scholarly journals Estimating Hilly Areas Population Using a Dasymetric Mapping Approach: A Case of Sri Lanka’s Highest Mountain Range

2019 ◽  
Vol 8 (4) ◽  
pp. 166 ◽  
Author(s):  
Ananda Karunarathne ◽  
Gunhak Lee
Keyword(s):  
Land Use ◽  
Population Distribution ◽  
Estimation Methods ◽  
Mountain Range ◽  
Dasymetric Mapping ◽  
Ancillary Data ◽  
Hilly Area ◽  
Fine Grain ◽  
Population Distributions ◽  
Gis Environment

Since populations in the developing world have been rapidly increasing, accurately determining the population distribution is becoming more critical for many countries. One of the most widely used population density estimation methods is dasymetric mapping. This can be defined as a precise method for areal interpolation between different spatial units. In most applications of dasymetric mapping, land use and land cover data have been considered as ancillary data for the areal disaggregation process. This research presents an alternative dasymetric approach using area specific ancillary data for hilly area population mapping in a GIS environment. Specifically, we propose a Hilly Area Dasymetric Mapping (HDM) technique by combining topographic variables and land use to better disaggregate hilly area population distribution at fine-grain division of ancillary units. Empirical results for Sri Lanka’s highest mountain range show that the combined dasymetric approach estimates hilly area population most accurately and because of the significant association that is found to exist between topographic variables and population distribution within this setting. This research is expected to have significant implications for national and regional planning by providing useful information about actual population distributions in environmentally hazardous and sparsely populated areas.

scholarly journals Improving Urban Population Distribution Models with Very-High Resolution Satellite Information

Data ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 13 ◽  
Author(s):  
Taïs Grippa ◽  
Catherine Linard ◽  
Moritz Lennert ◽  
Stefanos Georganos ◽  
Nicholus Mboga ◽  
...  
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Population Distribution ◽  
Urban Land Use ◽  
Added Value ◽  
Dasymetric Mapping ◽  
Distribution Models ◽  
Satellite Information ◽  
Lower Accuracy ◽  
Very High

Built-up layers derived from medium resolution (MR) satellite information have proven their contribution to dasymetric mapping, but suffer from important limitations when working at the intra-urban level, mainly due to their difficulty in capturing the whole range of variation in terms of built-up densities. In this regard, very-high resolution (VHR) remote sensing is known for its ability to better capture small variations in built-up densities and to derive detailed urban land use, which plead in favor of its use when mapping urban populations. In this paper, we compare the added value of various combinations of VHR data sets, compared to a MR one. A top-down dasymetric mapping strategy is applied to reallocate population counts from administrative units into a regular 100 × 100 m grid, according to different weighting layers. These weighting layers are created from MR and/or VHR input data, using simple built-up proportion or reallocation “weights”, obtained from a set of multiple ancillary data used to train a Random Forest regression model. The results reveal that (1) a built-up mask derived from VHR can improve the accuracy of the reallocation by roughly 13%, compared to MR; (2) using VHR land-use information alone results in lower accuracy than using a MR built-up mask; and (3) there is a clear complementarity between VHR land cover and land use.

scholarly journals A Disease Control-Oriented Land Cover Land Use Map for Myanmar

Data ◽  
2021 ◽  
Vol 6 (6) ◽  
pp. 63
Author(s):  
Dong Chen ◽  
Varada Shevade ◽  
Allison Baer ◽  
Jiaying He ◽  
Amanda Hoffman-Hall ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Population Distribution ◽  
Large Spatial Scale ◽  
The Public ◽  
Spatial Coverage ◽  
Vector Borne ◽  
Do So ◽  
Control And Eradication

