Built-up Expansion Research Articles

Warming reality of Kozhikode Urban Area: Uncovering the heat of built-up expansion and vegetation loss

Urban expansion often comes at the expense of natural landscapes, leading to increased temperatures and environmental degradation. This study examines the effects of urbanization on land cover changes and their implications for land surface temperature (LST) by analyzing normalized difference satellite indices in the Kozhikode Urban Area (KUA), a city where substantial urbanization has significantly impacted the natural environment. By investigating changes in vegetation and impervious surfaces from 1993 to 2022, the research highlights a troubling trend: while vegetation cover has sharply declined, impermeable surfaces like roads and buildings have surged, contributing to a dramatic rise in average temperatures. The study reveals that built-up areas significantly elevate LST, whereas vegetation and water bodies offer cooling effects. The results underscore the urgent need for integrating green infrastructure into urban planning to combat rising temperatures and enhance environmental sustainability. This research emphasizes the importance of considering smaller, vegetation-rich cities like Kozhikode in broader urban heat research, as they present unique challenges and opportunities for effective climate adaptation strategies. By highlighting these issues, this study aims to inform policymakers and urban planners about the critical role of urban greening in mitigating the effects of climate change and fostering sustainable urban environments.

Assessing the Impacts of Migration on Land Degradation in the Savannah Region of Nigeria

Migration-induced land degradation is a challenging environmental issue in Sub-Saharan Africa. The need for expansion due to urban development has raised the question of effective sustainable measures. Understanding migration and land degradation links is paramount for sustainable urban development and resource use. This is particularly true in Nigeria, where elevated migration levels frequently result in accelerated land degradation due to urban expansion. Given the need to understand the impact of migration on land degradation in the Savannah Region of Nigeria (SRN), this study introduces a novel approach by integrating remote sensing data (NDVI, NDBI) with local community perceptions (mixed-methods approach) to assess the impact of migration on land degradation in four migration destination communities located in two local government areas (LGAs) (Sabon Gari East and Sabon Gari West of Fagge LGA; Zuba and Tungamaje of Gwagwalada LGA). We conducted focus group discussions and a semi-structured survey with 360 household heads to obtain a comprehensive view of perceptions. Our findings revealed that 41.1% and 29.5% of the respondents agreed and strongly agreed that migration significantly contributes to land degradation. We analysed the spatiotemporal patterns of the Normalised Difference Vegetation Index (NDVI) and the Normalised Difference Built-Up Index (NDBI) acquired from Landsat 8 datasets for 2014 to 2023. While increasing NDBI values were observed in all communities, a slight decrease in NDVI was noted in Sabon Gari East and Tungamaje. Our analyses highlighted activities leading to land degradation such as land pressure due to built-up expansion at Sabon Gari East, Sabon Gari West, and Tungamaje, and deforestation at Zuba. Based on the varying challenges of migration-induced land degradation, we recommend adequate community participation in suggesting targeted interventions and policies to foster various adaptive capacities and sustainable environments within SRN communities and Sub-Saharan Africa.

Peri-urban dynamics: assessing expansion patterns and influencing factors

BackgroundPeri-urbanization, the expansion of large metropolitan centers into adjacent peri-urban regions, is a growing concern due to land scarcity and escalating housing costs. These zones, a blend of rural and urban features, blur the line between urban and rural areas, creating new landscapes. This study examines historical, present, and potential growth trends in the peri-urban area surrounding Durgapur Municipal Corporation (DMC). Analytical techniques and spatial metrics are used to track development intensity changes over time, including built-up density, Shannon’s entropy, Landscape expansion index, Average Weighted Mean Expansion Index, Annual Built-Up Expansion Rate, Built-Up Expansion Intensity Index, and Built-Up Expansion Difference Index. Landscape indices like Patch Density, Edge Density, Landscape Shape Index, Largest Patch Index, Ratio of Open Space, and Area Weighted Mean Patch Fractal are used to understand fragmentation, connectivity, and spatial relationships. The Logistic Regression Model (LRM) is used to identify influencing factors and CA-Markov modeling for future built-up areas.ResultsBetween 1991 and 2001, built-up area in the region increased significantly, primarily due to urban development near industrial zones, roadways, and mining areas. The growth was primarily concentrated in the western sector and near National Highway-2 (NH-2). Urban sprawl was a continuous trend, with the highest built-up density in the South-South-East (SSE) direction from 1991 to 2011. Additionally, a key determinant of built-up development was the distance to the city core. By 2031, the built-up area is expected to concentrate in the western and southeast regions, reaching 177.90 km2.ConclusionsThis expansion is attributed to urban development near industrial zones, roadways, mining areas, and other infrastructure. The study identifies distance to the city center as a significant influencing factor for built-up development. The results emphasize the need for inclusive urban planning methods prioritizing sustainable development principles and prudent resource management for future growth and efficient management in the DMC’s peri-urban area.

