scholarly journals ANÁLISE DAS VARIAÇÕES ESPACIAIS NO MUNICÍPIO DE PONTAL DO PARANÁ (PARANÁ – BRASIL), ENTRE OS ANOS DE 1980 e 2032 DECORRENTES DA INSTALAÇÃO DO COMPLEXO PORTUÁRIO

2020 ◽  
Vol 15 (02) ◽  
pp. 263-290
Author(s):  
Jean Jesus Ilsuk da Silva ◽  
Sony Cortese Caneparo
Keyword(s):  
Land Use ◽  
Predictive Modeling ◽  
Urban Areas ◽  
Temporal Dynamics ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Palabras Clave ◽  
The Future ◽  
Clark University ◽  
Spatio Temporal

O município de Pontal do Paraná está localizado no litoral do estado do Paraná, na região sul do Brasil. Em 1995, foi aí instalado o Porto de Pontal Importação e Exportação LTDA e, em 2013, foi aprovada a licença ambiental para a construção de um complexo portuário neste município. Tal obra se apresenta como um desafio, devido ao potencial que o mesmo apresenta em produzir impactos ambientais e mudanças nos padrões de uso da terra.Essa pesquisa objetiva analisar as mudanças espaciais que podem ocorrer futuramente no uso da terra e na cobertura vegetal em Pontal do Paraná (2032), em virtude da instalação deste complexo. Foram utilizadas rotinas de sistemas de informações geográficas, inseridas no IDRISI TAIGA, da Clark University, dentre elas se destacam a  Cadeia de Markov e os Autômatos Celulares para a geração do cenário futuro. O resultado da modelagem preditiva (2032), em função da expansão portuária, foi um aumento nas áreas urbanas, fator que poderia impactar diretamente as áreas de Restingas, de Mangues e da Floresta Ombrófila Densa. O presente trabalho revelou que o uso da modelagem preditiva pode ser uma ferramenta bastante útil para a avaliação e interpretação de cenários futuros. Palavras-chave: Modelagem Preditiva; Ambiente Litorâneo; Dinâmica Espaço-Temporal.   ANALYSIS OF SPACE VARIATIONS IN THE MUNICIPALITY OF PONTAL DO PARANÁ (PARANÁ - BRAZIL), BETWEEN 1980 AND 2032 ARISING FROM THE PORT COMPLEX INSTALLATION Abstract The city of Pontal do Paraná is located on the coast of the state of Paraná, in the southern region of Brazil. In 1995, the Port of Pontal Importação e Exportação Company was installed there, and in 2013, the environmental license was approved for the installation of a port complex in this municipality. This Port presents itself as a challenge, due to its potential in producing environmental impacts and changes in land use patterns. This research aims to analyze the spatial changes that may occur in the future of land use and vegetation cover of Pontal do Paraná (2032), due to the installation of this complex. Routines of geographic information systems, inserted in the IDRISI TAIGA, of Clark University, among them, the Markov Chain and the Cellular Automatics were used to generate the future scenario. The result of predictive modeling (2032), caused by the port expansion, was an increase in urban areas, a factor that could directly impact the areas of restingas,  mangroves, and the atlantic rainforest. The present study revealed that the use of predictive modeling can be a very useful tool for the evaluation and interpretation of future scenarios. Keywords: Predictive Modeling; Coastal Environment; Spatio-Temporal Dynamics.   ANÁLISIS DE LAS VARIACIONES ESPACIALES EN EL MUNICIPIO DE PONTAL DO PARANÁ (PARANÁ - BRASIL), ENTRE LOS AÑOS 1980 Y 2032 RESULTANTE DE LA INSTALACIÓN DEL COMPLEJO PORTUARIO Resumen El municipio de Pontal do Paraná está ubicado en la costa del estado de Paraná, en la región sur de Brasil. En 1995, se instaló el Puerto de Importación y Exportación de Pontal Ltd. y, en 2013, se aprobó el permiso ambiental para la construcción de un complejo portuario en este municipio. Esta obra se presenta como un desafío, debido a la posibilidad  de producir impactos ambientales y cambios en los patrones de uso de la tierra. El objetivo de este estudio es analizar los cambios espaciales que puedan ocurrir en el futuro uso de la tierra y la vegetación en el Pontal do Paraná (2032), debido a la instalación de este complejo. Las rutinas se utilizan sistemas de información geográfica, insertado en el IDRISI TAIGA, Clark University, entre ellos se encuentran la Cadena de Markov y Autómatas Celulares para la generación de escenarios futuros. Los resultados de la modelización predictiva (2032), dependiendo de la expansión de lo puerto, fue un aumento en las zonas urbanas, un factor que podría tener un impacto directo sobre las áreas de Restinga, Manglares y Bosque Ombrophilous Denso. El presente estudio demostró que el uso de modelado predictivo puede ser una herramienta muy útil para la evaluación e interpretación de escenarios futuros. Palabras clave: Modelado Predictivo; Costero; Dinámica Espacio-Temporal.

