scholarly journals Methods of Population Spatialization Based on the Classification Information of Buildings from China’s First National Geoinformation Survey in Urban Area: A Case Study of Wuchang District, Wuhan City, China

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2558 ◽  
Author(s):  
Linze Li ◽  
Jiansong Li ◽  
Zilong Jiang ◽  
Lingli Zhao ◽  
Pengcheng Zhao
Keyword(s):  
Population Density ◽  
Accuracy Assessment ◽  
Spatial Relationship ◽  
Water Area ◽  
Level Population ◽  
Least Squares Regression ◽  
Street Level ◽  
Residential House ◽  
Multi Level ◽  
Classification Information

Most of the currently mature methods that are used globally for population spatialization are researched on a single level, and are dependent on the spatial relationship between population and land covers (city, road, water area, etc.), resulting in difficulties in data acquisition and an inability to identify precise features on the different levels. This paper proposes a multi-level population spatialization method on the different administrative levels with the support of China’s first national geoinformation survey, and then considers several approaches to verify the results of the multi-level method. This paper aims to establish a multi-level population spatialization method that is suitable for the administrative division of districts and streets. It is assumed that the same residential house has the same population density on the district level. Based on this assumption, the least squares regression model is used to obtain the optimized prediction model and accurate population space prediction results by dynamically segmenting and aggregating house categories.In addition, it is assumed that the distribution of the population is relatively regular in communities that are spatially close to each other, and that the population densities on the street level are similar, so the average population density is assessed by optimizing the community and surrounding residential houses on the street level. Finally, the scientificalness and rationality of the proposed method is proved by spatial autocorrelation analysis, overlay analysis, cross-validation analysis and accuracy assessment methods.

scholarly journals Geo-Spatial Analysis of Population Density and Annual Income to Identify Large-Scale Socio-Demographic Disparities

2021 ◽  
Vol 10 (7) ◽  
pp. 432
Author(s):  
Nicolai Moos ◽  
Carsten Juergens ◽  
Andreas P. Redecker
Keyword(s):  
Population Density ◽  
Large Scale ◽  
Methodological Approach ◽  
Spatial Relationship ◽  
Postal Code ◽  
Annual Income ◽  
Choropleth Maps ◽  
Clustering Approach ◽  
Relationship Of ◽  
Insight Into

This paper describes a methodological approach that is able to analyse socio-demographic and -economic data in large-scale spatial detail. Based on the two variables, population density and annual income, one investigates the spatial relationship of these variables to identify locations of imbalance or disparities assisted by bivariate choropleth maps. The aim is to gain a deeper insight into spatial components of socioeconomic nexuses, such as the relationships between the two variables, especially for high-resolution spatial units. The used methodology is able to assist political decision-making, target group advertising in the field of geo-marketing and for the site searches of new shop locations, as well as further socioeconomic research and urban planning. The developed methodology was tested in a national case study in Germany and is easily transferrable to other countries with comparable datasets. The analysis was carried out utilising data about population density and average annual income linked to spatially referenced polygons of postal codes. These were disaggregated initially via a readapted three-class dasymetric mapping approach and allocated to large-scale city block polygons. Univariate and bivariate choropleth maps generated from the resulting datasets were then used to identify and compare spatial economic disparities for a study area in North Rhine-Westphalia (NRW), Germany. Subsequently, based on these variables, a multivariate clustering approach was conducted for a demonstration area in Dortmund. In the result, it was obvious that the spatially disaggregated data allow more detailed insight into spatial patterns of socioeconomic attributes than the coarser data related to postal code polygons.

scholarly journals SATELLITE-DERIVED BATHYMETRY: ACCURACY ASSESSMENT ON DEPTHS DERIVATION ALGORITHM FOR SHALLOW WATER AREA

2017 ◽  
Vol XLII-4/W5 ◽  
pp. 159-164 ◽  
Author(s):  
N. M. Said ◽  
M. R. Mahmud ◽  
R. C. Hasan
Keyword(s):  
Shallow Water ◽  
Accuracy Assessment ◽  
Water Area ◽  
Bathymetric Data ◽  
Sensing Technology ◽  
Shallow Water Area ◽  
Multispectral Satellite Imagery ◽  
Marginal Improvement ◽  
Acquisition Technique ◽  
Hydrographic Surveying

Over the years, the acquisition technique of bathymetric data has evolved from a shipborne platform to airborne and presently, utilising space-borne acquisition. The extensive development of remote sensing technology has brought in the new revolution to the hydrographic surveying. Satellite-Derived Bathymetry (SDB), a space-borne acquisition technique which derives bathymetric data from high-resolution multispectral satellite imagery for various purposes recently considered as a new promising technology in the hydrographic surveying industry. Inspiring by this latest developments, a comprehensive study was initiated by National Hydrographic Centre (NHC) and Universiti Teknologi Malaysia (UTM) to analyse SDB as a means for shallow water area acquisition. By adopting additional adjustment in calibration stage, a marginal improvement discovered on the outcomes from both Stumpf and Lyzenga algorithms where the RMSE values for the derived (predicted) depths were 1.432 meters and 1.728 meters respectively. This paper would deliberate in detail the findings from the study especially on the accuracy level and practicality of SDB over the tropical environmental setting in Malaysia.