Unravelling the Role of Socio-Physical Drivers for Potential Built-up Site Selection in the Kumaun Himalayas Using GIS-Based Fuzzy-AHP and Machine Learning

Rapid and uncontrolled urban growth in the Kumaun Himalayas in absence of proper land use policy has pushed built-up areas towards the tectonically and ecologically sensitive regions, reducing the availability of suitable built-up land while simultaneously increasing the vulnerability of both communities and environment. The identification of areas for sustainable built-up growth is of paramount importance to address the challenges arising from unregulated urban expansion. In this study GIS-based Fuzzy-AHP technique and machine learning algorithms (SVM and BN) were employed to delineate the potential built-up sites selection in Hawalbagh Block, Uttarakhand (India) using nine socio-physical drivers, including slope, aspect, LU/LC, distance to road, distance to drainage, distance to lineament, distance to landslide, distance to settlement, and lithology. The suitability maps generated by the three methods were validated using AU-ROC analysis, which demonstrated that each approach produces outstanding results with AU-ROC values more than 0.90. The comparison of the approaches shows that SVM (AUROC=0.99) outperforms BN (0.95) and GIS-based Fuzzy-AHP (0.90). The suitability maps were classified into five suitability classes. Assuming that very high and high suitability classes are acceptable for built-up expansion, the study identified potential built-up locations in the study region covering an area of 148.86 km2, 85.23 km2, and 55.25 km2 according to the Fuzzy-AHP technique, SVM model, and BN model, respectively. The suitability zonation in this study can serve as a foundation for the development of land-use policy or the formulation of master plans aimed at achieving a sustainable mountain ecology in the Kumaun Himalayas.

LAND USE LAND COVER (LULC) CHANGE ANALYSIS IN BAGUIO CITY USING GIS-BASED TRANSITION POTENTIAL MODELING

Abstract. Baguio City experienced rapid LULC changes in the past decades due to rising population resulting in increasing demand in residential and commercial areas. The LULC change from 2003 to 2011 was quantified through a transition potential model using Artificial Neural Network and Cellular Automata. Potential driving factors considered in this study were barangay population and population density, land category, elevation, slope, soil type, and distances from the Central Business District, roads, and sinkholes. Multilayer Perceptron with Backpropagation technique was employed in the modeling simulations where various value combinations for the five hyperparameters were tested. Among the combinations of hyperparameter values tested, the combination that achieved the highest simulation accuracy was 1, 0.001, 1000, 10, and 0.01 for Neighborhood, Learning Rate, Iterations, Hidden Layers, and Momentum respectively. The prediction results for the year 2035 show that built-up areas in Baguio City will increase by 760.79 hectares while vegetation and bare soil will decrease by 347.64 hectares and 413.26 hectares, respectively. Built-up areas are expected to form mostly in Alienable and Disposable lands, barangays with high population density, and areas near roads and pre-existing development. On the other hand, minimal built-up expansion is expected in vacant forests, forest reserves, and areas near sinkholes. These findings are in line with the city government's expectations. With an accuracy of 72.80% and a Kappa statistic of 0.61, it can be concluded that the model is capable of predicting future LULC change and may serve as a viable guide for future land use development plans.