scholarly journals Current and Future Land Use Characters of a National Central City in Eco-Fragile Region—A Case Study in Xi’an City Based on FLUS Model

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 286
Author(s):  
Dingrao Feng ◽  
Wenkai Bao ◽  
Meichen Fu ◽  
Min Zhang ◽  
Yiyu Sun
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Urban Development ◽  
Urban Expansion ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Pattern Change ◽  
Land Use Dynamics ◽  
The Future ◽  
Xi’An City

Land use change plays a key role in terrestrial systems and drives the process of ecological pattern change. It is important to investigate the process of land use change, predict land use patterns, and reveal the characteristics of land use dynamics. In this study, we adopted the Markov model and future land use (FLUS) model to predict the future land use conditions in Xi’an city. Furthermore, we investigated the characteristics of land use change from a novel perspective, i.e., via establishment of a complex network model. This model captured the characteristics of the land use system during different periods. The results indicated that urban expansion and cropland loss played an important role in land use pattern change. The future gravity center of urban development moved along the opposite direction to that from 2000 to 2015 in Xi’an city. Although the rate of urban expansion declined in the future, urban expansion remained the primary driver of land use change. The primary urban development directions were east-southeast (ENE), north-northeast (NNE) and west-southwest (WSW) from 1990 to 2000, 2000 to 2015, and 2015 to 2030, respectively. In fact, cropland played a vital role in land use dynamics regarding all land use types, and the stability of the land use system decreased in the future. Our study provides future land use patterns and a novel perspective to better understand land use change.

SOME SURVEYING AND MAPPING ASPECTS OF LAND-USE PLANNING

1962 ◽  
Vol 16 (5) ◽  
pp. 248-254
Author(s):  
N. L. Nicholson
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Urban Areas ◽  
Aerial Photographs ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Block Methods ◽  
Surveying And Mapping

In recording actual land use field-by-field or block-by-block methods can be applied either to detailed maps or to aerial photographs. In land-use mapping the usual scales are 1:25,000 for urban areas, 1:50,000 for densely settled areas with complex land-use patterns, and 1:250,000 for sparsely settled areas. In preparation of the map manuscript the base sheet used is of the material known as cronaflex and the transfer and reduction of information is achieved by use of the “reflex map reducer” a device invented by a geographer on the staff of the Geographical Branch.

scholarly journals Exploring the Spatial and Temporal Relationship between Air Quality and Urban Land-Use Patterns Based on an Integrated Method

2020 ◽  
Vol 12 (7) ◽  
pp. 2964 ◽  
Author(s):  
Chia-An Ku
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Urban Areas ◽  
Temporal Relationship ◽  
Urban Land ◽  
Urban Land Use ◽  
Air Pollutant ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Mitigation Planning

The deterioration of air quality in urban areas is often closely related to urbanization, as this has led to a significant increase in energy consumption and the massive emission of air pollutants, thereby exacerbating the current state of air pollution. However, the relationship between urban development and air quality is complex, thus making it difficult to be analyzed using traditional methods. In this paper, a framework integrating spatial analysis and statistical methods (based on 170 regression models) is developed to explore the spatial and temporal relationship between urban land use patterns and air quality, aiming to provide solid information for mitigation planning. The thresholds for the influence of urban patterns are examined using different buffer zones. In addition, the differences in the effects of various types of land use pattern on air quality were also explored. The results show that there were significant differences between 1999 and 2013 with regards to the correlations between land use patterns and air pollutant concentrations. Among all land uses, forest, water and built-up areas were proved to influence concentrations the most. It is suggested that the developed framework should be applied further in the real-world mitigation planning decision-making process

scholarly journals An innovative modeling approach of linking land use patterns with soil antibiotic contamination in peri-urban areas

2020 ◽  
Vol 134 ◽  
pp. 105327 ◽  
Author(s):  
Fangkai Zhao ◽  
Liding Chen ◽  
Haw Yen ◽  
Gang Li ◽  
Long Sun ◽  
...  
Keyword(s):  
Land Use ◽  
Urban Areas ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Modeling Approach

Detecting the Development of Land Use Patterns for Building in Urban Areas by Using High Resolution Image

2013 ◽  
Vol 15 (3) ◽  
pp. 160
Author(s):  
A Amar
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Urban Areas ◽  
Rapid Development ◽  
Secondary Data ◽  
Survey Method ◽  
Field Observations ◽  
Land Use Patterns ◽  
Spatial Image ◽  
Use Patterns