scholarly journals Surface Heterogeneity-Involved Estimation of Sample Size for Accuracy Assessment of Land Cover Product from Satellite Imagery

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4430
Author(s):  
Ren ◽  
Cai ◽  
Du
Keyword(s):  
Land Cover ◽  
Statistical Model ◽  
Sample Size ◽  
Scale Effect ◽  
Accuracy Assessment ◽  
Surface Heterogeneity ◽  
Sample Size Estimation ◽  
Size Estimation ◽  
Landscape Indicators ◽  
Multi Level

Sample size estimation is a key issue for validating land cover products derived from satellite images. Based on the fact that present sample size estimation methods account for the characteristics of the Earth’s subsurface, this study developed a model for estimating sample size by considering the scale effect and surface heterogeneity. First, we introduced a watershed with different areas to indicate the scale effect on the sample size. Then, by employing an all-subsets regression feature selection method, three landscape indicators describing the aggregation and diversity of the land cover patches were selected (from 14 indicators) as the main factors for indicating the surface heterogeneity. Finally, we developed a multi-level linear model for sample size estimation using explanatory variables, including the estimated sample size (n) calculated from the traditional statistical model, size of the test region, and three landscape indicators. As reference data for developing this model, we employed a case study in the Jiangxi Province using a 30 meter spatial resolution global land cover product (Globeland30) from 2010 as a classified map, and national 30 meter land use/cover change (LUCC) data from 2010 in China. The results showed that the adjusted square coefficient of R2 is 0.79, indicating that the joint explanatory ability of all predictive variables in the model to the sample size is 79%. This means that the predictability of this model is at a good level. By comparing the sample size NsNS obtained by the developed multi-level linear model and n as calculated from the statistics model, we find that NsNsNS is much smaller than n, which mainly contributes to the concerns regarding surface heterogeneity in this study. The validity of the established model is tested and is proven as effective in the Anhui Province. This indicates that the estimated sample size from considering the scale effect and spatial heterogeneity in this study achieved the same accuracy as that calculated from a probability statistical model, while simultaneously saving more time, labour, and money in the accuracy assessment of a land cover dataset.

A Method of Extracting Inter-Object Spatial Relationship in Remote Sensing Image Objects

2014 ◽  
Vol 519-520 ◽  
pp. 537-540
Author(s):  
Xiao Li Liu ◽  
Wen Tao Yang ◽  
Guo Bin Zhu ◽  
Jing Gang LI ◽  
Xue Li
Keyword(s):  
Remote Sensing ◽  
Spatial Relationship ◽  
Relation Extraction ◽  
Spatial Relation ◽  
Remote Sensing Image ◽  
Order Property ◽  
Inclusion Relation ◽  
Image Objects ◽  
Multi Level ◽  
Topological Models

Spatial relation extraction is very important for remote sensing application, but most existing topological models hardly describe the order property of transformations among the topological relations. This paper proposed a new method to extract inclusion relation of image based on topology map in the process of multi-level image classification.

scholarly journals Space Transformation in Residential House Small Entrepreneurs Banana Sale

2021 ◽  
Vol 1 (4) ◽  
pp. 41-50
Author(s):  
Rinaldi Mirsa ◽  
Muhammad Muhammad ◽  
Fidyati Fidyati ◽  
Eri Saputra ◽  
Muhammad Rumiza
Keyword(s):  
Spatial Relationship ◽  
Living Environment ◽  
Spatial Transformation ◽  
Business Goals ◽  
Storage Room ◽  
The People ◽  
Space Transformation ◽  
Residential House ◽  
Space Requirements ◽  
Residential Space

Space transformation occurs in line with the needs and availability of resources owned by space users. The arrangement and utilization of space is carried out to optimize the function of the space owned and the limited space owned by the space user requires an adjustment in the use of the space owned to achieve business goals as well as the comfort of the living environment. Pante Bidari is a banana sale    producing area in Aceh, which is located in East Aceh Regency, where the majority of the people work as small entrepreneurs and   workers in the Banana Sale Industry. The process carried out when producing Pisang Sale uses a special room consisting of a storage room, peeling room, sale room and packaging room. This study aims to determine how the transformation of space in a small banana sale house. The method used in this research is a qualitative method. This study found that the spatial transformation that occurred in the small businessman's house of Pisang Sale in Pante Bidari District, East Aceh Regency is one way to optimize the utilization and         utilization of space, so that the Pisang Sale production room consists of storage room, stripping room, sale room and packaging room. using residential space as an aspect of activity in residential homes, so that ongoing activities are not disturbed by other activities,       residential space in terms of space dimensions there are changes that include addition, reduction and movement of space aimed at adjusting space requirements. Judging from the spatial relationship, there are several spaces that are far from each other and close to each other, so that access to activities carried out can optimize the function of the space.

scholarly journals Does high COVID-19 spread impact neighbouring countries? Evidence from Ireland