Land-Use Land-Cover Dynamics of Peri-Urban Areas of National Capital Region (NCR) of Delhi

This paper attempts to analyse the Land-Use Land-Cover (LULC) changes during the years from 1999 to 2020 in the peri-urban areas of Delhi (Capital of India). These areas have been undergoing tremendous transformation, especially spatial transformation due to their proximity to urban areas and availability of cheaper land. Peri-urban areas of Delhi have observed a significantly higher growth of built-up area in these two decades compared to other LULC changes such as vegetation, waterbodies, wasteland and agriculture. In this concern, a statistical relationship of built-up expansion with transport infrastructure is established to address the land use dynamics.

Analyzing land use and land cover change in the Pra River Basin: A multi-tool approach for informed decision-making

Quantifying land use and land cover change (LULCC) is essential for addressing societal challenges and conserving ecosystems. This study focuses on analyzing the dynamics of five LULC classifications at a 30 m spatial resolution using satellite imagery from Landsat, coupled with various tools (Google Earth Engine, ArcMap, QGIS, and IDRISI SELVA) and indicators such as the Bare Soil Index (BSI). Historic time series data (2007, 2015, and 2023) were utilized to assess land change and predict future scenarios (2030 and 2063) using (CA) Markov models of the land change modeler (LCM). With a kappa coefficient and overall accuracy of 95 %, and 93 % respectively, our analysis reveals that LUC has significantly impacted nearly a third of the Pra River Basin (PRB) land area in two decades (2007–2023) after the launch of Agenda 2063 (The Africa We Want), with projected future impacts doubling under a business-as-usual (BAU) scenario. Geographical variations in LULC change processes are evident, with farmland abandonment, deforestation, and agricultural expansion observed predominantly in the northwestern region (Upper Pra, Anum, and Birim) of the basin. A 25 % increment of cultivated/farmland from 2007 to 2015 and a further 8 % in 2023. The northeast region (Upper and Lower Offin, Twifo Praso) faces deforestation and encroachment along riverbanks, along with built-up expansion due to mining activities. The northern region (Oda and Upper Pra) experiences exponential built-up and bare land increment. Accelerated phases of LULC change are attributed to factors such as mining (illegal and small-scale), agricultural production, migration, and population increment highlighting the influence of economic activities on land use dynamics. The concept gives a hybrid approach of using multi-tools in offering critical awareness of the state of the river system and encouraging the need for informed decision-making towards the anthropogenic impacts on the basin through effective management of LULC classes.

Spatial congruency or discrepancy? Exploring the spatiotemporal dynamics of built-up expansion patterns and flood risk

Most coastal cities have been experiencing unprecedented urbanization-induced flood risk, climatic events, and haphazard anthropogenic activities, jeopardizing residents' lives and building environments. Despite mounting flood-related studies, analyzing the correlation between the spatiotemporal dynamics of Built-up Expansion patterns (BE) and flood risk remains unknown and holds divergent perspectives. In this context, the coastal city of Alexandria, Egypt, characterized by multiple urban patterns and experiencing heavy rainfall annually, was selected as a testbed. Our method defined the spatiotemporal rates of BE from 1995 to 2023, quantified flood risk spatially, and finally investigated the correlation between BE and flood risk through spatial and statistical analysis. Our results show the built-up area occupied 30.32 % of the total city area till 2023, and the infilling pattern dominated the BE growth by 45.21 % of the total built-up area, followed by leapfrogging and edge expansion by 33.25 % and 21.55 %, respectively. The unplanned-infilling pattern is predominantly highly correlated with the flood-vulnerable peaks (correlation coefficient (rk) = 0.975, p-value < 0.05) and lowers dramatically towards planned-infilling regions with flood protections. Meanwhile, a spatial mismatch exists between high-risk peaks and leapfrogging and edge expansion (rk = 0.118 and 0.662, respectively, with a p-value < 0.01), indicating that controlling the built-up amount is inadequate for mitigating flood risk. Porosity-based urban configuration and spatial distribution of built-up patches in harmony with nature-based solutions are recommended for shaping flood-resilient and effective urban planning.

