Assessment of Ecological Environment Quality in Kolkata Urban Agglomeration, India

Abstract The global ecosystem has been significantly disrupted on various spatiotemporal scales over the last three decades due to human activities. Geospatial technology can quickly, effectively, and quantitatively to evaluate the spatiotemporal change of eco-environmental quality (EEQ). The present study is focused on novel approach of Remote Sensing based Ecological Index (RSEI), using Landsat Imagery data to assess environmental conditions and changes pattern. Four ecological indicators were prepared in the year 1990, 2000, 2010 and 2020 of Kolkata urban agglomeration (KUA) to evaluate the ecological environmental condition. The principal component analysis (PCA) and spatial autocorrelation analysis can relate all indicators with each other’s and RSEI. Out study indicated, greenness and wetness have a positive effect on EEQ of the province, but both dryness and heat have a negative effect. However, it should be noted that greenness has a greater impact on the eco-environment than the other three indicators. Based on the RSEI values, we have categorized the environmental standards of the study area into four groups - very good (0.81 - 1.00), good (0.61 - 0.80), acceptable (0.41 - 0.60), poor (0.21 - 0.40), and very poor (0.00 - 0.20), where high values indicate that environmental quality is stable and healthy for living organisms and low values indicate relatively unstable and threatening conditions of the environment. The status of RSEI showed that 9.02%, 12.29%, 12.79% and 37.23% of an area was under poor to very poor condition in the year of 1990, 2000, 2010 and 2020 respectively. Good to very good condition of RSEI values was increased from 19.12% to 34.074% during 1990 to 2010, but declined of RSEI value 9.47% during 2010 to 2020 due to urban expansion. Here, Moran's I values fund that 0.265, 0.543, 0.396 and 0.367 in the year 1990, 2000, 2010 and 2020 respectively. The result of Moran’s I values indicate that clustering nature. The present study can helpful for the decision making of ecological management guided by planners and policy makers.

Download Full-text

Spatial Modeling of Environmental Quality Change Based on Geographic Information System

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/887/1/012016 ◽

2021 ◽

Vol 887 (1) ◽

pp. 012016

Author(s):

I. G. Wiratmaja ◽

A. W. Sejati

Keyword(s):

Information System ◽

Environmental Quality ◽

Principal Component ◽

Geographical Information ◽

Vegetation Density ◽

Quality Change ◽

Ecological Index ◽

Raster Calculator ◽

Coastal Land

Abstract A coastal area is prone to decrease of environmental quality due to coastal land abrasion and inundation/tidal flood. Several studies have shown that the coastline of Sayung District, Demak Regency, is moving toward the mainland for 6.8 mm/year and the surrounding area is experiencing land subsidence for 5-7 cm/year. These phenomena have consequences to the environmental quality in the area. In this case, this research aims to develop a spatial model using a Geographical Information System (GIS) in describing and predicting changes of environmental quality in Sayung District, Demak Regency. Four variables in a Risk-Screening Ecological Index (RSEI) approach, namely (1) vegetation density, (2) soil moisture, (3) soil quality, and (4) built space and surface temperature were used as indicators of the environmental quality. A raster calculator and Spatial Principal Component Analysis (SPCA) were then used to calculate total value of the environmental quality. This research results that the environmental quality of the study area is decreasing which indicated by the RSEI value of 0.614 (1999), 0.4749 (2009), and 0.3933 (2019). The environmental quality in the study area is also worsened by waterbody expansion.

Download Full-text

Evaluation of the Eco-Environmental Quality and its Index-Induced Influencing Factors in the Yangtze River Delta Urban Agglomeration

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1073-1076.1166 ◽

2014 ◽

Vol 1073-1076 ◽

pp. 1166-1170

Author(s):

Fang Yang ◽

Min Liu ◽

Wen Xiao Jia ◽

Lin Lin ◽

Wei Ning Xiang

Keyword(s):

Environmental Quality ◽

Yangtze River ◽

Yangtze River Delta ◽

River Delta ◽

Urban Agglomeration ◽

Clear Understanding ◽

The Yangtze River ◽

Related Factors ◽

Ecological Index ◽

The Yangtze River Delta

This paper aims to provide a clear understanding of the characteristics and spatial-temporal pattern of the eco-environmental quality (EEQ) in the Yangtze River Delta Urban Agglomeration. Based on the 500m × 500m pixel scale, the evaluation was carried out using the Ecological Index (EI). Results show that the EEQs are both classified into “Moderately high” level in 2005 and 2010. It exhibits obvious spatial variability with Shanghai-Nanjing belt as a demarcation belt, indicating that EI values are higher in the south and lower in the north while EEQ in the belt is the worst. Further research demonstrates it is policy-induced emission reduction that gave rise to the overall eco-environmental improvement from 2005 to 2010, followed by an increase in the volume of water resources and green space. The EEQs of Shanghai, Jiaxing, Nantong, Lianyungang, Yancheng and Xuzhou are more constrained and influenced by EI-related factors.

