This document provides demographic information about continental Odisha, India. It includes maps showing the variation in total population, population growth, population density, sex ratio, household size, and literacy rate across districts. The highest population is in Cuttack district, while the lowest is in Debagarh. Malkangiri has the highest population growth at 21.6%, and Bargarh the lowest at 10.02%. Cuttack also has the highest population density. Rayagada has the highest sex ratio at 1048 females per 1000 males, while Nayagarh has the lowest at 916 females per 1000 males. Physical, social, and economic factors contribute to variations between districts.
THE POPULATION CENSUS IN INDIA is a main topic in indian demography..this ppt contains basic information regarding indian census...
it was presented & uploaded by:
MANOJKUMAR A
1st m.tech urban & regional planning..
IDS MANASAGANGOTHRI , MYSORE, KARNATAKA
I’m professional presentation maker . These presentations are for sale for 20$ each, if required you can contact me on my gmail id bestpptmaker@gmail.com and you can also suggest me topics for your required presentations
its a presentation on census 2011 it shows various data which is very much helpful in knowing various data such as population rate, literacy rate, sex ratio etc....
it would be very much helpful in planning different policies...
it also tells about the history of census and as well as its importance
THE POPULATION CENSUS IN INDIA is a main topic in indian demography..this ppt contains basic information regarding indian census...
it was presented & uploaded by:
MANOJKUMAR A
1st m.tech urban & regional planning..
IDS MANASAGANGOTHRI , MYSORE, KARNATAKA
I’m professional presentation maker . These presentations are for sale for 20$ each, if required you can contact me on my gmail id bestpptmaker@gmail.com and you can also suggest me topics for your required presentations
its a presentation on census 2011 it shows various data which is very much helpful in knowing various data such as population rate, literacy rate, sex ratio etc....
it would be very much helpful in planning different policies...
it also tells about the history of census and as well as its importance
The informal sector is now seen as the next engine of growth for India's economy. Nearly 81% of all employed persons in India make a living by working in the informal sector, with only 6.5% in the formal sector and 0.8% in the household sector, according to a new ILO (International Labour Organisation) report "Women and Men in the Informal Economy – A Statistical Picture (Third edition) 2018 ."A majority of women in India are informal workers. The statistics of the ILO report indicates that 95% of work force is in the informal sector. , the transition to formality is increasingly seen as a central goal in national employment policies (ILO, 2014a).
This paper will study the challenges imposed by the in formalization of the economy and how detrimental can that be for the economic development in general.
Key words: Informal Economy, Dual burden of work, unorganized sector
1. Scene.
2. Demographic Transition Theory.
3. Demographic Transition in India.
4. Understanding India’s Demographic Transition.
5. Demographic Dividend.
6. Opportunities for India caused by the Demographic Dividend.
7. Challenges faced by India.
8. State-wise trends in the Demographic Transition.
9. Results in terms of Statistics.
10. India’s Demographic Conclusion.
11. Bibliography
This presentation provide information about Harappa Civilization. Its discovery, town planning, subsistence strategies of the people, major findings and theories of decline.
The informal sector is now seen as the next engine of growth for India's economy. Nearly 81% of all employed persons in India make a living by working in the informal sector, with only 6.5% in the formal sector and 0.8% in the household sector, according to a new ILO (International Labour Organisation) report "Women and Men in the Informal Economy – A Statistical Picture (Third edition) 2018 ."A majority of women in India are informal workers. The statistics of the ILO report indicates that 95% of work force is in the informal sector. , the transition to formality is increasingly seen as a central goal in national employment policies (ILO, 2014a).
This paper will study the challenges imposed by the in formalization of the economy and how detrimental can that be for the economic development in general.
Key words: Informal Economy, Dual burden of work, unorganized sector
1. Scene.
2. Demographic Transition Theory.
3. Demographic Transition in India.
4. Understanding India’s Demographic Transition.
5. Demographic Dividend.
6. Opportunities for India caused by the Demographic Dividend.
7. Challenges faced by India.
8. State-wise trends in the Demographic Transition.
9. Results in terms of Statistics.
10. India’s Demographic Conclusion.
11. Bibliography
This presentation provide information about Harappa Civilization. Its discovery, town planning, subsistence strategies of the people, major findings and theories of decline.
Can you imagine a world without human beings? Who would have utilised resources and created the social and cultural environment? The people are
important to develop the economy and society.
The people make and use resources and are
themselves resources with varying quality. Coal is but a piece of rock, until people were able to invent technology to obtain it and make it a ‘resource’. Natural events like a river flood or Tsunami becomes a ‘disaster’ only when they affect a crowded village or a town. Hence, population is the pivotal element in social studies. It is the point of referance from which all other elements are observed and from which they derive significance and meaning.
‘Resources’, ‘calamities’ and ‘disasters’ are all meaningful only in relation to human beings. Their numbers, distribution, growth and characteristics or qualities provide the basic background for understanding and appreciating all aspects of the environment.
It is for class 9th . It gives a short description about the ncert lesson population.............................................................................................
Migration Profile of Odisha with focus on BhubaneswarKamlesh Kumar
Migration is one the most important demographic component to determine the size, growth and structure of population of a particular region, besides fertility and mortality. For a large country like India, the study of movement of population in different parts of the country helps in understanding the dynamics of the society and societal change better. Bhubaneswar is one of the magnets for migrants in east India attributing to its exponential growth rates. This is an attempt to map the migration pattern in the city and the state.
Population Projection of Khordha District, ODISHA 2021-51Kamlesh Kumar
Work is based on Walter Isard's methods in a simplistic manner.