Examining spatial coordination of human-land-industry-service system from a regionalization approach: A case study of Beijing

Sustainable development requires a better understanding of the coupling relationship among population, urban land use, industry, and city services since they are closely related to the benefits of human beings. However, previous studies tend to focus on population and land use for urbanization-targeted insights without considering city services and industry development from a people-centered on the fine scale. This research aims to fill this gap by examining the spatial coordination of the human-land-industry-service system combining the coupling coordinated degree model and a regionalization approach with Beijing as a case study. Results show that about 3% area of Beijing is enjoying favorable coordination along with over 6% having moderate coordination, and sub-systems like human-land are experiencing better-coordinated development. Though the well-known monocentric structure of Beijing dominated by built-up expansion exists, sub-centers represented by Tongzhou and Changping rise to possibly alleviate the non-capital functions. In addition, Gini coefficients at 200-, 500-, 1000-, and 2000-meter scales decrease, and coarser resolutions present more continuous and smoother patterns. Three points concerning the unbalanced development, the scale effect, and the urbanization pattern of Beijing are further discussed. Our framework could be easily extended to explore interactions in other systems with the ability to consider both the fine details and the general spatial regionalization patterns.

Slums Evolution and Sustainable Urban Growth: A Comparative Study of Makoko and Badia-East Areas in Lagos City

This research addresses the evolution of slums in two specific areas of Lagos City, a consequence of rapid urbanization in developing economies. The study aims to analyze the key characteristics of slums’ evolution while analyzing mathematical spatial changes within the Badia-East and Makoko slum areas in two decades, focusing on promoting sustainable urban growth. The integration of Remote Sensing (RS) technology and Geographic Information System (GIS) tools has dramatically facilitated the monitoring of Land Use Land Cover (LULC) changes over two decades. This research used Landsat imagery acquired in 2000, 2010, and 2020 to meet our research aims. The research applied supervised classification and the Normalized Difference Built-up Index (NDBI) for image mapping and mathematical-based analysis. Research created a spatial framework using grid-cell maps, performed change detection, and generated detailed maps to support our analysis. A comparative analysis was also performed in the selected areas with a different history in slum management systems. The findings reveal that both Makoko and Badia-East exhibit slum-like characteristics. Makoko witnessed a significant increase in informal built-up expansion of 27.6% and 7.7% between 2000 and 2010 and between 2010 and 2020, respectively. These changes converted 34.65 hectares of non-built-up land in Makoko into built-up areas. Conversely, Badia-East reported an increase in built-up areas only between 2000 and 2010, which amounted to 8.5%. However, the government’s intervention caused a decrease of 12.7% in built-up areas of Badia-East between 2010 and 2020. The study underscores the urgent need for slum clearance and upgrading initiatives in Makoko, like those implemented in Badia-East between 2013 and 2017. The conclusion drawn from the study of Makoko’s informal built-up areas is that it is causing adverse effects on human life and the environment. The expansion has resulted in an increase in air and water pollution, which is having adverse effects. Therefore, the study recommends upgrading and clearing out Makoko and suggests taking lessons from the successful experience in Badia-East. The study also highlights the importance of prioritizing community needs and voices to ensure efficient resource utilization while safeguarding the well-being of future generations.

Land use dynamics and its influences on groundwater depth levels in South region of National Capital Territory (NCT) of Delhi, India.