Download Full-text

The Spatial Distribution Characteristics of Carbon Emissions at County Level in the Harbin−Changchun Urban Agglomeration

Atmosphere ◽

10.3390/atmos12101268 ◽

2021 ◽

Vol 12 (10) ◽

pp. 1268

Author(s):

Yixia Wang

Keyword(s):

Spatial Distribution ◽

Carbon Emissions ◽

Carbon Emission ◽

Urban Agglomeration ◽

Moran’S I ◽

County Level ◽

Distribution Characteristics ◽

Moran's I ◽

Spatial Distribution Characteristics ◽

Global And Local

China has clearly put forward the strategic goals of reaching the “Carbon Emission Peak” by 2030, and achieving “Carbon Neutrality” by 2060. To achieve these goals, it is necessary to precisely understand the spatial distribution characteristics of historical carbon emissions in different regions. This paper has selected a representative national-level urban agglomeration in China, the Harbin−Changchun urban agglomeration, to study the temporal and spatial distribution characteristics of carbon emissions in its counties. This paper has constructed global and local Moran’s I indexes for the 103 counties in this urban agglomeration by using the carbon emission values reflected by night light data from 1997 to 2017 to perform global and local autocorrelation analysis on a spatial level. The results show that: (1) the main characteristic of carbon emission clustering in the Harbin−Changchun urban agglomeration is similar clustering; (2) the changes in carbon emissions of the Harbin−Changchun urban agglomeration have a strong correlation with relevant policies. For example, due to the impact of the “Twelfth Five-Year Plan” policies, in 2013, the global county-level Moran’s I index of the carbon emissions in the Harbin−Changchun urban agglomeration decreased by 0.0598; (3) the areas where high carbon emission values cluster together (“High−High Cluster”) and low carbon emission values cluster together (“Low−Low Cluster”) in the Harbin−Changchun urban agglomeration are highly concentrated, and the clusters are closely related to the development level of different regions.

Download Full-text

A Protocol for Pollution Index, Source Identification, and Spatial Analysis of Heavy Metals in Top Soil

Journal of Environmental Health and Sustainable Development ◽

10.18502/jehsd.v5i4.4965 ◽

2020 ◽

Author(s):

Amir Mohammadi ◽

Sepideh Nemati Mansour ◽

Maryam Faraji ◽

Ali Abdolahnejad ◽

Ali Toolabi ◽

...

Keyword(s):

Spatial Analysis ◽

Toxic Metals ◽

Source Identification ◽

Contamination Factor ◽

Pollution Index ◽

Principal Component ◽

Moran’S I ◽

Moran's I ◽

Top Soil ◽

Z Scores

Introduction: This study aimed to assess a good protocol for the contamination indexes, concentration, spatial analysis, and source identification of toxic metals in top soils. Materials and Methods: In the first step, samples were taken from top soil (30 cm) and the metals were extracted and detected with ICP-AES. In the second step, Enrichment Factor, Geoaccumulation Index, and Contamination Factor of metals were calculated to determine soil contamination degree. Furthermore, the principal component analysis and correlation between metals were conducted for source identification. Results: Spatial analysis, as an important section of the present protocol, was performed using Arc GIS, kriging, and Moran's I models. As results of Moran's I model showed, distribution pattern for Fe, As, Cd, Cu, Ni, Pb, and Zn were random (z-scores ranged from -1.17 to 1.09), indicatingthat these elements could be emitted from different potential sources. In Moran's model, spatial autocorrelation of each pollutant could be measured based on its value and location. Conclusion: The finding of this protocol can be used for extraction of contamination indexes, concentration, spatial analysis, and source identification of toxic metals in top soils.