1. ARITHMATICAL INCREASE METHOD OF PROJECTION
2. GEOMETRIC INCREASE METHOD
3. INCREMENTAL INCREASE METHOD
‘Fashion’ is a notoriously difficult term to pin down, and it is extremely doubtful whether it is possible to come up with necessary and sufficient conditions for something justifiably to be called ‘fashionable’. Generally speaking, we can distinguish between two main categories in our notion of fashion: one that fashion refers to clothing or that fashion is a general mechanism, logic or ideology that, among other things, applies to the area of clothing.
Adam Smith , who was among the first philosophers to give fashion a central role in his anthropology, claims that fashion applies first and foremost to areas in which taste is
a central concept. This applies in particular to clothes and furniture, but also to music, poetry and architecture. Immanuel Kant provides a description of fashion that focuses on general changes in human lifestyles: ‘All fashions are, by their very concept, mutable ways of living.’
However, trends die quickly and with that comes waste. Clothing produced by fast fashion brands are oftentimes made from cheap materials, like polyester and acrylic, and not built to last: The average American throws away 80 pounds of clothing every year. We’ve been conditioned to believe that buying a garment and wearing it once is justifiable. It’s not. Due to the growing demand in the fast fashion industry, we see a vast overproduction of clothing; for example, the Copenhagen Fashion Summit reports that fashion is responsible for 92 million tons of solid waste dumped in landfills each year. This cultural shift on how we consume clothing is leaving a huge mark on the planet. Fashion has become much more than representation and being covered.
COMMUNAL HARMONY: PUNJABI & TIBETANS IN DELHIKamlesh Kumar
LANDSCAPE AS TEXT
Delhi, the majestic, cosmopolitan, sprawling capital of the nation viewed as one of the global nodes bustling with life in haste. It has maintained its identity as a pluralistic amalgamation with myriads of ethno-religious groups and minority communities. Such is the very famous, our own ‘little Tibet’- Majnu Ka Tila situated at a stone’s throw from the Delhi University North Campus. Officially known as Aruna Nagar Colony is the universal gathering place
for Tibetans living around Delhi and a transit point for the people of the trans-Himalayan range and conversely a gateway to Tibet for the Indians and foreign tourists alike as the capital city enjoys a status of a flourishing educational and political hub.
Tall buildings on either side make the narrow alley so dark it’s as if the sun never makes it here. Shops on either side sell only exotic Tibetan jewellery, Buddhist artefacts and crockery. In this labyrinth of a colony, the stalls are full of copies of branded shoes and clothes, reflecting the latest in fashion trends across Asia. Many of the tiny outlets sell Buddhist curios and Tibetan literature. Ahead, the alley opens into a bright courtyard facing the monastery. Old ladies sit in the sun, making fresh momos and laphing, pancakes rolled with chilli paste. Besides MKT is a Foodie's paradise, the eateries here are not only popular for its momos, but one can also enjoy authentic Tibetan, Chinese and Korean delicacies along with the yummiest of the English pastries.
Majnu Ka Tila not only is limited to Tibetan community but constituted by the Punjabi community as well which has a historical context.
The area provides a microcosm of diversified India where there is invisible transition and diffusion of identity, culture of distinct communities and Indianisation of Tibetan lifestyle.
For instance, many Tibetans who cannot afford the rising rents of the Tibetan enclave (due to hotels and tourist activities) are forced to live in the Punjabi Basti where renting an apartment is cheaper comparatively. Living in Punjabi zone is seen influencing a cultural and identity loss. To diffuse with the Punjabi population is perceived as a risk “of identity loss”, and forgetting your Tibetan culture. These frontiers are mental, social and religious. Nonetheless, the ethnic groups interacting and sharing a space is a matter of pride as community harmony.
An overlay operation is much more than a simple merging of linework; all the attributes of the features taking part in the overlay are carried through. In general, there are two methods for performing overlay analysis—feature overlay (overlaying points, lines, or polygons) and raster overlay. Some types of overlay analysis lend themselves to one or the other of these methods. Overlay analysis to find locations meeting certain criteria is often best done using raster overlay (although you can do it with feature data). Of course, this also depends on whether your data is already stored as features or raster. It may be worthwhile to convert the data from one format to the other to perform the analysis.
Weighted Overlay
Overlays several raster files using a common measurement scale and weights each according to its importance.
The weighted overlay table allows the calculation of a multiple criteria analysis between several raster files.
Raster- The raster of the criteria being weighted.
Influence- The influence of the raster compared to the other criteria as a percentage of 100.
Field- The field of the criteria raster to use for weighting.
Remap- The scaled weights for the criterion.
In addition to numerical values for the scaled weights in Remap, the following options are available:
Restricted- Assigns the restricted value (the minimum value of the evaluation scale set, minus one) to cells in the output, regardless of whether other input raster files have a different scale value set for that cell.
No data - Assigns No Data to cells in the output, regardless of whether other input raster files have a different scale value set for that cell.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
In the context of remote sensing, change detection refers to the process of identifying differences in the state of land features by observing them at different times. This process can be accomplished either manually (i.e., by hand) or with the aid of remote sensing software. Manual interpretation of change from satellite images or aerial photos involves an observer or analyst defining areas of interest and comparing them between images from two dates. This may be accomplished either on-screen (such as in a GIS) or on paper. When analyzing aerial photographs, a stereoscope which allows for two spatially-overlapping photos to be displayed in 3D, can aid photo interpretation. Manual image interpretation works well when assessing change between discrete classes (forest openings, land use and land cover maps) or when changes are large (e.g., heavy mechanized maneuver damage, engineering training impacts). Manual image interpretation is also an option when trying to determine change using images or photos from different sources (comparing historic aerial photographs to current satellite imagery).
