scholarly journals A New Approach to Refining Land Use Types: Predicting Point-of-Interest Categories Using Weibo Check-in Data

2020 ◽  
Vol 9 (2) ◽  
pp. 124 ◽  
Author(s):  
Xucai Zhang ◽  
Yeran Sun ◽  
Anyao Zheng ◽  
Yu Wang
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Urban Planning ◽  
Human Mobility ◽  
Activity Patterns ◽  
Land Use Classification ◽  
Social Sensing ◽  
Point Of Interest ◽  
Land Use Types ◽  
Activity Behavior

The information of land use plays an important role in urban planning and optimizing the allocation of resources. However, traditional land use classification is imprecise. For instance, the type of commercial land is highly filled with the categories of shopping, eating, etc. The number of mixed-use lands is increasingly growing nowadays, and these lands sometimes are too mixed to be well investigated by conventional approaches such as remote sensing technology. To address this issue, we used a new social sensing approach to classify land use according to human mobility and activity patterns. Previous studies used other social sensing approaches to predict land use types at the parcel or the area level, whilst fine-grained point-of-interest (POI)-level land use data are likely to more useful in urban planning. To abridge this research gap, we proposed a new social sensing approach dedicated to classifying land use at a finer scale (i.e., POI-level or building level) according to human mobility and activity patterns reflected by location-based social network (LBSN) data. Specifically, we firstly investigated spatial and temporal patterns of human mobility and activity behavior using check-in data from a popular Chinese LBSN named Sina Weibo and subsequently applied those patterns to predicting the category of POI to refine urban land use classification in Guangzhou, China. In this study, we applied three classification methods (i.e., naive Bayes, support vector machines, and random forest) to recognize category of a certain POI by spatial and temporal features of human mobility and activity behavior as well as POIs’ locational characteristics. Random forest outperformed the other two methods and obtained an overall accuracy of 72.21%. Apart from that, we compared the results of the different rules in filtering check-in samples. The comparison results show that a reasonable rule to select samples is essential for predicting the category of POI. Moreover, the approach proposed in this study can be potentially applied to identifying functions of buildings according to visitors’ mobility and activity behavior and buildings’ locational characteristics.

scholarly journals A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

2021 ◽  
Vol 13 (6) ◽  
pp. 3070
Author(s):  
Patrycja Szarek-Iwaniuk
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Anthropogenic Pressure ◽  
Land Use Land Cover ◽  
Land Use Classification ◽  
Land Cover Data ◽  
Spatial Changes ◽  
Land Use Types

Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.

scholarly journals Social Sensing for Urban Land Use Identification

2020 ◽  
Vol 9 (9) ◽  
pp. 550
Author(s):  
Adindha Anugraha ◽  
Hone-Jay Chu ◽  
Muhammad Ali
Keyword(s):  
Land Use ◽  
Accuracy Assessment ◽  
Urban Land ◽  
Demographic Characteristics ◽  
Urban Land Use ◽  
Land Uses ◽  
Land Use Classification ◽  
Residential Areas ◽  
Social Sensing ◽  
Land Use Maps

The utilization of urban land use maps can reveal the patterns of human behavior through the extraction of the socioeconomic and demographic characteristics of urban land use. Remote sensing that holds detailed and abundant information on spectral, textual, contextual, and spatial configurations is crucial to obtaining land use maps that reveal changes in the urban environment. However, social sensing is essential to revealing the socioeconomic and demographic characteristics of urban land use. This data mining approach is related to data cleaning/outlier removal and machine learning, and is used to achieve land use classification from remote and social sensing data. In bicycle and taxi density maps, the daytime destination and nighttime origin density reflects work-related land uses, including commercial and industrial areas. By contrast, the nighttime destination and daytime origin density pattern captures the pattern of residential areas. The accuracy assessment of land use classified maps shows that the integration of remote and social sensing, using the decision tree and random forest methods, yields accuracies of 83% and 86%, respectively. Thus, this approach facilitates an accurate urban land use classification. Urban land use identification can aid policy makers in linking human activities to the socioeconomic consequences of different urban land uses.

scholarly journals Identifying Urban Functional Zones Using Public Bicycle Rental Records and Point-of-Interest Data

2018 ◽  
Vol 7 (12) ◽  
pp. 459 ◽  
Author(s):  
Xiaoyi Zhang ◽  
Wenwen Li ◽  
Feng Zhang ◽  
Renyi Liu ◽  
Zhenhong Du
Keyword(s):  
Land Use ◽  
Travel Behavior ◽  
Visual Analytics ◽  
Human Mobility ◽  
Residential Areas ◽  
Trip Purpose ◽  
Mobility Data ◽  
Point Of Interest ◽  
Functional Zones ◽  
Urban Functions

Human mobility data have become an essential means to study travel behavior and trip purpose to identify urban functional zones, which portray land use at a finer granularity and offer insights for problems such as business site selection, urban design, and planning. However, very few works have leveraged public bicycle-sharing data, which provides a useful feature in depicting people’s short-trip transportation within a city, in the studies of urban functions and structure. Because of its convenience, bicycle usage tends to be close to point-of-interest (POI) features, the combination of which will no doubt enhance the understanding of the trip purpose for characterizing different functional zones. In our study, we propose a data-driven approach that uses station-based public bicycle rental records together with POI data in Hangzhou, China to identify urban functional zones. Topic modelling, unsupervised clustering, and visual analytics are employed to delineate the function matrix, aggregate functional zones, and present mixed land uses. Our result shows that business areas, industrial areas, and residential areas can be well detected, which validates the effectiveness of data generated from this new transportation mode. The word cloud of function labels reveals the mixed land use of different types of urban functions and improves the understanding of city structures.