Download Full-text

Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I

Jurnal Buana Informatika ◽

10.24002/jbi.v8i4.1446 ◽

2017 ◽

Vol 8 (4) ◽

Cited By ~ 1

Author(s):

Matheus Supriyanto Rumetna ◽

Eko Sediyono ◽

Kristoko Dwi Hartomo

Keyword(s):

Land Use ◽

Land Use Change ◽

Spatial Autocorrelation ◽

Land Use Changes ◽

Geographical Information ◽

Moran’S I ◽

Complete Spatial Randomness ◽

Moran's I ◽

Global Moran’S I ◽

Spatial Randomness

Abstract. Bantul Regency is a part of Yogyakarta Special Province Province which experienced land use changes. This research aims to assess the changes of shape and level of land use, to analyze the pattern of land use changes, and to find the appropriateness of RTRW land use in Bantul District in 2011-2015. Analytical methods are employed including Geoprocessing techniques and analysis of patterns of distribution of land use changes with Spatial Autocorrelation (Global Moran's I). The results of this study of land use in 2011, there are thirty one classifications, while in 2015 there are thirty four classifications. The pattern of distribution of land use change shows that land use change in 2011-2015 has a Complete Spatial Randomness pattern. Land use suitability with the direction of area function at RTRW is 24030,406 Ha (46,995406%) and incompatibility of 27103,115 Ha or equal to 53,004593% of the total area of Bantul Regency.Keywords: Geographical Information System, Land Use, Geoprocessing, Global Moran's I, Bantul Regency. Abstrak. Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I. Kabupaten Bantul merupakan bagian dari Provinsi Daerah Istimewa Yogyakarta yang mengalami perubahan tata guna lahan. Penelitian ini bertujuan untuk mengkaji perubahan bentuk dan luas penggunaan lahan, menganalisis pola sebaran perubahan tata guna lahan, serta kesesuaian tata guna lahan terhadap RTRW yang terjadi di Kabupaten Bantul pada tahun 2011-2015. Metode analisis yang digunakan antara lain teknik Geoprocessing serta analisis pola sebaran perubahan tata guna lahan dengan Spatial Autocorrelation (Global Moran’s I). Hasil dari penelitian ini adalah penggunaan tanah pada tahun 2011, terdapat tiga puluh satu klasifikasi, sedangkan pada tahun 2015 terdapat tiga puluh empat klasifikasi. Pola sebaran perubahan tata guna lahan menunjukkan bahwa perubahan tata guna lahan tahun 2011-2015 memiliki pola Complete Spatial Randomness. Kesesuaian tata guna lahan dengan arahan fungsi kawasan pada RTRW adalah seluas 24030,406 Ha atau mencapai 46,995406 % dan ketidaksesuaian seluas 27103,115 Ha atau sebesar 53,004593 % dari total luas wilayah Kabupaten Bantul. Kata Kunci: Sistem Informasi Georafis, tata guna lahan, Geoprocessing, Global Moran’s I, Kabupaten Bantul.

Download Full-text

Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

Scientific Research Journal ◽

10.24191/srj.v9i2.9405 ◽

2012 ◽

Vol 9 (2) ◽

pp. 1

Author(s):

Asra Hosseini

Keyword(s):

Spatial Autocorrelation ◽

Urban Areas ◽

Smart Cities ◽

Moran’S I ◽

Moran's I ◽

Urban Planner ◽

Universal Feature ◽

Autocorrelation Method ◽

Selection Of

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature of urban areas. This study explored issue of measuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% of neighbourhoods' area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined.

Download Full-text

Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

Scientific Research Journal ◽

10.24191/srj.v9i2.5389 ◽

2012 ◽

Vol 9 (2) ◽

pp. 1

Author(s):

Asra Hosseini

Keyword(s):

Spatial Autocorrelation ◽

Smart Cities ◽

Moran’S I ◽

Moran's I ◽

Urban Planner ◽

Universal Feature ◽

Autocorrelation Method ◽

Selection Of ◽

Monotonic Relation

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature ofurban areas. This study explored issue ofmeasuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% ofneighbourhoods, area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined.

Download Full-text

Geographical variations and associated factors of defaulting from immunization among children aged 12 to 23 months in Ethiopia: using spatial and multilevel analysis of 2016 Ethiopian Demographic and Health Survey

Environmental Health and Preventive Medicine ◽

10.1186/s12199-021-00984-8 ◽

2021 ◽

Vol 26 (1) ◽

Author(s):

Mukemil Awol ◽

Zewdie Aderaw Alemu ◽

Nurilign Abebe Moges ◽

Kemal Jemal

Keyword(s):