Automated methods of remote sensing change detection usually are of two forms: post-classification change detection and image differencing using band ratios. In post-classification change detection, the images from each time period are classified using the same classification scheme into a number of discrete categories like land cover types. The two (or more) classifications are compared and the area that is classified the same or different is tallied. With image differencing, a band ratio such as NDVI is constructed from each input image, and the difference is taken between the band ratios of different times. In the case of differencing NDVI images, positive output values may indicate an increase in vegetation, negative values a decrease in vegetation, and values near zero no change. With either post-classification or image differencing change detection, it is necessary to specify a threshold below which differences between the two images is considered to be non-significant. The specification of thresholds is critical to the results of change detection analysis and usually must be found through an iterative process.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Accuracy assessment is an important part of any classification project. It compares the classified image to another data source that is considered to be accurate or ground truth data. Ground truth can be collected in the field; however, this is time consuming and expensive. Ground truth data can also be derived from interpreting high-resolution imagery, existing classified imagery, or GIS data layers.
The most common way to assess the accuracy of a classified map is to create a set of random points from the ground truth data and compare that to the classified data in a confusion matrix. Although this is a two-step process, you may need to compare the results of different classification methods or training sites, or you may not have ground truth data and are relying on the same imagery that you used to create the classification. To accommodate these other workflows, this process uses three geoprocessing tools: Create Accuracy Assessment Points, Update Accuracy Assessment Points, and Compute Confusion Matrix.
Thresholding
Thresholding is the process of identifying the pixels in a classified image that are the most likely to be classified incorrectly. These pixels are put into another class (usually class 0). These pixels are identified statistically, based upon the distance measures
that were used in the classification decision rule.
Accuracy Assessment : Error Matrix
Accuracy assessment is a general term for comparing the classification to geographical data that are assumed
to be true, in order to determine the accuracy of the classification process. Usually, the assumed-true data are derived from ground truth data. It is usually not practical to ground truth or otherwise test every pixel of a classified image. Therefore, a set of reference pixels is usually used. Reference pixels are points on the classified image for which actual data are (or will be) known. The reference pixels are randomly selected.
Overall accuracy: Overall accuracy is used to indicate the accuracy of whole classification (i.e. number of correctly classifier pixels divided by the total number of pixels in the error matrix)
User’s accuracy(commission error): User’s accuracy is regarded as the probability that a pixel classified on map actually represents that
class on the ground or reference data
Producer’s accuracy(omission error): Producer’s accuracy represents the probability of reference pixel being correctly classified
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
The objective of image classification is to classify each pixel into only one class (crisp or hard classification) or to associate the pixel with many classes (fuzzy or soft classification). The classification techniques may be categorized either on the basis of training process (supervised and unsupervised) or on the basis of theoretical model (parametric and non-parametric).
Unsupervised classification is where the groupings of pixels with common characteristics are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as waterbodies, developed areas, forests, etc.).
Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Input classes are selected based on the knowledge of the user. The user also sets the bounds for how similar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the input classes (AOI), plus or minus a certain increment (often based on “brightness” or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Interpolation is the process of using points with known values to estimate values at other unknown points. It can be used to predict unknown values for any geographic point data, such as elevation, rainfall, noise levels, atmospheric components and so on.
The Inverse Distance Weighting (IDW) assumes each input point to have a local influence that diminishes with distance. It assumes that closer things are more alike than those that are farther apart. It weights the points closer to the processing cell greater than those further away. A specified number of points, or all points within a specified radius can be used to determine the output value of each location. To predict a value for any unmeasured location, IDW will use the measured values surrounding the prediction location. Those measured values closest to the prediction location will have more influence on the predicted value than those farther away.
Spline estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points. This method is best for gently varying surfaces, such as elevation, water table heights, or pollution concentrations. A Regularized method creates a smooth, gradually changing surface with values that may lie outside the sample data range.
Kriging is a geostatistical interpolation technique that considers both the distance and the degree of variation between known data points when estimating values in unknown areas. Kriging assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface. The Kriging tool fits a mathematical function to a specified number of points, or all points within a specified radius, to determine the output value for each location. Kriging is a multistep process; it includes exploratory statistical analysis of the data, variogram modeling, creating the surface, and (optionally) exploring a variance surface. Kriging is most appropriate when you know there is a spatially correlated distance or directional bias in the data. It is often used in soil science and geology.
Trend is a statistical method that finds the surface that fits the sample points using a least-square regression fit. It fits one polynomial equation to the entire surface. This results in a surface that minimizes surface variance in relation to the input values. The surface is constructed so that for every input point, the total of the differences between the actual values and the estimated values (i.e., the variance) will be as small as possible.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Raster data is commonly obtained by scanning maps or collecting aerial photographs and satellite images. Scanned map datasets don't normally contain spatial reference information (either embedded in the file or as a separate file). With aerial photography and satellite imagery, sometimes the location information delivered with them is inadequate, and the data does not align properly with other data one has. Thus, to use some raster datasets in conjunction with other spatial data, we need to align or georeference them to a map coordinate system. A map coordinate system is defined using a map projection (a method by which the curved surface of the earth is portrayed on a flat surface). Georeferencing a raster data defines its location using map coordinates and assigns the coordinate system of the data frame. Georeferencing raster data allows it to be viewed, queried, and analyzed with other geographic data.
