Coverage in basic version of City Skyline of India - Geography
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The City Skyline of India, 2008-09 provides information on different kinds of consumers, market sizes, their income, savings and expenditure patterns. The top 112 cities are also ranked on the basis of “Economy” and “Reside in” indices. It also comprises people’s perception about their cities on education, health facilities, environment, entertainment and public facilities captured through a survey. The product covers demography, age distribution, consumer markets – assets owned, socio economic classifications, consumer psychographics, income categories, earning potential, education, housing, safety, entertainment, environment, health, among others. Your requirements of Consumer Market Research are addressed very effectively. The City Skyline of India is your key Market Research Resource for Urban India.
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The phenomenon of increased urbanization in India is facing one of its foremost challenges in the form of disparity between redistribution of economic opportunity and growth. The centre of poverty is gradually shifting towards urban centres and this situation is further worsened by already high population densities, poor infrastructure and a general lack of effective housing policy and provisioning for the poor. The Census of India 2011 suggests that 66% of all statutory towns in India have slums, with 17.4% of total urban households currently residing. However, this estimate of slums takes into account certain criteria set by the Census for a settlement to be featured as a slum. A large proportion of households who are living in similar or poorer dwelling conditions than those living in slums have been omitted. This study encompasses all those settlements that comply with the definition of slums (as given by the Census of India) as well as those with similar or poorer dwelling conditions that those of slums as ‘Informal Settlements’, because these are primarily dwelling units where most of the urban poor live. Interventions should be targeted at all these informal settlements instead of only slums as defined by the Census, since the quality of life and infrastructure in these informal settlements are similar to those of slums.
The objective of the present study is to look into the contribution of informal settlement households to urban economy. The primary reason for looking at this particular question is to determine whether the informal settlement households, who normally form the poor strata of the urban population, do contribute to the urban economy to a significant extent or not. If they do contribute to urban economy, whether providing proper urban services to them should be treated as their legitimate right? For greater comprehension, this study attempts to discover the role of informal settlement population as a productive agent in urban economy, which is in contrast to the general notion that this section of population is “burden to the city.”Contribution of Urban Informal Settlement Dwellers to Urban Economy in India
Contribution of Urban Informal Settlement Dwellers to Urban Economy in IndiaIndicus Analytics Private Limited
PREDICTING GROWTH OF URBAN AGGLOMERATIONS THROUGH FRACTAL ANALYSIS OF GEO-SPATIAL DATA
Location Analytics is one of the fastest emerging fields in the broad area of Business Intelligence/Data Science. By
some industry estimates, almost 80% of all data has a location dimension to it. Consequently, identification of
trends and patterns in spatially distributed information has far reaching applications ranging from urban planning, to
logistics and supply chain management, location based marketing, sales territory planning and retail store location.
In view of this, we present an approach based on Fractal Analysis (FA) of highly granular geo-spatial data.
Specifically, we use proprietary data available at approximately1 square km level for New Delhi, India provided by Indicus Analytics (India’s leading economic data analytics firm based in New Delhi). We compare and contrast the patterns and insights generated using the FA approach with other more traditional approaches such as spatial to correlation and structural similarity indices. Preliminary results indicate that there are indeed “selfsimilar” local patterns that are completely missed by spatial correlation that are accurately captured by the more sophisticated FA approach. These patterns provide deep insights into the underlying socio-economic and demographic processes and can be used to predict the spatial distribution of these variables in the future. For example, questions such as what are the pockets of population growth in a city and how will businesses and government respond to that growth can be answered using the proposed approach.Predicting growth of urban agglomerations through fractal analysis of geo spa...
Predicting growth of urban agglomerations through fractal analysis of geo spa...Indicus Analytics Private Limited
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The phenomenon of increased urbanization in India is facing one of its foremost challenges in the form of disparity between redistribution of economic opportunity and growth. The centre of poverty is gradually shifting towards urban centres and this situation is further worsened by already high population densities, poor infrastructure and a general lack of effective housing policy and provisioning for the poor. The Census of India 2011 suggests that 66% of all statutory towns in India have slums, with 17.4% of total urban households currently residing. However, this estimate of slums takes into account certain criteria set by the Census for a settlement to be featured as a slum. A large proportion of households who are living in similar or poorer dwelling conditions than those living in slums have been omitted. This study encompasses all those settlements that comply with the definition of slums (as given by the Census of India) as well as those with similar or poorer dwelling conditions that those of slums as ‘Informal Settlements’, because these are primarily dwelling units where most of the urban poor live. Interventions should be targeted at all these informal settlements instead of only slums as defined by the Census, since the quality of life and infrastructure in these informal settlements are similar to those of slums.