This study aimed at obtaining factual information and overview to the development of land use patterns for buildings in urban areas by interval time period, both spatially and aspatially, by utilizing high-resolution satellite photo image (high resolution spatial image) combined with field observations. This research used survey method approach. The data of this study consisted of primary and secondary data classified into spatial and aspatial data in the form of time series obtained through documents recording techniques, field observations, previous mapping sources, as well as depth interviews. The analysis technique used Image Processing Analysis through programs and software Arc View. The result of research showed that there was a quite rapid development of land use patterns for building in Palu within the last 50 years (≤ 1970 till 2010) It had building addition in 65,173 units (82.28%), from 14,032 units in ≤1970 to 79,205 units in 2010, and the addition of extensive use of land for building was 4723.52 ha (89.06%), from 516.98 ha in ≤ 1970 to 4723.52 ha in 2010. The development level of land use patterns for building was getting along with the size of distribution and population growth in Palu.

scholarly journals Pre-event water contributions and streamwater residence times in different land use settings of the transboundary mesoscale Lužická Nisa catchment

2017 ◽  
Vol 65 (2) ◽  
pp. 154-164 ◽  
Author(s):  
Martin Šanda ◽  
Pavlína Sedlmaierová ◽  
Tomáš Vitvar ◽  
Christina Seidler ◽  
Matthias Kändler ◽  
...  
Keyword(s):  
Land Use ◽  
Urban Areas ◽  
Storm Event ◽  
Residence Times ◽  
Runoff Generation ◽  
Land Use Patterns ◽  
Agricultural Catchments ◽  
Use Patterns ◽  
The Mean ◽  
Land Use Control

AbstractThe objective of the study was to evaluate the spatial distribution of peakflow pre-event water contributions and streamwater residence times with emphasis on land use patterns in 38 subcatchments within the 687 km2large mesoscale transboundary catchment Lužická Nisa. Mean residence times between 8 and 27 months and portions of pre-event water between 10 and 97% on a storm event peakflow were determined, using18O data in precipitation and streamwater from a weekly monitoring of nearly two years. Only a small tracer variation buffering effect of the lowland tributaries on the main stem was observed, indicating the dominant impact on the mountainous headwaters on the runoff generation. Longest mean streamwater residence times of 27 months were identified in the nearly natural headwaters of the Jizera Mountains, revealing no ambiguous correlation between the catchment area and altitude and the mean residence time of streamwater. Land use control on the pre-event water portions were determined for three land use categories with percentage of urban areas from 0 to 10%, 10 to 20% and more than 20%. The fraction of pre-event water in the first category decreases from 97% to 65% with the increasing percentage of forest from 76% to 100%, revealing that forests may provide only a limited infiltration of precipitation due to leaf interception and soil water use for transpiration. Fractions of pre-event water of 39–87% in the second (agricultural catchments) and of 10–35% in the third (urbanized catchments) category increase with percentage of non-urban areas.

scholarly journals Modeling the relative role of human mobility, land-use and climate factors on dengue outbreak emergence in Sri Lanka

2018 ◽  
Author(s):  
Ying Zhang ◽  
Jefferson Riera ◽  
Kayla Ostrow ◽  
Sauleh Siddiqui ◽  
Harendra de Silva ◽  
...  
Keyword(s):  
Land Use ◽  
Sri Lanka ◽  
Vector Control ◽  
Urban Areas ◽  
Temporal Dynamics ◽  
Human Mobility ◽  
Relative Role ◽  
Land Use Patterns ◽  
Modeling Framework ◽  
Dengue Outbreak

AbstractBackgroundMore than 80,000 dengue cases including 215 deaths were reported nationally in less than seven months between 2016-2017, a fourfold increase in the number of reported cases compared to the average number over 2010-2016. The region of Negombo, located in the Western province, experienced the greatest number of dengue cases in the country and is the focus area of our study, where we aim to capture the spatial-temporal dynamics of dengue transmission.MethodsWe present a statistical modeling framework to evaluate the spatial-temporal dynamics of the 2016-2017 dengue outbreak in the Negombo region of Sri Lanka as a function of human mobility, land-use, and climate patterns. The analysis was conducted at a 1 km × 1 km spatial resolution and a weekly temporal resolution.ResultsOur results indicate human mobility to be a stronger indicator for local outbreak clusters than land-use or climate variables. The minimum daily temperature was identified as the most influential climate variable on dengue cases in the region; while among the set of land-use patterns considered, urban areas were found to be most prone to dengue outbreak, followed by areas with stagnant water and then coastal areas. The results are shown to be robust across spatial resolutions.ConclusionsOur study highlights the potential value of using travel data to target vector control within a region. In addition to illustrating the relative relationship between various potential risk factors for dengue outbreaks, the results of our study can be used to inform where and when new cases of dengue are likely to occur within a region, and thus help more effectively and innovatively, plan for disease surveillance and vector control.