Malaria is a serious infectious disease that leads to massive casualties globally. Myanmar is a key battleground for the global fight against malaria because it is where the emergence of drug-resistant malaria parasites has been documented. Controlling the spread of malaria in Myanmar thus carries global significance, because the failure to do so would lead to devastating consequences in vast areas where malaria is prevalent in tropical/subtropical regions around the world. Thanks to its wide and consistent spatial coverage, remote sensing has become increasingly used in the public health domain. Specifically, remote sensing-based land cover/land use (LCLU) maps present a powerful tool that provides critical information on population distribution and on the potential human-vector interactions interfaces on a large spatial scale. Here, we present a 30-meter LCLU map that was created specifically for the malaria control and eradication efforts in Myanmar. This bottom-up approach can be modified and customized to other vector-borne infectious diseases in Myanmar or other Southeastern Asian countries.

scholarly journals Research on Human Travel Correlation for Urban Transport Planning Based on Multisource Data

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 195
Author(s):  
Hua Chen ◽  
Ming Cai ◽  
Chen Xiong
Keyword(s):  
Travel Demand ◽  
Population Distribution ◽  
Human Mobility ◽  
Urban Transport ◽  
License Plate ◽  
Traffic Patterns ◽  
Trajectory Data ◽  
Transport Planning ◽  
Population Distributions ◽  
Single Dataset

With the rapid development of positioning techniques, a large amount of human travel trajectory data is collected. These datasets have become an effective data resource for obtaining urban traffic patterns. However, many traffic analyses are only based on a single dataset. It is difficult to determine whether a single-dataset-based result can meet the requirement of urban transport planning. In response to this problem, we attempted to obtain traffic patterns and population distributions from the perspective of multisource traffic data using license plate recognition (LPR) data and cellular signaling (CS) data. Based on the two kinds of datasets, identification methods of residents’ travel stay point are proposed. For LPR data, it was identified based on different vehicle speed thresholds at different times. For CS data, a spatiotemporal clustering algorithm based on time allocation was proposed to recognize it. We then used the correlation coefficient r and the significance test p-values to analyze the correlations between the CS and LPR data in terms of the population distribution and traffic patterns. We studied two real-world datasets from five working days of human mobility data and found that they were significantly correlated for the stay and move population distributions. Then, the analysis scale was refined to hour level. We also found that they still maintain a significant correlation. Finally, the origin–destination (OD) matrices between traffic analysis zones (TAZs) were obtained. Except for a few TAZs with poor correlations due to the fewer LPR records, the correlations of the other TAZs remained high. It showed that the population distribution and traffic patterns computed by the two datasets were fairly similar. Our research provides a method to improve the analysis of complex travel patterns and behaviors and provides opportunities for travel demand modeling and urban transport planning. The findings can also help decision-makers understand urban human mobility and can serve as a guide for urban management and transport planning.

scholarly journals Exploring Urban Spatial Feature with Dasymetric Mapping Based on Mobile Phone Data and LUR-2SFCAe Method

2018 ◽  
Vol 10 (7) ◽  
pp. 2432 ◽  
Author(s):  
Lingbo Liu ◽  
Zhenghong Peng ◽  
Hao Wu ◽  
Hongzan Jiao ◽  
Yang Yu
Keyword(s):  
Land Use ◽  
Spatial Heterogeneity ◽  
Mobile Phone ◽  
Census Data ◽  
Base Station ◽  
Distance Decay ◽  
Base Stations ◽  
Mobile Phone Data ◽  
Dasymetric Mapping ◽  
Spatial Feature

Dasymetric mapping of high-resolution population facilitates the exploration of urban spatial feature. While most relevant studies are still challenged by weak spatial heterogeneity of ancillary data and quality of traditional census data, usually outdated, costly and inaccurate, this paper focuses on mobile phone data, which can be real-time and precise, and also strengthens spatial heterogeneity by its massive mobile phone base stations. However, user population recorded by mobile phone base stations have no fixed spatial boundary, and base stations often disperse in extremely uneven spatial distribution, this study defines a distance-decay supply–demand relation between mobile phone user population of gridded base station and its surrounding land patches, and outlines a dasymetric mapping method integrating two-step floating catchment area method (2SFCAe) and land use regression (LUR). The results indicate that LUR-2SFCAe method shows a high fitness of regression, provides population mapping at a finer scale and helps identify urban centrality and employment subcenters with detailed worktime and non-worktime populations. The work involving studies of dasymetric mapping based on LUR-2SFCAe method and mobile phone data proves to be encouraging, sheds light on the relationship between mobile phone users and nearby land use, brings about an integrated exploration of 2SFCAe in LUR with distance-decay effect and enhances spatial heterogeneity.