The objective of the present study is to look into the contribution of informal settlement households to urban economy. The primary reason for looking at this particular question is to determine whether the informal settlement households, who normally form the poor strata of the urban population, do contribute to the urban economy to a significant extent or not. If they do contribute to urban economy, whether providing proper urban services to them should be treated as their legitimate right? For greater comprehension, this study attempts to discover the role of informal settlement population as a productive agent in urban economy, which is in contrast to the general notion that this section of population is “burden to the city.”Contribution of Urban Informal Settlement Dwellers to Urban Economy in India
Contribution of Urban Informal Settlement Dwellers to Urban Economy in IndiaIndicus Analytics Private Limited
PREDICTING GROWTH OF URBAN AGGLOMERATIONS THROUGH FRACTAL ANALYSIS OF GEO-SPATIAL DATA
Location Analytics is one of the fastest emerging fields in the broad area of Business Intelligence/Data Science. By
some industry estimates, almost 80% of all data has a location dimension to it. Consequently, identification of
trends and patterns in spatially distributed information has far reaching applications ranging from urban planning, to
logistics and supply chain management, location based marketing, sales territory planning and retail store location.
In view of this, we present an approach based on Fractal Analysis (FA) of highly granular geo-spatial data.
Specifically, we use proprietary data available at approximately1 square km level for New Delhi, India provided by Indicus Analytics (India’s leading economic data analytics firm based in New Delhi). We compare and contrast the patterns and insights generated using the FA approach with other more traditional approaches such as spatial to correlation and structural similarity indices. Preliminary results indicate that there are indeed “selfsimilar” local patterns that are completely missed by spatial correlation that are accurately captured by the more sophisticated FA approach. These patterns provide deep insights into the underlying socio-economic and demographic processes and can be used to predict the spatial distribution of these variables in the future. For example, questions such as what are the pockets of population growth in a city and how will businesses and government respond to that growth can be answered using the proposed approach.Predicting growth of urban agglomerations through fractal analysis of geo spa...
Predicting growth of urban agglomerations through fractal analysis of geo spa...Indicus Analytics Private Limited
With technology based solutions seen as key to achieving financial inclusion, the role of e-money becomes important in reaching out to the unbanked masses. While regulatory space in India has been slowing opening up to allow non-banks to act as e-money issuers and prudential norms are in place, regulatory concerns remain regarding the safety of customer funds and the potential impact of e-money on monetary aggregates. The regulator’s dilemma, as described by David Porteous, is whether or not to implement measures that may hinder expansion of access to nonusers in the interest of greater protection for those who already have access, and it is for each country to evolve models and practices appropriate to their economy. It is however instructive to absorb lessons from international experiences that exemplify how regulations can evolve to meet the challenges involved in non-bank e-money issuers, all with the aim of bringing about universal financial inclusion. POLICY BRIEF ON FINANCIAL INCLUSION IN INDIA: E-money issuers- Risks, Rewards...
POLICY BRIEF ON FINANCIAL INCLUSION IN INDIA: E-money issuers- Risks, Rewards...Indicus Analytics Private Limited
M-PESA is a mobile based transfer of money between customers, facilitated by network of retail agents. Kenya is the first country to have adopted M-PESA where the model witnessed huge success and is contributing big way in enabling financial inclusion in the country. Deployment of M-PESA in India can bring similar benefits as experienced in Kenya. Growing mobile penetration in rural areas would ensure that people are able to benefit from mobile based money transfer concept. Indian regulatory system has also been gearing up to allow technology benefits in enabling financial inclusion, developments are only at introductory stage. POLICY BRIEF ON FINANCIAL INCLUSION IN INDIA: The M-PESA Model- An Overview
POLICY BRIEF ON FINANCIAL INCLUSION IN INDIA: The M-PESA Model- An OverviewIndicus Analytics Private Limited
POLICY BRIEF ON FINANCIAL INCLUSION IN INDIA: The Business Correspondent mode...
POLICY BRIEF ON FINANCIAL INCLUSION IN INDIA: The Business Correspondent mode...Indicus Analytics Private Limited
Sustaining the development of the country will require current levels of growth to trickle down to the poorest and more excluded of society. A critical way to extend these benefits will be to bring people into the formal sector of finance, whereby they may have more reliable and cheaper access to their financial needs of remittances, savings, borrowings etc. Many models have been suggested as alternatives to traditional branch banking, the current penetration of which is abysmally low. Options include mobile banking, enlisting business correspondents, encouraging MFIs, etc., and each option has its strengths and weaknesses. The models with the greatest potential for the future should be able to leverage on existing retail networks and the rapidly expanding ICT (information and communication technology) platform. As such, the BC model, clubbed with m-banking technology, holds the greatest promise to achieving universal inclusion and steps must be taken to encourage its sustained proliferation POLICY BRIEF ON FINANCIAL INCLUSION IN INDIA: Models to enable universal inc...