Generally, we georeference raster data using existing spatial data (target data)—such as georeferenced rasters or a vector feature class—that resides in the desired map coordinate system. The process involves identifying a series of ground control points—known x,y coordinates—that link locations on the raster dataset with locations in the spatially referenced data (target data). Control points are locations that can be accurately identified on the raster dataset and in real-world coordinates. Many different types of features can be used as identifiable locations, such as road or stream intersections, the mouth of a stream, rock outcrops, the end of a jetty of land, the corner of an established field, street corners, or the intersection of two hedgerows. The control points are used to build a polynomial transformation that will shift the raster dataset from its existing location to the spatially correct location. The connection between one control point on the raster dataset (the from point) and the corresponding control point on the aligned target data (the to point) is a link.
Finally, the georeferenced raster file can be exported for further usage.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
With increasing use of remote sensing, the need for crispier, accurate and enhanced precision has deemed to the improvement in the spectral and spatial resolution of remotely sensed imagery. For most of the systems, panchromatic images typically have higher resolution, while multispectral images offer information in several spectral channels. Resolution merge (also called pan-sharpening) allows us to combine advantages of both kinds of images by merging them into one.
The resolution merge or pan sharpening is the technique used to obtain high resolution multi-spectral images. The color information is collected from the coarse resolution satellite data and the intensity from the high resolution satellite data.
The main constraint is to preserve the spectral information for aspects like land use. Saving theimage from distortion of the spectral characteristics is important in the merged dataset.
The most common techniques for spatial enhancement of low-resolution imagery combining high and low resolution data can be used are: Intensity-Hue-Saturation, Principal Component, Multiplicative and Brovey Transform.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Remote Sensing: Normalized Difference Vegetation Index (NDVI)Kamlesh Kumar
The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that uses the visible and near-infrared (NIR) bands of the electromagnetic spectrum to analyze whether the target (image) being observed contains green vegetation or not. Healthy vegetation (chlorophyll) reflects more near-infrared (NIR) and green light compared to other wavelengths. But it absorbs more red and blue light. This is why our eyes see vegetation as the colour green. If we could see near-infrared, then it would be strong for vegetation too.
It is basically measured through the use of Intensity, Hue and saturation of an image and through pixels as well.
The density of vegetation (NDVI) at a certain point on the image is equal to the difference in the intensities of reflected light in the red and infrared range divided by the sum of these intensities.
푁퐷푉퐼=((푁퐼푅−푅퐸퐷))/((푁퐼푅+푅퐸퐷))
The result of this formula generates a value between -1 and +1. If you have low reflectance (low values) in the red band and high reflectance in the NIR, this will yield a high NDVI value. And vice versa.
Remote Sensing: Principal Component AnalysisKamlesh Kumar
Principal components analysis is a orthogonal transformational technique (preserving the symmetry between vectors and angles) to reveal new set of data arguably better from the original data set and better capture the essential information as well. It happens often that some variables are highly correlated with a lot of duplication. Instead of discarding the redundant data, principal components analysis condenses the info. in inter-correlated variables into a few variables, called principal components.
The main idea of Principal Component Analysis (PCA) is to reduce the dimensionality of a data set consisting of many variables correlated with each other, either heavily or lightly, while retaining the variation present in the dataset, up to the maximum extent.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
The advantage of digital imagery is that it allows us to manipulate the digital pixel values in the image. Even after the radiometric corrections image may still not be optimized for visual interpretation. An image 'enhancement' is basically anything that makes it easier or better to visually interpret. An enhancement is performed for a specific application as well. This enhancement may be inappropriate for another purpose, which would demand a different type of enhancement.
Filtering is used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on their spatial frequency. ‘Rough’ textured areas of an image, where the changes in tone are abrupt, have high spatial frequencies, while ‘smooth’ areas with little variation have low spatial frequencies. A common filtering procedure involves moving a ‘matrix' of a few pixels in dimension (ie. 3x3, 5x5, etc.) over each pixel in the image, using mathematical calculation and replacing the central pixel with the new value.
A low-pass filter is designed to emphasize larger, homogeneous areas of similar tone and reduce the smaller detail in an image. Thus, low-pass filters generally serve to smooth the appearance of an image. In some cases, like 'low-pass filtering', the enhanced image can actually look worse than the original, but such an enhancement was likely performed to help the interpreter see low spatial frequency features among the usual high frequency clutter found in an image. High-pass filters do the opposite and serve to sharpen the appearance of fine detail in an image. Directional, or edge detection filters are designed to highlight linear features, such as roads or field boundaries. These filters can also be designed to enhance features which are oriented in specific directions.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Mountainous regions occupy one-fourth of the world’s terrestrial surface, most rich in diverse landscapes and hold on to the biodiversity and cultural diversity along with supporting 10% of humankind with their direct life support base. Most mountainous regions have been at the far periphery of mainstream societal concerns for a long time. Remote, relatively inaccessible, they were generally pictured as difficulty, unyielding and unprofitable environments. Very less have focused attention on mountainous people and cultures, primitive religion, marginal survival, unusual adaptation to very high altitude, fraternal polyandry to obliterate informed communication and more meaningful analysis in practical sense. Early research concentrated mainly on specialised studies with little cross disciplinary endeavour. During the last few decades there have been spasmodic accounts of the highland and lowland mainly induced by events of great economic or political significance and due to the degradation of highlands which are potential threats to subjacent lowland population centre. Recent developments, expanding highland research and awareness spread by institutions and governments have shone a new ray of light towards the bright future. However, increased awareness with political advocacy must be pursued further.