scholarly journals Experiment in Finding Look-Alike European Cities Using Urban Atlas Data

2020 ◽  
Vol 9 (6) ◽  
pp. 406
Author(s):  
Zdena Dobesova
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Mining ◽  
Land Use ◽  
Urban Areas ◽  
Nearest Neighbor ◽  
Land Use Patterns ◽  
Use Patterns ◽  
European Cities ◽  
Trained Neural Network

The integration of geography and machine learning can produce novel approaches in addressing a variety of problems occurring in natural and human environments. This article presents an experiment that identifies cities that are similar according to their land use data. The article presents interesting preliminary experiments with screenshots of maps from the Czech map portal. After successfully working with the map samples, the study focuses on identifying cities with similar land use structures. The Copernicus European Urban Atlas 2012 was used as a source dataset (data valid years 2015–2018). The Urban Atlas freely offers land use datasets of nearly 800 functional urban areas in Europe. To search for similar cities, a set of maps detailing land use in European cities was prepared in ArcGIS. A vector of image descriptors for each map was subsequently produced using a pre-trained neural network, known as Painters, in Orange software. As a typical data mining task, the nearest neighbor function analyzes these descriptors according to land use patterns to find look-alike cities. Example city pairs based on land use are also presented in this article. The research question is whether the existing pre-trained neural network outside cartography is applicable for categorization of some thematic maps with data mining tasks such as clustering, similarity, and finding the nearest neighbor. The article’s contribution is a presentation of one possible method to find cities similar to each other according to their land use patterns, structures, and shapes. Some of the findings were surprising, and without machine learning, could not have been evident through human visual investigation alone.

Exploring Taxi Demand Distribution of Comprehensive Land-Use Patterns Using Online Car-Hailing Data and Points of Interest in Chengdu, China

2021 ◽  
pp. 036119812110152
Author(s):  
Weijie Yu ◽  
Wei Wang ◽  
Xuedong Hua ◽  
Xueyan Wei
Keyword(s):  
Land Use ◽  
Urban Areas ◽  
Land Use Intensity ◽  
Land Use Patterns ◽  
Land Use Pattern ◽  
Traffic Demand ◽  
Use Patterns ◽  
Demand Distribution ◽  
Points Of Interest ◽  
Use Pattern

With the rapid advance of urbanization, land-use intensity is increasing, and various land-use forms gather to form comprehensive land-use patterns. Traffic demand shows variability and complexity under comprehensive land-use patterns. Accurate analysis of traffic demand in urban transportation is the key to active traffic control and road guidance. Researchers have widely studied the relationship between traffic demand and land-use patterns, while land-use intensity is ignored when classifying land-use patterns, and the traffic demand distribution in each land-use pattern is not studied specifically. Taxi is a flexible public mode in urban areas, and taxi demand is an important component in analyzing traffic demand and identifying traffic hotspots in cities. This paper explores taxi demand distribution of comprehensive land-use patterns using online car-hailing data and points of interest (POI) in Chengdu, China. The demand-driven traffic analysis zones are developed by clustering origin–destination points of online car-hailing services. Using POI data, comprehensive land-use patterns are classified with land-use forms and land-use intensity. The K-shape algorithm is adopted to extract the typical taxi demand distribution in each comprehensive land-use pattern. Finally, two indicators, total taxi demand (TTD) and taxi demand difference (TDD), are computed and further analyzed. Results show that taxi demand distribution is still differential even under the same land-use pattern. Three land-use patterns whose average hourly taxi demand reaches about 300 vehicles per square kilometer have the largest TTD and most uneven TDD. The findings can support traffic management, land-use combination, and land-use adjustment to avoid concentrated taxi demand and mismatched TDD.

The Archaeology of Group Size

2010 ◽  
Author(s):  
Matt Grove
Keyword(s):  
Land Use ◽  
Human Evolution ◽  
Group Size ◽  
Human Populations ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Archaeological Data ◽  
Population Sizes ◽  
Trajectories Of Change ◽  
Spatio Temporal

This chapter aims to summarize the results of recent research producing estimates of hominin range areas, population sizes, and land use patterns based on archaeological data. Estimates of such variables are essential to any geographic or demographic discussion of human evolution, yet at present no generally applicable quantitative method is available to link them to the often abundant data of the archaeological record. Such data offer a unique window onto the patterns of adaptation characterizing prehistoric human populations, and developing a generic method to describe trajectories of change will allow researchers to compare range areas, population sizes and land use patterns between different regions and periods from throughout the vast spatio-temporal range of human evolution. The discussion gives particular emphasis to estimating a trajectory of group size through time from shortly after 2 million years ago until approximately 14,000 years ago.

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