2021 ◽  
Vol 4 ◽  
pp. 56
Author(s):  
Rakesh Ahmed ◽  
Peter May
Keyword(s):  
Population Density ◽  
Research Question ◽  
Age Distribution ◽  
Disease Spread ◽  
Sensitivity Analyses ◽  
First Year ◽  
Least Squares Regression ◽  
Infection Rates ◽  
High Infection ◽  
Quasi Experimental

Background: Coronavirus disease 2019 (COVID-19) has necessitated public health responses on an unprecedented scale. Controlling infectious diseases requires understanding of the conditions that increase spread. Prior studies have identified sociodemographic, epidemiological and geographic associations. Ireland offers an unusual opportunity to quantify how high infection rates in one country impacted cases in a neighbouring country. Methods: We analysed official statistics on confirmed COVID-19 cases on the island of Ireland for 52 weeks from March 2020. Our main research question was: Did higher cases in Northern Ireland (NI) impact the number of cases in the Republic of Ireland (ROI)? We used least squares regression to compare confirmed cases in ROI counties that border NI with the rest of the state. We included in our model sociodemographic, epidemiological and geographic factors. We employed the latitude of each county town as an instrumental variable to isolate a quasi-experimental estimate of the cross-border spread. Results: In the quasi-experimental framework, and controlling for population density, age distribution and circulatory disease prevalence, border counties had an extra 21.0 (95%CI: 8.4-33.6) confirmed COVID-19 cases per 1000 people. This equates to an estimated 9,611 additional cases in ROI, or 4% of the national total in the first year of the pandemic. Our results were substantively similar in non-experimental frameworks, with alternative additional predictors, and in sensitivity analyses. Additionally, population density in ROI counties was positively associated with confirmed cases and higher proportions of residents in the professional classes was negatively associated. Conclusion: On the island of Ireland during the first year of the COVID-19 pandemic, high infection rates in NI increased cases in the neighbouring ROI. Maximising co-ordination of pandemic responses among neighbouring countries is essential to minimising disease spread, and its associated disruptions to society and the economy. Socioeconomic disadvantage appeared to confer significant additional risk of spread.

scholarly journals Nexus between population density and COVID-19 pandemic in the south Indian states: A geo-statistical approach

2020 ◽  
Author(s):  
Mohammad Arif ◽  
Soumita Sengupta
Keyword(s):  
Population Density ◽  
Tamil Nadu ◽  
Spatial Relationship ◽  
The Novel ◽  
Global Public Health ◽  
Indian States ◽  
Health Crisis ◽  
South Indian ◽  
Novel Coronavirus

The unprecedented growth of the novel coronavirus (SARS-CoV-2) as a severe acute respiratory syndrome escalated to the 2019 coronavirus disease (COVID-19) pandemic. It has created an unanticipated global public health crisis. The virus is spreading rapidly in India which poses serious threat to 135 crore population. Population density poses some unforeseen challenges to control the COVID-19 contagion. In times of crisis, data is crucial to understand the spatial relationship between density and the infection. The article study the district wise transmissions of the novel coronavirus in five south Indian states until 6th June 2020 and its relationship with the respective population density. The five states are purposefully selected for better healthcare infrastructure vis-à-vis other states in India. We observed that corona virus spread depends on the spatial distribution of population density in three states especially in Tamil Nadu, Karnataka and Telangana. The results indicate that the long-term impacts of the COVID-19 crisis are likely to differ with demographic density. Policy initiatives aimed at reducing the health consequences of the COVID-19 pandemic should understand how vulnerabilities cluster together across districts.

Macro-Level Analysis of Factors Related to Areawide Highway Traffic Congestion

2002 ◽  
Vol 1817 (1) ◽  
pp. 11-16 ◽  
Author(s):  
Elisabeth Hahn ◽  
Arun Chatterjee ◽  
Mary Sue Younger
Keyword(s):  
Population Density ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Strong Positive Correlation ◽  
Least Squares Regression ◽  
Highway Traffic ◽  
Land Area ◽  
Related Factors ◽  
Positive Correlation ◽  
Travel Rate

The relationship between traffic congestion, travel demand, and supply of roadways is investigated by use of the travel rate index, a congestion measure developed by researchers at the Texas Transportation Institute. Data for the top 138 urbanized areas (by population) were assembled for developing a least-squares regression model. The travel rate index was selected as the response (dependent) variable. A variety of explanatory variables were used to address highway and transit supply and travel demand-related factors. The partial regression coefficients measured the effect of each explanatory (independent) variable on congestion (as measured by travel rate index), holding all other independent variables constant. The results of the multiple regression analysis indicated a negative correlation between freeway lane miles and combined travel rate index. Additionally, a strong positive correlation was observed between combined travel rate index and population density and net land area. A positive correlation was observed between combined travel rate index and bus transit service revenue miles. Principal arterial lane miles and rail transit revenue miles variables were not observed to be significant for explaining traffic congestion and were removed entirely during the stepwise regression. The results indicated that the best predictors among the tested variables were freeway lane miles, population density, net land area, and bus revenue miles. When used together, these predictors accounted for approximately 61% of the total variation in the dependent variable, combined travel rate index.

Sign in / Sign up

Export Citation Format

Share Document