Water is hydrosphere is made up of all the water on Earth. This includes all of the rivers, lakes, streams, oceans, groundwater, polar ice caps, glaciers and moisture in the air (like rain and snow). The hydrosphere is found on the surface of Earth, but also extends down several miles below, as well as several miles up into the atmosphere. So, there is a need for study of water as a scarce resource.
WHAT IS HYDROLOGICAL CYCLE
SYSTEM APPROACH IN HYDROLOGY
HYDROLOGIC INPUT & OUTPUT
VARIATION IN HYDROLOGICAL CYCLE
COMPONENTS
EVAPORATION
EVAPOTRANSPIRATION
PRECIPITATION
INTERCEPTION
INFILTRATION
GROUND WATER
RUN-OFF
HUMAN IMPACT
EARTH SURFACE
CLIMATE CHANGE
ATMOSPHERIC POLLUTION
MULTI PURPOSE PROJECTS
WATER WITHDRAWAL
MANAGEMENT AND CONTROL
An assessment on the temperate ecosystem with the following sub headings:
Geological evolution: Location and Extent
Atmospheric changes
Hydrological Changes
Land Degradation
Biodiversity Loss
Challenges to Human Community
Geosystem Approach: El Nino Southern Oscillation EffectsKamlesh Kumar
Earth system as a whole is very complex and dynamic, for that matter we prepare models to represent the functioning linkages and processes for better understanding. However, the geo-systems can not be summed up in just one model. Hence, we use system analysis approach, if we see Earth as a giant system, there're many sub-systems for better comprehension representing only a particular component of the system.
Here, I've tried to cover the geo-system approach siting a globe affecting example of the El Nino Southern Oscillation (ENSO) phenomena.
This report is detailed study of the research conducted in Kirori Mal College. The basic objective of this report is to get a tough insight in the use of research techniques. Geography, being a field science, a geographical enquiry always need to been supplemented through well planned Research. Research is an essential component of geographic enquire. It is a basic procedure to understand the earth as a home of humankind. Disaster management is an inseparable part of the discipline especially which deals with the study of natural phenomena. This research focuses upon the FIRE safety plan of the institution. It is carried out through observation, sketching, measurement, interviews, etc. The Research facilitate the collection of local level information that is not available through secondary sources.
In this report, various methodologies have been employed such as my, measurement and interviewing, photographing, examining, the collection and gathering of information at different corners of the institution and later, tabulating and computing them is an important part of the field work.
Furthermore, the research report has been prepared in concise form alongside with maps and diagrams for giving visual impressions. Moreover, it contains all the details of the procedures followed, methods, tools and techniques employed.
Disaster Prevention & Preparedness: Earthquake in NepalKamlesh Kumar
This report is detailed study of the field survey conducted in Kathmandu and Sindhupalchowk in Nepal on the earthquake disaster. The basic objective of this report is to get a tough insight in the use of field techniques regarding disaster management. Geography deals with human interaction with nature. This phenomenon can be better understood through field studies. Geography, being a field science, a geographical enquiry always need to be supplemented through well planned field surveys. Field is an essential component of geographic enquire. It is a basic procedure to understand the earth as a home of humankind. It is carried out through observation, sketching, measurement, interviews, etc. Field work takes the children out of the class and enables them to better understand the subject by visiting the areas practically giving an insight into the social, cultural and economic lives of the people. This also adds up the advantage of visiting the grass root levels of the society and ameliorative comprehension of the GLOCAL lives. It also has instilled various research making techniques in the budding geographers and shaping their thinking perspectives. The field surveys facilitate the collection of local level information that is not available through secondary sources.
In this report, various methodologies have been employed such as mapping, digitization, measurement and interviewing (questionnaires designing), the collection and gathering of information at the local level by conducting primary surveys and later, tabulating and computing them is an important part of the field survey.
Furthermore, the field study report has been prepared in concise form alongside with maps and diagrams for giving visual impressions. Moreover, it contains all the details of the procedures followed, methods, tools and techniques employed and the modern technology of navigation, satellite connections, GIS software have been very helpful in the pre-field drills.
The report has the following headings and sub-headings:
Introduction
Study area
Transit: Table & Maps
Disaster scenario of Nepal
Earthquake: Timeline
Causes
Impact
Who is helping Nepal?
Reconstruction and Rehabilitation Status
Objectives & Methodology
Literature review
Data representation and Analysis
Findings and Suggestions
Conclusions
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
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2. CONTENTS
Statement of The Problem 1
Study Area 1
Administrative Map 2
Indicators 4
Collection of Data 4
Data Analysis
Formulae 5
Representation and Interpretation
Map: District Population 6
Map: Population Growth 8
Map: Population Density 10
Map: Sex Ratio 12
Map: Household Size 14
Map: Literacy 16
Map: Composite Index 18
Appendix 19
3. 1
STATEMENT OF THE PROBLEM
Although the state is endowed with vast natural resources it has remained on the bottom of
the developmental chart of the nation. With such a reserve of natural resources and human
resource potential, it is like a hibernating beast which must awake for good. Stealing the
limelight of the most favourable smart city, the capital is growing like never before along
with a few more cities. Yet the state remains mostly rural and lagging in most aspects except
for the coastal regions. My analysis is that the state has not been given its due attention in
planning which is the reason for its present backwardness.
STUDY AREA: CONTINENTAL ODISHA
CRITERIA
✓ Based on the broader division of ‘central highlands’ taken from the regional division
of OHK Spate and RL Singh.
✓ Homogeneity of demographic aspects
✓ Polarization of geographical aspects including forested area as well as abundance of
natural resources.