scholarly journals Spatiotemporal Patterns of Human Mobility and Its Association with Land Use Types during COVID-19 in New York City

2021 ◽  
Vol 10 (5) ◽  
pp. 344
Author(s):  
Yuqin Jiang ◽  
Xiao Huang ◽  
Zhenlong Li
Keyword(s):  
New York ◽  
Social Media ◽  
Land Use ◽  
New York City ◽  
York City ◽  
Local Governments ◽  
Human Mobility ◽  
Federal State ◽  
Mobility Patterns ◽  
Land Use Types

The novel coronavirus disease (COVID-19) pandemic has impacted every facet of society. One of the non-pharmacological measures to contain the COVID-19 infection is social distancing. Federal, state, and local governments have placed multiple executive orders for human mobility reduction to slow down the spread of COVID-19. This paper uses geotagged tweets data to reveal the spatiotemporal human mobility patterns during this COVID-19 pandemic in New York City. With New York City open data, human mobility pattern changes were detected by different categories of land use, including residential, parks, transportation facilities, and workplaces. This study further compares human mobility patterns by land use types based on an open social media platform (Twitter) and the human mobility patterns revealed by Google Community Mobility Report cell phone location, indicating that in some applications, open-access social media data can generate similar results to private data. The results of this study can be further used for human mobility analysis and the battle against COVID-19.

Pedestrian Count Expansion Methods: Bridging the Gap between Land Use Groups and Empirical Clusters

2019 ◽  
Vol 2673 (5) ◽  
pp. 720-730
Author(s):  
Aditya Medury ◽  
Julia B. Griswold ◽  
Louis Huang ◽  
Offer Grembek
Keyword(s):  
Land Use ◽  
Activity Patterns ◽  
Objective Measures ◽  
Land Use Classification ◽  
Surrounding Area ◽  
Short Term ◽  
Classification Approach ◽  
Set Up ◽  
Volume Estimates

Count expansion methods are a useful tool for creating long-term pedestrian or cyclist volume estimates from short-term counts for safety analysis or planning purposes. Expansion factors can be developed based on the trends from automated counters set up for long periods of time. Evidence has shown that the activity patterns can vary between sites so that there is potential to create more accurate estimates by grouping similar long-term count trends into factor groups. There are two common approaches to developing factor groups in pedestrian and cyclist count expansion studies. The land use classification approach has the advantage of being simple to apply to short-term count locations based on attributes of the surrounding area, but it requires assumptions by the researchers about which characteristics correlate with different activity patterns. Empirical clustering approaches can potentially create more distinct clusters by effectively matching locations with similar patterns, but they do not present an easy way to apply the resulting factor groups to appropriate short-term count sites. This study connects the two approaches and takes advantage of the benefits of both by using objective measures of the surrounding land use to model membership in the empirical cluster groups.

Research on Three Modes of Land Use Classification

2014 ◽  
Vol 584-586 ◽  
pp. 379-382
Author(s):  
Dong Jin Qi
Keyword(s):  
Land Use ◽  
Urban Planning ◽  
Policy Analysis ◽  
Land Use Classification ◽  
Experiential Mode ◽  
Is Value

Analyses main problems of Land Use Classification Standard in China, learns methods of linguistics and policy analysis, proposes three modes of land use classification. Experiential mode based on facts, appraisive mode is value-oriented, prescriptive mode focuses on act. According to the characteristics of three modes, land use classification presents different forms in every stages of urban planning and illuminates the basic methods of urban planning.

scholarly journals Spatiotemporal Variations of Land Use/Cover Changes in Inner Mongolia (China) during 1980–2015

2018 ◽  
Vol 10 (12) ◽  
pp. 4730 ◽  
Author(s):  
Siqin Tong ◽  
Zhenhua Dong ◽  
Jiquan Zhang ◽  
Yongbin Bao ◽  
Ari Guna ◽  
...  
Keyword(s):  
Land Use ◽  
Inner Mongolia ◽  
Environmental Changes ◽  
Land Use Changes ◽  
Land Use Classification ◽  
High Coverage ◽  
Land Use Types ◽  
Before And After ◽  
Variation Rate

Land use/cover change (LUCC) is one of the major environmental changes and has become a hot topic in the study of global change. Based on four land use classification maps, this study used the intensity analysis method to quantitatively monitor the land use changes which occurred in Inner Mongolia during 1980–2015. The results showed that changes occurred although the trends of corresponding land use types were different (increase or decrease), and the land use changes had an obvious increasing or decreasing trend before and after 2000, respectively. Generally, woodland, high-coverage grassland, and moderate-coverage grassland decreased and the other land use types increased during 1980–2015. In addition, the changes had great differences in spatial distribution. The area of grassland had the largest decrease, indicating that the quality of grassland has declined in Inner Mongolia. The variation rate of land use in 1980–1990 was faster than the rates in 1990–2000 and 2000–2015.

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