The present study envisions the influences of land use dynamics on the spatial trend of groundwater depth levels over a period of two decades in the south region of NCT Delhi, India. The findings have inferred that among five major LULC categories, built-up has shown a sprawling trend (+2.17 km2/year) from the north, northeastern, and central portions to the confined patches observed in the south and southwest fringes of South Delhi from 2001 to 2021. Likewise, vegetation class has also witnessed significant increment (+1.91 km2/year) to the peripheral boundary, i.e., southern, southwest/eastern fringes, and central portions under recent initiatives of city forests, plantation drives, and urban green spaces programs. On the contrary, a negative rate of change has been observed in fallow land (-2.78 km2/year), agriculture (-1.22 km2/year), and water bodies (-0.07 km2/year). LULC transition matrix has also showed prominent conversion of fallow land into vegetation and built-up class, and change of vegetation into built-up, and fallow land category. Subsequently, corresponding LULC maps have been superimposed with long-term trends of groundwater depth levels (as spatial contours). For entire South Delhi region, depth to water levels ranged from 2.02 to 66.45 meters below ground level (mbgl) where shallower depths followed a negative trend and remain persistent throughout the time period in north and northeastern fringes. Higher fluctuations in groundwater depletion with positive trends directly get influenced with allied land use transitions such as a steady increase in built-up area and steeper depth levels (> 40 mbgl) as observed in central, southwestern, and southern parts. Moreover, buffer peripheries in the proximity of groundwater monitoring stations viz., Hauz Khas, Pushp Vihar, Jamali, Gadaipur, and Bhatti Kalan have observed deeper groundwater levels allied to built-up expansion. Thus, groundwater depletion trends can be ascribed to the incessant conversion of recharging areas into impervious zones along with uneven distribution of groundwater usage and supply. Conversely, expanding vegetative land has also shown improved groundwater depth levels. Therefore, land use influences must be managed in the long run for ensuring sustainable management of groundwater resources.

Influence assessment of urban expansion on groundwater level fluctuations in Gandhinagar, Gujarat, India.

Being the state capital of Gujarat, Gandhinagar is snowballing urban population, resulting in overexploitation of groundwater resources and consequent decline in local groundwater level. The key objective of the current research is to understand the impact of urban expansion on the groundwater level of Gandhinagar district for the last 3 decades. Long-term land use/land cover (LULC) alterations using Landsat images (1991-2021) reveal a 234% increase in overall built-up area and it is more prominent in western and southern parts than the eastern part of study area till 2021 due to urban sprawl of adjacent Ahmedabad City. Spatial distribution of groundwater levels exhibits the same pattern of groundwater level drop as that of the urban expansion and the drop of maximum depth of the groundwater level has also observed during study tenure. Rapid population growth indicates inevitable urban densification which may lead to increase in groundwater abstraction and consequent groundwater level depletion of Gandhinagar City in near future. The scenario may be worsened due to the reduction in groundwater recharge area owing to enhancement of impervious surfaces. A negative correlation is established between groundwater level and respective built-up areas from 1991 to 2011. After 2001, groundwater levels in some areas showed a rising trend and the number of those locations have increased from 2001 to 2021, indicating a sufficient supply of surface water, meeting the escalating water demand and subsequent reduction in groundwater abstraction. High fluoride content was found in many groundwater samples collected from Gandhinagar's shallow unconfined aquifer. In lieu of almost unperturbed natural groundwater recharge, built-up expansion, rising population, and over-exploitation result in groundwater level depletion in both shallow and deeper aquifers. To replenish the already depleted groundwater level and for sustainable water supply, an integration of rainwater-surface water-groundwater management plan and sustainable urban management plan is highly required. The future sustainable urban-groundwater management plan of Gandhinagar City must emphasized on the expansion of green and permeable spaces for groundwater recharge, mandatory rainwater harvesting system in every building possible, suitable area demarcation for artificial recharge, and identification of areas which are less prone to groundwater level depletion for city expansion. The outcomes of the present study will help the decision-makers to prepare inclusive and resilient urban management plan to accomplish the 6th and 11th Sustainable Development Goals of United Nations by 2030.

Measuring the extent and impact of urban expansion in an agricultural-urbanized landscape in Central Iran.

The unplanned urban expansion is a major environmental challenge in Iran resulting in vast degradation of agricultural lands. Focusing on an agricultural-urbanized landscape in Central Iran, the spatial pattern of built-up expansion was assessed from Landsat data processed in 1992 (TM), 2002 (ETM+), 2012 (TM), and 2022 (OLI). Multi-year crop NDVI was also used as a proxy for cropland suitability to assess the intensity of the urban growth impact. Results showed that (1) the area of built-up surfaces increased almost up to double than that of 1992 and passed 36% (413.42 km2) by 2022, (2) the region experienced a coalescence-diffusion transition phase with decreasing spatial connectivity of newly developed patches with old ones, (3) the most suitable croplands were lost in the middle period (2002-2012) when urban patches started to diffuse, and (4) a significantly positive spatial (Spearman's) relationship (r (22100) = 0.181, p = 0.000) was found between the area and quality of croplands which further highlights the high importance of cropland protection in the region.