✓ To study the continental part of the state which is predominantly rural and backward
as opposed to their counterpart coastal districts.
Geographical Profile of Odisha
The state of Odisha or Orissa is located in the eastern part of the country, neighbor to the
state of West Bengal, Jharkhand, Chhattisgarh, Telangana and Andhra Pradesh. The state got
separated from Bihar on 1st
April 1936 with the historic city Cuttack as the capital which was
later shifted to Bhubaneswar in 1956. The temple city along with the state is testimonial to
the Indian cultural heritage in every sense, the state is birth place of world-famous cultural
phenomenon- Rath Yatra.
The 9th
largest state of India has the population of 4.18 crores as of 2011. The population
density is 260 persons per sq. km. which is fairly below the national average. The has
recorded a growth rate of about 14% which is below the national average as well. The
literacy rate in the state is about 73% a figure that has improved tremendously in the last few
years due to the consistent efforts of the government. The sex ratio at 978 is appreciable.
PHYSICAL
The state can be broadly divided into four categories: the northern plateau, the Eastern Ghats,
the central tract, and the coastal plains. Geology of the state varies in structure and age, the
continental part is one of the ancient rocks on the planet- Gondwana rock system. The
northern plateau is an extension of the forest-covered and mineral-rich Chota Nagpur plateau.
The region is drained by Mahanadi, Subarnarekha, Baitarani, Brahmani and Rushikulya. The
mountain peak Deomali (1672 mts.) is situated in Koraput district. The state is leading in the
production of bauxite, manganese ore, graphite, and nickel ore among others. Coal from the
Talcher field in Dhenkanal is power house to many industries. Apart from this the state is
budding in hydroelectric power mainly from the Hirakud and Machkund projects.
The region has a tropical wet-dry (savanna) climate. January is the coolest month while
May is the warmest, Balangir recording highest of 44.2 °C in 2019. Average annual rainfall in
the state is about 1,500 mm, mostly due to the southwest monsoon.
5. 3
THE ODIA
Odisha has predominantly a rural population, Scheduled Tribes and
Scheduled Castes forming 2/5th
of the population. The caste structure in
Odisha is similar to that in other states of eastern India. Just below the
highest-level Brahmans are the Karanas (the writer class), who
claim Kshatriya status, with the pen as their weapon rather than the
sword1
. The Khandayats (Swordsmen) are mostly cultivators but call
themselves “Khandayat-Kshatriyas.” The tribal people for a long time
have been undergoing the process of Hinduization, and many tribal
chieftains also have claimed Kshatriya status. The irrigated rice-farming
region of the coastal plains is heavily populated. Although some tribal
people have settled in the plains, most live in the hill areas. Most of the
major cities are situated in the coastal plains. Odisha has a well-
developed social, physical and industrial infrastructure, and the state
government has undertaken several infrastructural projects to further
promote overall development. The state's infrastructure includes well-
connected road and rail networks, airports, ports, power, and telecom.
ECONOMY
3/5th
of the population is engaged in agriculture despite of the fact that
most of the land is unfavourable of high productivity. Nevertheless, the
state has evolved as a key player in mineral and metal industries in the
nation. The state's economy witnessed high growth rates between 2011-
12 and 2017-18, with Gross State Domestic Product (GSDP) of 10.30
per cent2
. It is the first state in India to have undertaken reform and
restructuring initiatives in the power sector. As of 2019, the state had a
total installed power generation capacity of 7,653.58 megawatt (MW).
The state has attracted Foreign Direct Investment (FDI) inflows worth
US$ 549 million during the period April 2000 to March 20193
. The
service sector is the largest chunk of the state economy with tourism
playing a major role in the growth.
1
https://www.britannica.com/
2
https://www.ibef.org/states/odisha.aspx
3
Department for Promotion of Industry and Internal Trade(DPIIT) Report
SL. DISTRICT
1 Anugul
2 Balangir
3 Baleshwar
4 Bargarh
5 Baudh
6 Bhadrak
7 Cuttack
8 Debagarh
9 Dhenkanal
10 Gajapati
11 Ganjam
12 Jagatsinghpur
13 Jajpur
14 Jharsuguda
15 Kalahandi
16 Kandhamal
17 Kendrapara
18 Keonjhar
19 Khordha
20 Koraput
21 Malkangiri
22 Mayurbhanj
23 Nabarangapur
24 Nayagarh
25 Nuapada
26 Puri
27 Rayagada
28 Sambalpur
29 Subarnapur
30 Sundargarh
83%
17%
RURAL URBAN
Table 1 List of
Districts, Odisha
6. 4
INDICATOR
Demographic Category
District wise Total Population of the Region 2011
District wise Total Population of the Region 2001
District wise Population Density of the Region 2011
District wise Number of Households of the Region 2011
District wise Sex Ratio of the Region 2011
District wise Literacy Rate of the Region 2011
Population Size is the total number of people living in each unit of area. It includes all live
population from each age-sex group.
Population Growth is the increase in the number of individuals in a population. The
population growth rate is the rate at which the number of individuals in a population
increases in a given time period, expressed as a fraction of the initial population.
Population density is a measurement of population per unit area. In simple terms population
density refers to the number of people living in an area per kilometre square.
Sex Ratio is the ratio of males to females in a population. In Bihar sex ratio at birth, which is
the number of females born per 1,000 males, is showing a worrying decline, according to a
Sample Registration System (SRS) survey.
Household Size: A ‘household’ is usually a group of persons who normally live together and
take their meals from a common kitchen unless the exigencies of work prevent any of them
from doing so. It is the number of households per 100 population in the area.