scholarly journals Water erosion vulnerability and sediment delivery rate in upper Iguaçu river basin – Paraná

RBRH ◽  
2016 ◽  
Vol 21 (4) ◽  
pp. 728-741 ◽  
Author(s):  
Matheus Fonseca Durães ◽  
José Alexandre Pinto Coelho Filho ◽  
Vinícius Augusto de Oliveira
Keyword(s):  
Land Use ◽  
River Basin ◽  
Water Erosion ◽  
Sustainable Land Use ◽  
Delivery Rate ◽  
Sediment Delivery ◽  
Solid Discharge ◽  
Risk Areas ◽  
Gis Environment ◽  
Iguaçu River

ABSTRACT Soil erosion is one of the most striking environmental degradation processes, which its mapping and assessment is an important tool for management activities and natural resource management in river basins, allowing managers to implement policies and sustainable land use occupation. This work aimed to apply the Revised Universal Soil Loss Equation (RUSLE) in a GIS environment in the upper Iguaçu river basin, located at Paraná State, in order to assess the vulnerability to water erosion as well as the concentration of dissolved solids in suspension to estimate the solid discharge and sediment delivery rate, allowing the identification of more susceptible areas to water erosion. The results showed that over 23.52% of the upper Iguaçu river basin presented soil losses below 2.5 t ha–1 yr–1, meaning current low potential for erosion. Regarding the solid discharge, the basin has values ranging from low to very high, also leading to high values for sediment delivery rate. The identification of risk areas associated with accelerated erosion, carried out in this study provide important information for measures associated with the management, conservation and planning of land use in the basin, which is highly relevant for predicting development of various scenarios for the state Paraná for its hydroelectric potential.

scholarly journals Modeling Urban Futures: Data-Driven Scenarios of Climate Change and Vulnerability in Cities

2021 ◽  
pp. 129-144
Author(s):  
L. Ortiz ◽  
A. Mustafa ◽  
B. Rosenzweig ◽  
Timon McPhearson
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Cellular Automata ◽  
Land Use Change ◽  
Numerical Models ◽  
Flood Hazards ◽  
Ancillary Data ◽  
Road Systems ◽  
Urban Futures ◽  
Land Use Development

AbstractCities are complex systems where social, ecological, and technological processes are deeply coupled. This coupling complicates urban planning and land use development, as changing one facet of the urban fabric will likely impact the others. As cities grapple with climate change, there is a growing need to envision urban futures that not only address more frequent and intense severe weather events but also improve day-to-day livability. Here we examine climate risks as functions of the local land use with numerical models. These models leverage a wide array of data sources, from satellite imagery to tax assessments and land cover. We then present a machine-learning cellular automata approach to combine historical land use change with local coproduced urban future scenarios. The cellular automata model uses historical and ancillary data like existing road systems and natural features to develop a set of probabilistic land use change rules, which are then modified according to stakeholder priorities. The resulting land use scenarios are evaluated against historical flood hazards, showcasing how they perform against stakeholder expectations. Our work shows that coproduced scenarios, when grounded with historical and emerging data, can provide paths that increase resilience to weather hazards as well as enhancing ecosystem services provided to citizens.

scholarly journals On accuracy of upper quantiles estimation

2010 ◽  
Vol 7 (4) ◽  
pp. 4761-4784
Author(s):  
I. Markiewicz ◽  
W. G. Strupczewski ◽  
K. Kochanek
Keyword(s):  
Distribution Function ◽  
Population Distribution ◽  
Model Misspecification ◽  
Estimation Method ◽  
Flood Frequency ◽  
Likelihood Method ◽  
Flood Frequency Analysis ◽  
Estimation Methods ◽  
Hypothetical Model ◽  
Log Normal

