Zhang,Haijun Wang,Siqi Wang
标识
DOI:10.1007/978-981-19-8331-3_5
摘要

Identifying Urban Functional Regions (UFR) can achieve the rational aggregation of social resource space, realize urban economic and social functions, promote the deployment of urban infrastructure, radiate and drive the development of surrounding regions, so the identification of urban functional regions can promote the efficient development of cities. However, the traditional functional region identification method is mainly based on remote sensing mapping, which relies more on the natural geographical characteristics of the region to describe and identify the region, while the urban functional region is closely related to human activities, and the traditional functional region identification results are not ideal. Social data includes a series of data that reflect people’s activities and behaviors, such as trajectory data, social media data, and travel data, thus the analysis of social data can more effectively solve the difficulties of traditional mapping and identification. POI (Point of Interest) data, as a typical type of social data, can be used to identify urban functional regions. We apply the LDA topic model to the POI data, and propose a new urban functional region identification method, which makes full use of the POI data to reflect the activity categories of urban populations to characterize the features of regional functions and achieve a high degree of identification of urban functional regions. Through experimental verification on real data, the experimental results show that the proposed method can more accurately identify urban functions, which proves the method reliable.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
情怀应助lily采纳,获得10
5秒前
Hakunamax发布了新的文献求助10
5秒前
7秒前
youbin完成签到 ,获得积分10
7秒前
玄同发布了新的文献求助10
8秒前
_xySH完成签到 ,获得积分10
9秒前
高高如风发布了新的文献求助10
10秒前
香蕉觅云应助认真的世倌采纳,获得10
10秒前
领导范儿应助albert采纳,获得10
11秒前
13秒前
隐形鸣凤发布了新的文献求助20
13秒前
聪明的棉花糖完成签到 ,获得积分10
16秒前
16秒前
大模型应助愤怒的自我采纳,获得10
19秒前
25秒前
自然的绮梅完成签到 ,获得积分10
28秒前
优秀从凝完成签到,获得积分10
30秒前
六十完成签到,获得积分10
30秒前
Han完成签到 ,获得积分10
37秒前
Hello应助科研通管家采纳,获得30
38秒前
烟花应助科研通管家采纳,获得10
38秒前
38秒前
传奇3应助科研通管家采纳,获得10
38秒前
38秒前
41秒前
41秒前
42秒前
小小王完成签到 ,获得积分10
44秒前
lucy完成签到,获得积分10
45秒前
46秒前
46秒前
51秒前
51秒前
欣喜的机器猫完成签到,获得积分20
51秒前
ywongmath完成签到,获得积分10
53秒前
小螃蟹完成签到,获得积分10
54秒前
科研修沟发布了新的文献求助10
56秒前
乔心发布了新的文献求助10
57秒前
59秒前
高分求助中
The three stars each : the Astrolabes and related texts 1070
Manual of Clinical Microbiology, 4 Volume Set (ASM Books) 13th Edition 1000
Sport in der Antike 800
De arte gymnastica. The art of gymnastics 600
少脉山油柑叶的化学成分研究 530
Sport in der Antike Hardcover – March 1, 2015 500
Boris Pesce - Gli impiegati della Fiat dal 1955 al 1999 un percorso nella memoria 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 有机化学 工程类 生物化学 纳米技术 物理 内科学 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 电极 光电子学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 2405772
求助须知:如何正确求助?哪些是违规求助? 2103798
关于积分的说明 5310313
捐赠科研通 1831301
什么是DOI,文献DOI怎么找? 912494
版权声明 560646
科研通“疑难数据库(出版商)”最低求助积分说明 487860