Spatial transition dynamics of urbanization and Rohingya refugees’ settlements in Bangladesh

Urbanization needs to conceptualize land use policy beyond the city boundaries. It might be explained by exploring complex and interconnected interdependencies in regional key driving factors that can have tele-coupling effects during sudden shocks. Over one million Rohingya have settled in the rural/forest area in Cox’s Bazar for locational proximity and comparatively easy escape from Myanmar after domestic conflicts. This study aims to explore the spatial dimension of urbanization in a regional transition context focusing on the newly built refugee settlements in the south-eastern coastal area of Bangladesh. Additional, we also identify further research scopes and gaps in the context of the case study. Analysis of openly available global data products from historical built-up expansion from 1975 to 2021 suggests that refugee settlements impact spatial development intensity (annual rate ⁓2.55 ha/year) and population density (annual rate ⁓9431 person/year) dimensions, which have a transition (rural-to-urban) of a greater regional scale than local urbanization. Our results from the expert-based key informant interviews have a broad agreement with the quantitative findings; however, the environmental: deforestation, economic: increasing business and employment opportunities and daily living expenses; and socio-cultural impacts: increasing conflict between host and refugee communities due to prostitution, drug dealing, and insecurity, are more local than findings from the geospatial analysis. Therefore, the local development policies need an urgent adjustment to comply with the local and regional balances in the above factors, along with that international development actors may also need attentive measures in the policy formulation. Further investigations in scenario-based urbanization dynamics are required to avoid an urban desert initiated in the absence or relocation of refugee settlement. Similar kinds of studies may also be replicated even in prosperous global North countries in order to conceptualize the spatial transformative process of rapid migration influx on urbanization with innovative open data in the absence of up-to-date official datasets and adoptions of mixed methods approaches with related actors including policymakers.

Machine learning-based country-level annual air pollutants exploration using Sentinel-5P and Google Earth Engine

Climatic condition is triggering human health emergencies and earth’s surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth’s health. Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human’s health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO2 variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50–60% NO2 was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist.

Economic and socioecological perspectives of urban wetland loss and processes: a study from literatures.

Existing literatures across the world highlighted the causes and rate of wetland loss; however, so far, no researches tried to analyze how these are guided by the socioeconomic and ecological conditions. The current review work wished to explore how economic and socioecological perspectives could control the rate and drivers of urban wetland loss. Through meta-analysis, this study also intended to explore the changing polarity in research publication and collaborative research. Total 287 original research articles indicating the rates and drivers of wetland loss from 1990 to June 2022 for the first objective and 1500 articles focusing wetland researches from Dimensions AI database for the last objective were taken.Results clearly revealed that the rate of urban wetland loss varies from 0.03 to 3.13% annually, and three main drivers like built-up, agricultural expansions, pollution were identified all across the world. Loss rate was found maximum in the developing and least developed countries. Pollution, built-up expansion, and agriculture expansion, respectively, in developed, developing, and least developed nations were identified as the most dominant drivers of urban wetland loss. Linking loss rate and drivers with socioecological and economic perspectives revealed that human development index (HDI), ecological performance index (EPI), sustainable development goal index (SDGI), and social progress index (SPI) is negatively associated with the rate of urban wetland loss. Contrarily, a poverty rate encouraged higher rate of loss. This study articulated that improving these socioecological and economic conditions could help wetland conservation and restoration to achieve SDGs.