Literacy is the ability to read and write. Literacy rate is the total number of literate persons in
a given age group, expressed as a percentage of the total population in that age group.
Literacy rate is calculated by dividing the number of literates of a given age range by the
corresponding age group population and multiply the result by 100.
COLLECTION OF DATA
Census of India 2001: Primary Census Abstract
Census of India 2011: Primary Census Abstract
District Census Handbook 2011
Survey of India
Universal Transverse Mercator (UTM) Projection System
The data for all the maps has been collected mainly from the official websites and reports of
government departments. This atlas depends entirely on second hand data. Firstly, topic
Demographic profile was chosen and then the indicators were chosen accordingly (e.g.
Population growth, Population density, Population size, Household size, Sex ratio and Literacy
rate). The maps were prepared with the help of software called ArcGIS 10.5. For convenience,
data was compiled in Microsoft excel sheets and saved it in CSV (comma delimited) format
and joined the data in ArcGIS and then classified and displayed the data through choropleth
technique.
7. 5
DATA ANALYSIS
FORMULAE
1. Population Size: For convenience, first calculate the average population of each district in
the whole region.
LQ= Population of the district 2011/Average share of population 2011
2. Population Growth (%) = [(Population 2011-Population 2001/ Population 2001)*100]
Location Quotient= District Population Growth (%)/ Region Population Growth (%)
3. Population Density= Population 2011/Area (Sq. Km.)
Location Quotient= District Population Density/ Region Population Density
4. Sex Ratio= (Males 2011/ Females 2011)*1000
Location Quotient= District Sex Ratio/ Region Sex Ratio
5. Household Size= (Total District Households 2011/ District Total Population 2011)*100
Location Quotient= District Household Size / Region Household Size
6. Literacy Rate= (Total Literates in District 2011/ Total Population 7+ 2011)*100
Location Quotient= District Literacy rate/ Region Literacy rate
7. Composite Index= Sum of all the Location Quotient/6
9. 7
INTERPRETATION
The above map shows the variation of total population in a particular district of Continental
Odisha region. The highest population growth is recorded in the district of Cuttack with 2.62
million people and LQ of 2.15 which is shown by the Red colour in the map with contrast to
the lowest of 3.1 lakh in Debagarh with a LQ of 0.25 represented by Yellow colour.
Physical Factors: Due to different in relief and geomorphic factor population is widely varied
throughout the region. Coastal areas such as Cuttack and Gajapati have an upper hand in
population concentration. Mineral rich regions of the north and the north east are equally
concentrated.
Social Factors: Migration to Odisha from the neighbouring states as well as intra state
migration especially rural-urban migration for better opportunities and lifestyle along with
inter-state marriages are another factor contribution to the dense population in some districts.
Economic Factors: The rapid economic growth led to the heavy in-migration of labourers,
professionals from all parts. The improvement in transport and communication, trade and
construction are an attribute. The smart city status to the capital gave a major boost to the whole
state altogether.
11. 9
INTERPRETATION
The above map shows the variation of population growth of Continental Odisha region. The
highest population growth is recorded in the district of Malkangiri with 21.6% growth which
is shown by the Red colour in the map with contrast to the lowest of 10.02% in Bargarh
represented by Yellow colour.
Physical Factors: Due to different in relief and geomorphic factor population growth is not
equally distributed. Mineral rich regions have been already habituated and at present the other
parts of the state are being filled up rapidly.
Social Factors: Migration to Odisha from the neighbouring states is a historical
phenomenon, the migration and hinduization of the migrants is a well-known fact. Along
with this inter-state marriages are another factor contribution to the growth. Family planning
is another major issue in the region. But there has been a considerable expansion in social
programmes, centrally sponsored and state government initiated, over the years. These
programmes cover employment generation, pensions, public distribution system, health and
sanitation, housing, education, special schemes for girls etc. in the region.
Economic Factors: Emigration and immigration of workers, students and labours plays an
important role in the variation of population growth in the region. The rapid economic growth
led to the heavy in-migration of agricultural labourers and poor peasants, mainly from
neighbouring states. The improvement in transport and communication, trade and
construction is an attribute.
13. 11
INTERPRETATION
The region covers the central, north, north-eastern and southern part of Odisha. The above map
shows the variation of population density of continental Odisha. The highest population density
is recorded in the district of Cuttack with Location Quotient of 2.85 which is shown by the Red
colour in the map because it is the former capital of state, apart from that it is a historic city, a
major hub as well as shares the status of twin-city with the current capital. The lowest is
recorded in the district of Kandhamal with LQ of 0.39 represented by Yellow colour.
Physical Factors: The region is drained by mighty rivers such as Brahmani, Mahanadi and
Indravati among others. Apart from this there are mineral mines and industries in the northern
part and forest in the southern contributing to the high variation of population density. The
Mahanadi river acts as the source of hydro power generation and also of irrigation system for
the population especially the region along the river. And the low population density has been
recorded due to the backwardness and forested area along with security issues as the
Dandakaranya is hub to the Maoists.
Social Factors: The districts share a common culture with a pinch of heterogenous tribal
population like Munda, Santhal, Savara, Juang, Oraon etc with the Odia, Bengali and Telugu.
The main religion is Hinduism. Other religions are practiced by small minorities. Places are
historical and cultural associations with Buddhism, Jainism and Lingayats.
Economic Factors: Agriculture remains the primary source of employment in the region and
is known to suffer from the vagaries of the weather, dependent upon the Monsoon. Other major
occupation pie is occupied by the manufacturing sector.