Multilevel Analysis ◽

Health Survey ◽

Associated Factors ◽

Demographic And Health Survey ◽

Community Level ◽

Scan Statistics ◽

Moran’S I ◽

Moran's I ◽

Depth Analysis ◽

Global Moran’S I

Abstract Background In Ethiopia, despite the considerable improvement in immunization coverage, the burden of defaulting from immunization among children is still high with marked variation among regions. However, the geographical variation and contextual factors of defaulting from immunization were poorly understood. Hence, this study aimed to identify the spatial pattern and associated factors of defaulting from immunization. Methods An in-depth analysis of the 2016 Ethiopian Demographic and Health Survey (EDHS 2016) data was used. A total of 1638 children nested in 552 enumeration areas (EAs) were included in the analysis. Global Moran’s I statistic and Bernoulli purely spatial scan statistics were employed to identify geographical patterns and detect spatial clusters of defaulting immunization, respectively. Multilevel logistic regression models were fitted to identify factors associated with defaulting immunization. A p value < 0.05 was used to identify significantly associated factors with defaulting of child immunization. Results A spatial heterogeneity of defaulting from immunization was observed (Global Moran’s I = 0.386379, p value < 0.001), and four significant SaTScan clusters of areas with high defaulting from immunization were detected. The most likely primary SaTScan cluster was seen in the Somali region, and secondary clusters were detected in (Afar, South Nation Nationality of people (SNNP), Oromiya, Amhara, and Gambella) regions. In the final model of the multilevel analysis, individual and community level factors accounted for 56.4% of the variance in the odds of defaulting immunization. Children from mothers who had no formal education (AOR = 4.23; 95% CI: 117, 15.78), and children living in Afar, Oromiya, Somali, SNNP, Gambella, and Harari regions had higher odds of having defaulted immunization from community level. Conclusions A clustered pattern of areas with high default of immunization was observed in Ethiopia. Both the individual and community-level characteristics were statistically significant factors of defaulting immunization. Therefore, the Federal Ethiopian Ministry of Health should prioritize the areas with defaulting of immunization and consider the identified factors for immunization interventions.

Download Full-text

Spatial distribution and determinant factors of anemia among adults aged 15–59 in Ethiopia; using mixed-effects ordinal logistic regression model

BMC Nutrition ◽

10.1186/s40795-021-00424-4 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Biruk Shalmeno Tusa ◽

Sewnet Adem Kebede ◽

Adisu Birhanu Weldesenbet

Keyword(s):

Public Health ◽

Spatial Distribution ◽

Scan Statistics ◽

Moran’S I ◽

Demographic And Health Surveys ◽

Targeted Prevention ◽

Hotspot Analysis ◽

Mixed Effect ◽

Moran's I ◽

Determinant Factors

Abstract Background Anemia is a global public health problem, particularly in developing countries. Assessing the geographic distributions and determinant factors is a key and crucial step in designing targeted prevention and intervention programmes to address anemia. Thus, the current study is aimed to assess the spatial distribution and determinant factors of anemia in Ethiopia among adults aged 15–59. Methods A secondary data analysis was done based on 2016 Ethiopian Demographic and Health Surveys (EDHS). Total weighted samples of 29,140 adults were included. Data processing and analysis were performed using STATA 14; ArcGIS 10.1 and SaTScan 9.6 software. Spatial autocorrelation was checked using Global Moran’s index (Moran’s I). Hotspot analysis was made using Gettis-OrdGi*statistics. Additionally, spatial scan statistics were applied to identify significant primary and secondary cluster of anemia. Mixed effect ordinal logistics were fitted to determine factors associated with the level of anemia. Result The spatial distribution of anemia in Ethiopia among adults age 15–59 was found to be clustered (Global Moran’s I = 0.81, p value < 0.0001). In the multivariable mixed-effectordinal regression analysis; Females [AOR = 1.53; 95% CI: 1.42, 1.66], Never married [AOR = 0.86; 95% CI: 0.77, 0.96], highly educated [AOR = 0.71; 95% CI: 0.60, 0.84], rural residents [AOR = 1.53; 95% CI: 1.23, 1.81], rich wealth status [AOR = 0.77; 95% CI: 0.69, 0.86] and underweight [AOR = 1.15; 1.06, 1.24] were significant predictors of anemia among adults. Conclusions A significant clustering of anemia among adults aged 15–59 were found in Ethiopia and the significant hotspot areas with high cluster anemia were identified in Somalia, Afar, Gambella, Dire Dewa and Harari regions. Besides, sex, marital status, educational level, place of residence, region, wealth index and BMI were significant predictors of anemia. Therefore, effective public health intervention and nutritional education should be designed for the identified hotspot areas and risk groups in order to decrease the incidence of anemia.

Download Full-text