Assessing and mapping spatial accessibility of peri-urban and rural neighborhood of Durgapur Municipal Corporation, India: A tool for transport planning

The urban growth and its relation to transport network within the peri-urban zone were investigated in this paper. Using multi-temporal satellite imageries and road network data, the current study investigates a tool for transport planning and how it creates impact on the growth of built-up area. For the years 2001, 2011 and 2021, Landsat TM and OLI TIRS satellite images were used to create built-up area as well as the network data has been extracted from the different sources and create two 309*309 metrics table for the accessing of accessibility of peri-urban and rural neighborhood of Durgapur Municipal Corporation. From the above two metrics table alpha, beta, gamma, pi, grid tree pattern degree of connectivity, average shortest path length, shimbel index, connectivity index etc. have been calculated and mapped. Using the Open Root Service tools, the travel time from the peri-urban and rural neighborhood to city center has been also calculated. From the different above index, it has been observed that the along the NH-2, the accessibility is high. On the other hand, to show the built-up characteristics, direction and distance wise the built-up density and built-up expansion intensity index have been also analyzed. The highest expansion rate and intensity has been observed in the WNW, NNW, ESE and SSE direction. The geographical weightage regression also has been applied in this study to show the relation between built-up area and road density. The findings of this study could help regional planners assess the current state of active transportation options and pinpoint areas that need more attention to make them more accessible when allocating new urban facilities or reallocating those that are already there to lessen the negative effects that individual motorized transportation has on urban mobility.

Spatiotemporal changes of green cover pattern in urban areas of Batticaloa, Sri Lanka

Sri Lanka’s cities are subject to varying green cover patterns due to rapid population growth in urban areas. Over the last two decades, urbanization has driven the rampant development of built-up areas, predominantly residential development. As a developing city in Sri Lanka, Batticaloa involves rapid construction development after the civil war. This trend increases pressure on green cover, making the city population more susceptible to urban challenges. This study investigates the spatial and temporal dynamics of green cover changes in selected urban areas in Batticaloa, Sri Lanka, from 2000 to 2020. Three (3) nearby areas were selected for the study, namely Kallady, Arayamapthy, and Kattankudy. ArcGIS Normalized Difference Vegetation Index (NDVI) analysis was used to identify the green cover pattern and changes using Landsat Images. The changing patterns were detected between 2000 - 2010 and 2010 – 2020. The study reveals that the total loss of green cover was high in the first decade, approximately 44.15%, which has slightly increased about 12.58% in the second decade of the selected years. The Kattankudy zone shows a high built-up density, while the Arayampathy zone is low. This pattern has led to the further loss of green cover in the Kattankudy zone, while the other two (2) zones are significantly mixed with the green cover. A significant proportion of the green cover is occupied by the newly built-up areas, mainly residential areas throughout the period. Thus, the green cover conservation should prioritize the built-up expansion to protect the environment. The residential green cover is essential for humans’ daily lives, and one of the most vital mechanisms in the urban green cover system cannot be ignored. This finding can contribute to the land use planning application and make policies to conserve the green cover in the future.

Monitoring the population change and urban growth of four major Pakistan cities through spatial analysis of open source data

ABSTRACT Cities are complex and dynamic entities in close proximity of people, implying multi temporal observations to analyse and understand the urban context. At present, open-source data and geospatial intelligence are becoming the important means of exploring, monitoring and predicting urban status of area growth and population increase. In last few decades, unemployment and absence of infrastructures in the rural areas promoted the unplanned and haphazard urbanization across the urban centres in Pakistan. This study focuses on exploring the potential of open-source/freely available datasets for city mapping and monitoring spatially. The study gives a spatial perspective of rapidly growing cities of Pakistan using Google Earth Engine to classify Landsat images over last four decades, and discovers sprawl patterns across cities. The study works out that the built-up area is significantly increasing with population growth over four decades and there is a strong positive correlation between population growth and built-up expansion. Using Open-Source Data (Landsat images and LandScan data), this study has offered a technical solution of Google Earth Engine-supported analysis of statistics and machine learning to spatially monitoring the population change and urban growth of four major Pakistan cities. It is undoubted that our working results will provide the timely and cost-effective information to policymakers, Govt Officials and citizens for more sustainable urbanization.

Simulating Built-up Expansion in West Delhi using a Neural Network Coupled Agent Based Prioritised Growth Model

Simulating Built-up Expansion in West Delhi using a Neural Network Coupled Agent Based Prioritised Growth Model