15. 13
INTERPRETATION
The above map shows the variation of Sex ratio in the Continental Odisha region. The highest
sex ratio is recorded in the district of Rayagada (1048 females per 1000 male) with Location
Quotient of 1.06 which is shown by the Red colour in the map and the lowest is recorded in the
district of Nayagarh (916 females per 1000 male) with LQ of 0.926 represented by Yellow
colour. Most of the districts have high level of sex ratio which is a matter of pride for the state.
Physical Factors: The most striking geographical feature of the region is the transitional
diversity in the state from corner to corner. Most districts experience in-migration of males for
better employment opportunities. Males are considered to be an asset for the family and for
society, as they are considered to be more productive and also since family lineage is carried
forward by the males. However, the results of the region are surprisingly contrasting.
Agriculture in these areas are characterized by small landholdings or peasants working in the
lands of the big landowners. Females, owing to their low literacy rates, have no option but to
be a cultivator. They usually work in the lands belonging to the male head of the family or as
a daily wage labourer in the lands of big landowners.
Social Factors: The high sex ratio in the rural areas reflects the social status of women in these
areas. Women, who are economically dependent on the men are confined to their homes and
are made to do household chores and take care of the elders, while the males of the households
migrate outside for better employment opportunities. Prejudice against female mobility leading
to very low literacy among them and poor female-employment opportunities in towns are also
inhibitory factors of female migration to urban centres.
The low sex ratio in some central districts are due to the existence of traditional patriarchal
system where the imbalance in sex ratio is, mainly, due to desire for male child. Family in such
society is considered complete only on the achievement of a son. No wonder, female child is
considered as an economic liability whereas male is credited with an economic asset by society.
Economic Factors: Out migration of males to work in urban centres is the main reason behind
the high female sex ratio. Females are made to work on in contrast to the Western or developed
countries, Indian urban centres are predominantly characterised by an excess of males over
females. Males migrate to towns in search of jobs, leaving their families at home due to the
higher cost of living in towns.
The urban centres consist of skilled workers working in manufacturing sectors or trade and
business. Males, having the advantage of a better education, are more likely to be skilled for
employment in these sectors. In contrast to the other parts of the region, this region is
predominantly characterised by an excess of males over females. Males migrate to urban
centres to seek employment leaving their families at home due to the higher cost of living in
towns.
17. 15
INTERPRETATION
The above map shows the variation of Household size of Continental Odisha region. The
highest household size is recorded in the district of Kalahandi with Location Quotient of 1.08
which is shown by the Red colour in the map, Sundargarh, only district with the medium
category value of LQ 0.975 and the lowest is recorded in the district of Cuttack with LQ of
0.939 represented by Yellow colour.
Physical Factors: The region is drained by mighty rivers such as Brahmani, Baitarani,
Mahanadi and Indravati among others makes the soil loose near the bank. Apart from this there
are mineral mines and industries in the northern part and forest in the southern contributing to
the high variation of population density.
Social Factors: The joint family system prevalent in the region promote large households.
Moreover, the districts share a common culture with a pinch of heterogenous tribal population
like Munda, Santhal, Savara, Juang, Oraon etc with the Odia, Bengali and Telugu. The main
religion is Hinduism. Other religions are practiced by small minorities. Places are historical
and cultural associations with Buddhism, Jainism and Lingayats. Similarly, nuclear family is
promoted in urban spaces.
Economic Factors: If we focus on its economic factors the more developed areas have small
size of household due to the concept of nuclear faily which has been trending since the 1980s
in the nation. Therefore, migration plays an important role in the economic aspects leading to
variation of household size in the region.
19. 17
INTERPRETATION
The above map shows the variation of Literacy rate of the continental Odisha region. The
highest literacy rate is recorded in the district of Jharsuguda (86.27%) with Location Quotient
of 1.264 which is shown by the Red colour in the map, while the lowest is recorded in the
district of Nabarangapur (48.2%) with LQ of 0.7 represented by Yellow colour.
Physical Factors: The region’s southern part is less developed comparatively with less
infrastructure and general awareness. Drained by the Indravati and Rushikulya river is
dominated by forested area and tribal population lacking communication and better
infrastructure, which ultimately leads to degraded education facilities.
Social Factors: Districts with higher urban centres are inhabited mostly by the working class
and higher class who are more aware of education and lifestyle. Unlike the rural setting,
where joint families are mostly found, these cities have nuclear families where working
individuals have lesser number of people immediately dependent on them. This leads to
children going to school, instead of working on paddy fields.
Economic Factors: The central part painted in red are urbanised and mining towns as well as
urban spaces with better lines of communications, infrastructure and technology aids in the
means to acquire better facilities resulting in better education system. Rural and urban
networks became the arteries of commercial and economic activities and growth. This has
made a significant impact on boosting the literacy rates of these districts. Creation of
employment opportunities have encouraged parents in sending their children to schools and
attain higher levels of education.
20. 18
MAP 7: COMPOSITE INDEX
A composite index is a grouping of many variable entities or factors combined together in a
standardised way to provide a meaningful result. Generally, a composite index has a large
number of factors that are averaged together to form an overall figure that’s easily
comprehensible. In this case my factors are as follows:
1. Total District Population
2. Population Growth
3. Population Density
4. Sex Ratio
5. Household Size
6. Literacy
The composite index is calculated by summing all the LQ’s of above-mentioned
factors/indicators and dividing it by number of LQ. It is found that northern portion of the
regions have moderate demography pattern if we take it as overall and in central and southern
parts have low demographic aspects which directs to need for development as a whole.