POLICY BRIEF ON FINANCIAL INCLUSION IN INDIA: Models to enable universal inc...Indicus Analytics Private Limited
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With technology based solutions seen as key to achieving financial inclusion, the role of e-money becomes important in reaching out to the unbanked masses. While regulatory space in India has been slowing opening up to allow non-banks to act as e-money issuers and prudential norms are in place, regulatory concerns remain regarding the safety of customer funds and the potential impact of e-money on monetary aggregates. The regulator’s dilemma, as described by David Porteous, is whether or not to implement measures that may hinder expansion of access to nonusers in the interest of greater protection for those who already have access, and it is for each country to evolve models and practices appropriate to their economy. It is however instructive to absorb lessons from international experiences that exemplify how regulations can evolve to meet the challenges involved in non-bank e-money issuers, all with the aim of bringing about universal financial inclusion. POLICY BRIEF ON FINANCIAL INCLUSION IN INDIA: E-money issuers- Risks, Rewards...
POLICY BRIEF ON FINANCIAL INCLUSION IN INDIA: E-money issuers- Risks, Rewards...Indicus Analytics Private Limited
M-PESA is a mobile based transfer of money between customers, facilitated by network of retail agents. Kenya is the first country to have adopted M-PESA where the model witnessed huge success and is contributing big way in enabling financial inclusion in the country. Deployment of M-PESA in India can bring similar benefits as experienced in Kenya. Growing mobile penetration in rural areas would ensure that people are able to benefit from mobile based money transfer concept. Indian regulatory system has also been gearing up to allow technology benefits in enabling financial inclusion, developments are only at introductory stage. POLICY BRIEF ON FINANCIAL INCLUSION IN INDIA: The M-PESA Model- An Overview
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POLICY BRIEF ON FINANCIAL INCLUSION IN INDIA: The Business Correspondent mode...Indicus Analytics Private Limited
Sustaining the development of the country will require current levels of growth to trickle down to the poorest and more excluded of society. A critical way to extend these benefits will be to bring people into the formal sector of finance, whereby they may have more reliable and cheaper access to their financial needs of remittances, savings, borrowings etc. Many models have been suggested as alternatives to traditional branch banking, the current penetration of which is abysmally low. Options include mobile banking, enlisting business correspondents, encouraging MFIs, etc., and each option has its strengths and weaknesses. The models with the greatest potential for the future should be able to leverage on existing retail networks and the rapidly expanding ICT (information and communication technology) platform. As such, the BC model, clubbed with m-banking technology, holds the greatest promise to achieving universal inclusion and steps must be taken to encourage its sustained proliferation POLICY BRIEF ON FINANCIAL INCLUSION IN INDIA: Models to enable universal inc...
POLICY BRIEF ON FINANCIAL INCLUSION IN INDIA: Models to enable universal inc...Indicus Analytics Private Limited
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Coverage in basic version of City Skyline of India - Geography