Abstract. Flood frequency analysis (FFA) entails estimation of the upper tail of a probability density function (PDF) of annual peak flows obtained from either the annual maximum series or partial duration series. In hydrological practice the properties of various estimation methods of upper quantiles are identified with the case of known population distribution function. In reality the assumed hypothetical model differs from the true one and one can not assess the magnitude of error caused by model misspecification in respect to any estimated statistics. The opinion about the accuracy of the methods of upper quantiles estimation formed from the case of known population distribution function is upheld. The above-mentioned issue is the subject of the paper. The accuracy of large quantile assessments obtained from the four estimation methods are compared for two-parameter log-normal and log-Gumbel distributions and their three-parameter counterparts, i.e., three-parameter log-normal and GEV distributions. The cases of true and false hypothetical model are considered. The accuracy of flood quantile estimates depend on the sample size, on the distribution type, both true and hypothetical, and strongly depend on the estimation method. In particular, the maximum likelihood method looses its advantageous properties in case of model misspecification.

scholarly journals Analysis of land use/land cover change in Adei watershed, Central Highlands of Ethiopia

2019 ◽  
Vol 41 (1) ◽  
pp. 146-153 ◽  
Author(s):  
Megersa Olumana Dinka ◽  
Degefa Dhuga Chaka
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Landsat Imagery ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Post Processing ◽  
Ancillary Data ◽  
The Stability ◽  
Land Cover Maps

Abstract Land use/land cover changes (LULCC) at Adei watershed (Ethiopia) over a period of 23 years (1986–2009) has been analysed from LANDSAT imagery and ancillary data. The patterns (magnitude and direction) of LULCC were quantified and the final land use/land cover maps were produced after a supervised classification with appropriate post-processing. Image analysis results revealed that the study area has undergone substantial LULCC, primarily a shift from natural cover into managed agro-systems, which is apparently attributed to the increasing both human and livestock pressure. Over the 23 years, the aerial coverage of forest and grass lands declined by 8.5% and 4.3%, respectively. On the other hand, agricultural and shrub lands expanded by 9.1% and 3.7%, respectively. This shows that most of the previously covered by forest and grass lands are mostly shifted to the rapidly expanding farm land use classes. The findings of this study suggested that the rate of LULCC over the study period, particularly deforestation due to the expansion of farmland need to be given due attention to maintain the stability and sustainability of the ecosystem.

scholarly journals Ecological and evolutionary dynamics of interconnectedness and modularity

2018 ◽  
Vol 115 (4) ◽  
pp. 750-755 ◽  
Author(s):  
Jan M. Nordbotten ◽  
Simon A. Levin ◽  
Eörs Szathmáry ◽  
Nils C. Stenseth
Keyword(s):  
Population Dynamics ◽  
Natural Selection ◽  
Evolution Equation ◽  
Evolutionary Dynamics ◽  
Population Distribution ◽  
Theoretical Framework ◽  
Ecological System ◽  
Macro Level ◽  
Population Distributions ◽  
Trait Space

In this contribution, we develop a theoretical framework for linking microprocesses (i.e., population dynamics and evolution through natural selection) with macrophenomena (such as interconnectedness and modularity within an ecological system). This is achieved by developing a measure of interconnectedness for population distributions defined on a trait space (generalizing the notion of modularity on graphs), in combination with an evolution equation for the population distribution. With this contribution, we provide a platform for understanding under what environmental, ecological, and evolutionary conditions ecosystems evolve toward being more or less modular. A major contribution of this work is that we are able to decompose the overall driver of changes at the macro level (such as interconnectedness) into three components: (i) ecologically driven change, (ii) evolutionarily driven change, and (iii) environmentally driven change.

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