- 1. CITY SKYLINE OF INDIA 2008-09 Cities District State Category Srinagar Srinagar Jammu & Kashmir Delta Kanniyakumari Kanniyakumari Tamil Nadu Beta Imphal Manipur Imphal West & Imphal East Delta Kolkata Kolkata West Bengal Alpha Madurai Madurai Tamil Nadu Beta Agra Agra Uttar PradeshDelta Varanasi Varanasi Uttar PradeshDelta Moradabad Moradabad Uttar PradeshDelta Bareilly Bareilly Uttar PradeshDelta Salem Salem Tamil Nadu Beta Allahabad Allahabad Uttar PradeshDelta Kanpur Kanpur Nagar Uttar PradeshBeta Chennai Chennai Tamil Nadu Alpha Thiruvananthapuram Thiruvananthapuram Kerala Gamma Gulbarga Gulbarga Karnataka Delta Jamshedpur Purbi Singhbhum Jharkhand Beta Hyderabad Hyderabad Andhra Pradesh Alpha Amritsar Amritsar Punjab Gamma Murshidabad Behrampor West Bengal Delta Vellore Vellore Tamil Nadu Gamma Meerut Meerut Uttar PradeshDelta Jabalpur Jabalpur Madhya Pradesh Gamma Bikaner Bikaner Rajasthan Delta Haora Haora West Bengal Gamma Thanjavur Thanjavur Tamil Nadu Delta Asansol Barddhaman West Bengal Beta Dhanbad Dhanbad Jharkhand Gamma Kozhikode Kozhikode Kerala Delta Coimbatore Coimbatore Tamil Nadu Alpha Jodhpur Jodhpur_r Rajasthan Delta
- 2. Bokaro Bokaro Jharkhand Delta Solapur Solapur Maharashtra Delta Agartala West Tripura Tripura Delta Anantapur Anantapur Andhra Pradesh Delta Ahmadabad Ahmadabad Gujarat Alpha Shillong East Khasi Hills Meghalaya Delta Lucknow Lucknow Uttar PradeshBeta Patna Patna Bihar Beta Ajmer Ajmer Rajasthan Delta Jaipur Jaipur Rajasthan Beta Medinipur Kharagpur West Bengal Delta Jalpaiguri Jalpaiguri West Bengal Delta Udaipur Udaipur Rajasthan Delta Vijayawada Krishna Andhra Pradesh Gamma Urban Areas in South 2424 Parganas Bengal Delta South ParganasWest Ranchi Ranchi Jharkhand Delta Guntur Guntur Andhra Pradesh Delta Hugli Hugli West Bengal Gamma Gwalior Gwalior Madhya Pradesh Delta Tiruchirappalli Tiruchirappalli Tamil nadu Gamma Nellore Nellore Andhra Pradesh Delta Jalandhar Jalandhar Punjab Gamma Mysore Mysore Karnataka Delta Urban Areas in North 2424 Parganas Bengal Beta North ParganasWest Bhavnagar Bhavnagar Gujarat Delta Kota Kota Rajasthan Delta Indore Indore Madhya Pradesh Beta Jamnagar Jamnagar Gujarat Delta Thrissur Thrissur Kerala Delta Nagpur Nagpur Maharashtra Beta Bathinda Bathinda Punjab Delta Visakhapatanam VisakhapatanamAndhra Pradesh Gamma Bhopal Bhopal Madhya Pradesh Beta Durg-Bhilai Durg Chhattisgarh Delta
- 3. Nanded Nanded Maharashtra Delta Cuttack Cuttack Orissa Delta Patiala Patiala Punjab Delta Rajkot Rajkot Gujarat Gamma Raurkela Sundargarh Orissa Delta Vadodara Vadodara Gujarat Beta Kancheepuram Kancheepuram Tamil Nadu Beta Kochi Ernakulam Kerala Beta Kannur Kannur Kerala Delta Delhi Delhi Delhi Alpha Guwahati Kamrup Assam Gamma Thiruvallur Thiruvallur Tamil Nadu Beta Bangalore Bangalore Karnataka Alpha Kolhapur Kolhapur Maharashtra Gamma Raipur Raipur Chhattisgarh Delta Belgaum Belgaum Karnataka Delta Dehradun Dehradun Uttaranchal Delta Ludhiana Ludhiana Punjab Beta Kohima Kohima Nagaland Delta Ghaziabad Ghaziabad Uttar PradeshGamma Hisar Hisar Haryana Delta Ambala Ambala Haryana Delta Urban Areas in Rangareddi Rangareddi Andhra Pradesh Gamma Daman Daman & Diu Daman & Diu Delta Pondicherry Pondicherry Pondicherry Delta Panipat Panipat Haryana Delta Faridabad Faridabad Haryana Beta Aurangabad Aurangabad_m Maharashtra Gamma Nashik Nashik Maharashtra Gamma Port Blair Andaman & Nicobar Islands Nicobar Islands Andaman & Delta Mangalore Dakshina Kannada Karnataka Delta Aizawl Aizawl Mizoram Delta Gurgaon (Old) Gurgaon Haryana Delta Pune Pune Maharashtra Alpha
- 4. Goa Goa Goa Gamma Gangtok East Sikkim Sikkim Delta Kavaratti Lakshadweep LakshadweepDelta Chandigarh Chandigarh Chandigarh Gamma Rupnagar Rupnagar Punjab Delta Bhubaneshwar Khordha Orissa Delta Gandhinagar Gandhinagar Gujarat Delta Surat Surat Gujarat Alpha Noida Gautam BuddhaUttar PradeshDelta Nagar Urban areas Thanehane T Maharashtra Beta Itanagar Papum Pare Arunachal Pradesh Delta Shimla Shimla Himachal Pradesh Delta Mumbai Mumbai Maharashtra Alpha Silvassa Dadra & Nagar Haveli & Nagar Haveli Dadra Delta