Urban Planning Techniques and Methodologies

Urban Planning Techniques and Methodologies

1. FACULTY OF ARCHITECTURE PLANNING & DESIGN Planning Techniques & Quantitative Analysis Abdul Quadir (Architect & Urban Planner) Assistant Professor FOAPD , Integral University Lucknow

2. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow Unit – 1 References Unit 1- Syllabus Coverage: Module-A Data base for physical surveys including land use, building use, density, building age, etc., and socio-economic surveys; Survey techniques; Module-B Land use classification or coding and expected outputs; Techniques of preparing base maps including understanding the concepts of scales, Module-C Components and detailing for various levels of plans like regional plan, city plan, zoning plan, and local area plan Master of Planning Syllabi, Faculty of Architecture Planning and Design, Integral University Lucknow Survey Techniques and Mapping

3. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References https:// www.britannica.com Urban Planning U rban planning , or defining regulations are uses of space that focus on the physical form, economic functions, and social impacts of the urban  environment  and on the location of different activities within it. Urban planning concerns itself with both the development of open land (“Greenfields sites”) and the revitalization of existing parts of the  city , thereby involving goal setting, data collection and analysis, forecasting, design development, strategic thinking, and public consultation. General Planning Techniques

4. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Planning Process

5. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Egyankosh Planning Techniques Surveying Techniques Base Map Preparation Projection and Forecasting Market Research and Trend analysis Problems and Potentials Mapping Participatory Techniques GIS Mapping Project Proposals

6. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Statistics Canada Planning for Towns and Cities by Dr Meenakshi Singhal Surveying Techniques By definition, surveys are a two-way communication process that enhances the nature and quality of communication between the surveyors and the citizens. The survey shows how the objectives will be reached by clearly describing the target population , the data requirements and the variables to be measured , as well as looking at the questions and possible answers and how the data will be processed and analyzed. Variables ? Social Infrastructure Physical Infrastructure Public Policy Transportation, Logistics and Mobility Energy and Environment Regional influence Economic potential

7. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Survey Need

8. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Types of Surveys Visual Surveys: These are direct inspection surveys which are performed by survey teams in groups or individual units. These survey includes the visual perception of a particular identified aspect. For Example: Road Inventory Survey Activity Mapping Signage and Street Furniture Road Inventory Survey ? A comprehensive survey which can be used to study the profile of the roads in the area of study features like road/Pavement condition, road pavement types, street lighting, luminosity, drain types, encroachments, presence of vendors/street furniture, bus stops etc .

9. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Types of Surveys Diagramming: Diagrams can structure and present information in a readily understandable visual form. They can be used as substitutes for dialogue to elicit information from respondents. This participatory diagramming is a process which asks respondents to share information visually. Mapping is one of the most powerful techniques of diagramming representing the physical and socio-economic attributes of an area (e.g.., infrastructure, land ownership, land use, density, social composition, etc.).

10. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Types of Surveys Dialogue: Semi-structured dialogue is a flexible two-way process where only some initial topics are investigated. These topics can be revised as the practitioner gains insight in the area as information flows in from respondents. Insights of Dialogue Survey ? Elite Bias – the tendency to give more weight to the answers of the educated. Hypothesis Confirmation Bias – tendency to focus selectively on information and ideas which conform to the preconceived hypotheses, assumptions, and beliefs of the interviewer. Concreteness Bias – tendency to generalize from the particulars without probing or cross-checking sufficiently. Consistency Bias – tendency to search prematurely for coherence in the information collected, in order to be able to draw meaningful conclusions as quickly as possible.

11. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Land Use Surveys “L and use” is the term used to describe the human use of land. It represents the economic and cultural activities (e.g., agricultural, residential, industrial, mining, and recreational uses) that are practiced at a given place. Public and private lands frequently represent very different uses. Land use differs from land cover in that some uses are not always physically obvious (e.g., land used for producing timber but not harvested for many years and forested land designated as wilderness will both appear as forest-covered, but they have different uses). Land Use & Land Cover ?

12. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Land Use Surveys Residential: Permits the building of houses, including low (single family homes), medium (townhouses) and high density (apartments/condos) buildings. Commercial: Permits facilities that are built for industry, commerce, trade, recreation, or business use. Sometimes referred to as Employment Use. Institutional/Public: Normally permits public uses such as city hall, municipal facilities, hospitals, museums, religious buildings, schools, and colleges. Industrial: Permits industrial or manufacturing use; that likely generate extra noise, traffic, larger areas of land and perhaps air emissions. Open Space: Any parcel or area of land or water that is essentially unimproved and devoted to outdoor active/passive recreation, public health and safety, and the preservation of natural resources. Protects public access to shorelines of water bodies. 

13. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Land Use Surveys Environmental: Protects sensitive or unstable land from degradation and impacts to sensitive wildlife and vegetation. Allows municipalities to protect the public from the impacts of flood, unstable land and erosion control. Crown Land: Cannot be sold because it legally belongs to the public. This includes lands designated as Crown lands, which are not subject to planning rules. Private development is not normally permitted on crown lands. The Ministry of Natural Resources and Forestry is responsible for managing Crown lands.  Mixed use: Mixed-use blends is a relatively new zone, which identifies lands where multiple uses (normally residential & commercial) are permitted to co-exist, where those functions are physically and functionally integrated to encourage Padestrianization and density while reducing sprawl. Crown Land Type ? Bio Sensitive / Eco sensitive zones River Sensitive zones Lal Dora Area Wetlands Heritage Areas

14. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Building Use Building use survey signifies the purpose of the residents or people residing or using it to map the type of land use to excel the planning strategy in the locality Also these surveys  are generally undertaken to help property owners understand the condition of a property, recording risks and potential expenditure that may be required, enabling them develop the appropriate remedial or  maintenance plans . Use in Planning ? Heritage Management Plan Rejuvenation / Rejuvenation Plan Redevelopment plan

15. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Building Use General property  condition. Identifiable defects. Any structural movement, through subsidence or settlement. Deterioration due to rising damp, penetrating damp, surface condensation or interstitial condensation. Rot or infestations such as woodworm. Heating ventilation and air conditioning services. Other building services such as electrical services, plumbing, drainage, and so on. Alterations that may have been made. Environmental issues. Legal issues that may require an additional expert investigation or advice. Energy performance.

16. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Building Density Building density  (measured by dwelling units per acre or floor area ratio – building square footage divided by land area) determines not only how crowded or built-up a neighborhood appears but also how much the land and the building is worth. The relationship, however, is not necessarily straightforward.  D ensity is the number of developed units in a specific area of land. Residential density, for example, is usually measured by dwelling units per acre (du/ac). The density of developments is determined by zoning codes, and can be an extremely contentious issue in planning and development debates at the local level. 

17. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Socio Economic Survey Socio Economic inquiries for different Rounds is decided on the basis of a 10 year cycle. In this cycle, a year is devoted to Land and Livestock Holdings, Debt and Investment; 1 year to Social Consumption (education, health care, etc.), 2 years to quinquennial surveys on household consumer expenditure, employment & un-employment situation and 4 years to non-agricultural enterprises, namely, manufacturing, trade and services in un-organized sector. Quinquennial inspections involve a thorough survey of all aspects of a building's fabric and are intended to identify problems which have developed since the last time it was inspected and to establish priorities for repair to ensure the preservation of the fabric.

18. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Socio Economic Survey Socio Economic inquiries for different Rounds is decided on the basis of a 10 year cycle. In this cycle, a year is devoted to Land and Livestock Holdings, Debt and Investment; 1 year to Social Consumption (education, health care, etc.), 2 years to quinquennial surveys on household consumer expenditure, employment & un-employment situation and 4 years to non-agricultural enterprises, namely, manufacturing, trade and services in un-organized sector. Quinquennial inspections involve a thorough survey of all aspects of a building's fabric and are intended to identify problems which have developed since the last time it was inspected and to establish priorities for repair to ensure the preservation of the fabric.

19. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow Components and detailing for various levels of plans

20. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References URDPFI

21. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Land Use Classification URDPFI

22. FACULTY OF ARCHITECTURE PLANNING & DESIGN Planning Techniques & Quantitative Analysis Abdul Quadir (Architect & Urban Planner) Assistant Professor FOAPD , Integral University Lucknow

23. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow Unit – 2 References Unit 2- Syllabus Coverage: Module-A Classification of regions, delineation techniques of various types of regions, analysis of structure of nodes, hierarchy, nesting, and rank size; Scalogram , sociogram , etc.; Planning balance sheet; Threshold analysis; Input output analysis, SWOT analysis Master of Planning Syllabi, Faculty of Architecture Planning and Design, Integral University Lucknow Analytical Methods

24. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Classification of regions https://mohua.gov.in/upload/uploadfiles/files/URDPFI%20Guidelines%20Vol%20I(2).pdf

25. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Delineation of regions https://mohua.gov.in/upload/uploadfiles/files/URDPFI%20Guidelines%20Vol%20I(2).pdf

26. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Delineation of regions Need https://mohua.gov.in/upload/uploadfiles/files/URDPFI%20Guidelines%20Vol%20I(2).pdf

27. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Delineation of regions Goa:  The State of Goa has prepared Goa Regional Plan, 2021. The basic approach for preparation of Goa  Regional Plan has been protection of environmentally sensitive area of  the State by introducing  “Eco  Sensitive Zones” and planning details at village Panchayats level. The Regional Plan gives a micro level  planning  of  the  State  of  Goa  with  the  village  Panchayats  as  the  planning  units.  Multiple  level  consultative  process  has  been  undertaken  in  preparation  of  the  plan.  A  decentralised   approach  was  adopted  in  which  a  vision  document  was  prepared  for  the  State  and  was  shared  with  the  local  bodies/villages . Plans prepared at grass root level were compiled at higher levels to prepare Draft State  Regional  Plan.  Another  round  of  public  participation  ensured  inclusiveness.  The  approach  has  been  presented in the following diagram.  https://mohua.gov.in/upload/uploadfiles/files/URDPFI%20Guidelines%20Vol%20I(2).pdf

28. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Analysis of structure of nodes & hierarchy The concept of megaregion as a polycentric urban system is often traced to Jean Gottmann’s work on “megalopolis” in 1961, which was developed to explain large-scale urbanization. Recently, a megaregion, often referred to as polycentric urban agglomerations, is functionally linked and understood as large, spatially linked clusters of urban agglomerations that maintain daily transactional movements of economic linkages, people, services, culture, and materials ( Shiliang et al. Citation2017). In contrast to concepts such as an urban area, a megaregion is made up of critical cities that serve as both hubs and hinges in connecting cities, rather than a single urban system centered on a dominant cit y (Ma, Li, and Huang Citation2021). As Harrison and Hoyler (Citation2015) pointed out, megaregions have emerged as a new nexus of globalization and localization. https://www.tandfonline.com/doi/full/10.1080/10095020.2022.2161425#d1e247 Megaregion has emerged as a global urban form, typically based on the polycentric strategy to enhance regional development. How to measure megaregional spatial structure and discriminate different roles of cities has become increasingly important to enrich the knowledge of the formation of a megaregion. Meanwhile, various indices have been used to identify vital nodes in the field of complex network. Which indices, however, are suitable for megaregion analysis remain unsolved.

29. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Analysis of structure of nodes & hierarchy The concept of megaregion as a polycentric urban system is often traced to Jean Gottmann’s work on “megalopolis” in 1961, which was developed to explain large-scale urbanization. Recently, a megaregion, often referred to as polycentric urban agglomerations, is functionally linked and understood as large, spatially linked clusters of urban agglomerations that maintain daily transactional movements of economic linkages, people, services, culture, and materials ( Shiliang et al. Citation2017). In contrast to concepts such as an urban area, a megaregion is made up of critical cities that serve as both hubs and hinges in connecting cities, rather than a single urban system centered on a dominant cit y (Ma, Li, and Huang Citation2021). As Harrison and Hoyler (Citation2015) pointed out, megaregions have emerged as a new nexus of globalization and localization. https://www.tandfonline.com/doi/full/10.1080/10095020.2022.2161425#d1e247 Megaregion has emerged as a global urban form, typically based on the polycentric strategy to enhance regional development. How to measure megaregional spatial structure and discriminate different roles of cities has become increasingly important to enrich the knowledge of the formation of a megaregion. Meanwhile, various indices have been used to identify vital nodes in the field of complex network. Which indices, however, are suitable for megaregion analysis remain unsolved.

30. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References https://ijtech.eng.ui.ac.id/article/view/1014 Nesting Ideas about nesting were presented by Gibson (1986) with examples in nature, such as valleys, mountains, leaves, and cells. He explained that valleys are nested in mountains, trees are nested in valleys, foliage is nested in trees, and cells are nested in foliage. This phenomenon illustrates the relationship between small units and larger units. Gibson (1986) also wrote about nesting and hierarchy: “Things are components of other things. They would constitute a hierarchy unless the hierarchy is uncategorized, but it is full of transition and overlaps” (Gibson, 1986, p. 9).  Example Considering the case of HI Roundabout is surrounded by tall buildings with commercial functions such as shopping malls, offices, and hotels. Every Sunday from 6:00 to 11:00 a.m., this area is used for Car Free Day events as declared by Regulation of the Authority People are performing various activities, such as sports activities being conducted by teenagers, adults, and children as well as family-related activities such as parenting, playing, chatting, enjoying food and drink, and shopping. Some families go to the CFD event to relax and enjoy the atmosphere. Teenagers and communities often hold many attractions, campaigns, promotional events, and performances while selling food and other merchandise. The observation shows that there are events nested within the main event (Gibson,1986).

31. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References https://www.tandfonline.com/doi/full/10.1080/10095020.2022.2161425#d1e247

32. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References https://ijtech.eng.ui.ac.id/article/view/1014 Scalogram Use in Planning Used to categorize settlements into levels of functional complexity and determine the types and diversity of services and facilities located in central places at various levels of a hierarchy.

33. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Sociogram Sociograms were developed by Jacob L. Moreno to analyze choices or preferences within a group .  They can diagram the structure and patterns of group interactions. A sociogram can be drawn on the basis of many different criteria: Social relations, channels of influence, lines of communication etc . Those points on a sociogram who have many choices are called stars. Those with few or no choices are called isolates. Individuals who choose each other are known to have made a mutual choice. One-way choice refers to individuals who choose someone but the choice is not reciprocated. Cliques are groups of three or more people within a larger group who all choose each other (mutual choice ). Sociograms are the charts or tools used to find the  sociometry  of a social space.

34. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Capital Planning: The process of budgeting resources for an organization's long-term plans, including projections for future projects and their potential gains and losses . Capital planning establishes the right projects to take on, allocation of the available funds, and how much should be spread across those projects to align with long-term organizational objectives. The capital plan generally is reviewed and approved by operations, finance, and executive leadership stakeholders.

35. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References A balance sheet is a financial statement that reports a company's assets, liabilities, and shareholder equity. The balance sheet is one of the three core financial statements that are used to evaluate a business. It provides a snapshot of a company's finances (what it owns and owes) as of the date of publication. The balance sheet adheres to an equation that equates assets with the sum of liabilities and shareholder equity. Fundamental analysts use balance sheets to calculate financial ratios . The balance sheet is an essential tool used by executives, investors, analysts, and regulators to understand the current financial health of a project. It is generally used alongside the two other types of financial statements: the income statement and the cash flow statement.

36. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Planning Balance Sheet An Planning Balance Sheet is a tool used to assess the potential impacts of an urban planning project. It provides a structured method to weigh the costs and benefits, both tangible and intangible, associated with a specific urban development or policy. The balance sheet allows planners, stakeholders, and decision-makers to evaluate the pros and cons of various initiatives from a multi-faceted perspective. It often includes social, environmental, economic, and infrastructural factors. Components of an Urban Planning Balance Sheet Economic Impacts: Costs : Project financing, land acquisition, construction, maintenance. Benefits : Increased property values, economic growth, job creation, tax revenue. Environmental Impacts: Costs : Potential harm to natural resources, pollution, loss of green spaces, increased carbon footprint. Benefits : Preservation or expansion of green spaces, energy-efficient designs, environmental sustainability. Social Impacts: Costs : Possible displacement of communities, inequality issues, disruption of existing social fabric. Benefits : Improved quality of life, affordable housing, enhanced social infrastructure (e.g., schools, hospitals). Transportation and Infrastructure: Costs : Investment in new infrastructure (roads, public transit), potential traffic congestion. Benefits : Improved connectivity, reduced travel time, increased accessibility. Cultural and Aesthetic Impacts: Costs : Loss of historic areas or local character. Benefits : Preservation of cultural heritage, enhancement of urban aesthetics, improved public spaces. Health and Well-being: Costs : Potential health risks (e.g., noise pollution, air quality deterioration). Benefits : Promotion of physical activity (e.g., walkability, cycling infrastructure), mental health improvements through better urban design. Category Costs Benefits Economic High construction costs, maintenance expenses Increased local business activity, job creation Environmental Loss of some natural habitats Sustainable energy systems, reduction in emissions Social Temporary relocation of residents Affordable housing, improved social services Transportation Construction-related traffic disruptions Better public transit, reduced commute times Cultural/Aesthetic Alteration of historic sites Revitalization of cultural landmarks Health and Well-being Noise pollution during construction Improved green spaces, walkability, cleaner air Example: Urban Development Project Balance Sheet

37. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References I-O Analysis Input-output analysis (I-O) is a macroeconomic analysis based on the interdependencies between different economic sectors or industries. Input-output analysis is used to estimate the impacts of positive or negative economic shocks and analyzes the ripple effects throughout the economy. The use of input-output analysis is not common in the Western world or neoclassical economics but often used in Marxist economics when central planning of an economy is required. Input-output tables are the foundation of input-output analysis, depicting rows and columns of data that quantify the supply chain for all of the sectors of an economy. Three types of impacts are modeled in input-output analysis. They are direct impact, indirect impact, and induced impact. I-O models estimate three types of impact: direct, indirect, and induced. These terms are another way of referring to initial, secondary, and tertiary impacts that ripple throughout the economy when a change is made to a given input level. By using I-O models, economists can estimate the change in output across industries due to a change in inputs in one or more specific industries. The direct impact of an economic shock is an initial change in expenditures. For example, building a bridge would require spending on cement, steel, construction equipment, labor, and other inputs. The indirect, or secondary, impact would be due to the suppliers of the inputs hiring workers to meet demand. The induced, or tertiary, impact would result from the workers of suppliers purchasing more goods and services for personal consumption. This analysis can also be run in reverse, seeing what effects on inputs were likely the cause of observed changes in outputs. Here's an example of how I-O analysis works. A local government wants to build a new bridge and needs to justify the cost of the investment. To do so, it hires an economist to conduct an I-O study. The economist talks to engineers and construction companies to estimate how much the bridge will cost, the supplies needed, and how many workers will be hired by the construction company . The economist converts this information into dollar figures and runs numbers through an I-O model, which produces the three levels of impacts. The direct impact is simply the original numbers put into the model, for example, the value of the raw inputs (cement, steel, etc .). The indirect impact is the jobs created by the supplying companies, so cement and steel companies. These companies need to hire workers to complete the project. They either have the funds to do so or have to borrow the money to do so, which would have another impact on banks . The induced impact is the amount of money that the new workers spend on goods and services for themselves and their families. This includes basics such as food and clothing, but now that they have more disposable income, it also relates to goods and services for enjoyment.

38. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References SWOT Analysis SWOT (strengths, weaknesses, opportunities, and threats) analysis is a framework used to evaluate  and to develop strategic planning. SWOT analysis assesses internal and external factors, as well as current and future potential. A SWOT analysis is designed to facilitate a realistic, fact-based, data-driven look at the strengths and weaknesses of an organization, initiatives, or within its industry or a project. Key Takeaways SWOT analysis is a strategic planning technique that provides assessment tools. Identifying core strengths, weaknesses, opportunities, and threats leads to fact-based analysis, fresh perspectives, and new ideas. A SWOT analysis pulls information from internal sources (strengths or weaknesses of the specific company) and external forces that may have uncontrollable impacts on decisions (opportunities and threats). SWOT analysis works best when diverse groups or voices within an organization can provide realistic data points rather than prescribed messaging. The findings of a SWOT analysis are often synthesized to support a single objective or decision that a company is facing. Get a bird’s eye view:  For one, conducting a comprehensive SWOT analysis provides a unique opportunity to gain greater insight into how your business operates. It’s all too easy to get lost in the weeds of the day-to-day workings of your company, and conducting a SWOT analysis allows you to take a broader, bird’s eye view of your business and the position it occupies in your industry . Improve specific campaigns and projects.  Another benefit of SWOT analyses is that this technique can be applied to a wide range of scenarios, not just as an overview of your business. You could use SWOT analyses to evaluate the potential strengths and weaknesses of a forthcoming advertising campaign, a planned content project, or even whether your company should be represented at a trade show or industry event. Benefits

39. FACULTY OF ARCHITECTURE PLANNING & DESIGN Planning Techniques & Quantitative Analysis Abdul Quadir (Architect & Urban Planner) Assistant Professor FOAPD , Integral University Lucknow

40. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow Unit – 3 References Unit 3- Syllabus Coverage: Module-A Methods of population forecasts and projections; Lorenz Curve, Ginni Ratio, Theil’s index, rations: urban – rural, urban concentration, metropolitan concentration; Location dimensions of population groups – social area and strategic choice approach – inter connected decision area analysis. Master of Planning Syllabi, Faculty of Architecture Planning and Design, Integral University Lucknow Demographic Methods

41. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Methods of Population Forecasts and Projections Population forecasting, also known as population projection, is a method for predicting the future size and structure of a population based on past trends and assumptions about the future. Demographers typically consider four main factors when forecasting population changes: fertility rates, mortality rates, initial age profile, and migration. The present and past population record for the city can be obtained from the census population records. After collecting these population figures, the population at the end of design period is predicted using various methods as suitable for that city considering the growth pattern followed by the city. 1. ARITHMETICAL INCREASE METHOD This method is suitable for large and old city with considerable development. If it is used for small, average or comparatively new cities, it will give low result than actual value. In this method the average increase in population per decade is calculated from the past census reports. This increase is added to the present population to find out the population of the next decade. Thus, it is assumed that the population is increasing at constant rate. Hence, dP / dt = C i.e. rate of change of population with respect to time is constant. Therefore, Population after nth decade will be Pn= P + n.C Where, Pn is the population after n decade and P is present population.

42. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References 2. GEOMETRICAL INCREASE METHOD (OR GEOMETRICAL PROGRESSION METHOD) In this method the percentage increase in population from decade to decade is assumed to remain constant. Geometric mean increase is used to find out the future increment in population. Since this method gives higher values and hence should be applied for a new industrial town at the beginning of development for only few decades. The population at the end of nth decade ‘Pn’ can be estimated as: Pn = P (1+ IG/100) n Where, IG = geometric mean (%) P = Present population N = no. of decades.   Example: 2   Considering data given in example 1 predict the population for the year 2021, 2031, and 2041 using geometrical progression method.

43. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References 3. INCREMENTAL INCREASE METHOD This method is modification of arithmetical increase method and it is suitable for an average size town under normal condition where the growth rate is found to be in increasing order. While adopting this method the increase in increment is considered for calculating future population. The incremental increase is determined for each decade from the past population and the average value is added to the present population along with the average rate of increase. Hence, population after nth decade is Pn = P+ n.X + {n (n+1)/2}.Y Where, Pn = Population after nth decade X = Average increase Y = Incremental increase Example: 3   Considering data given in example 1 predict the population for the year 2021, 2031, and 2041 using incremental increase method.

44. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References 4. GRAPHICAL METHOD In this method, the populations of last few decades are correctly plotted to a suitable scale on graph . The population curve is smoothly extended for getting future population. This extension should be done carefully and it requires proper experience and judgment. The best way of applying this method is to extend the curve by comparing with population curve of some other similar cities having the similar growth condition. 5. COMPARATIVE GRAPHICAL METHOD In this method the census populations of cities already developed under similar conditions are plotted. The curve of past population of the city under consideration is plotted on the same graph. The curve is extended carefully by comparing with the population curve of some similar cities having the similar condition of growth. The advantage of this method is that the future population can be predicted from the present population even in the absent of some of the past census report. The use of this method is explained by a suitable example given below. Example: 4 Let the population of a new city X be given for decades 1970, 1980, 1990 and 2000 were 32,000; 38,000; 43,000 and 50,000, respectively. The cities A, B, C and D were developed in similar conditions as that of city X. It is required to estimate the population of the city X in the years 2010 and 2020. The population of cities A, B, C and D of different decades were given below: City A was 50,000; 62,000; 72,000 and 87,000 in 1960, 1972, 1980 and 1990 respectively. City B was 50,000; 58,000; 69,000 and 76,000 in 1962, 1970, 1981 and 1988, respectively. City C was 50,000; 56,500; 64,000 and 70,000 in 1964, 1970, 1980 and 1988, respectively. City D was 50,000; 54,000; 58,000 and 62,000 in 1961, 1973, 1982 and 1989, respectively .

45. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References 5. COMPARATIVE GRAPHICAL METHOD Population curves for the cities A, B, C, D and X were plotted. Then an average mean curve is also plotted by dotted line as shown in the figure. The population curve X is extended beyond 50,000 matching with the dotted mean curve. From the curve the populations obtained for city X are 58,000 and 68,000 in year 2010 and 2020. 5. MASTER PLAN METHOD The big and metropolitan cities are generally not developed in haphazard manner, but are planned and regulated by local bodies according to master plan. The master plan is prepared for next 25 to 30 years for the city. According to the master plan the city is divided into various zones such as residence, commerce and industry. The population densities are fixed for various zones in the master plan. From this population density total water demand and wastewater generation for that zone can be worked out. So by this method it is very easy to access precisely the design population.

46. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References LORENZ CURVE The Lorenz curve is represented by a straight diagonal line, which represents perfect equality in income or wealth distribution; the Lorenz curve lies beneath it, showing estimated distribution. The area that is between the straight line and the curved line is the Gini coefficient. The Gini Coefficient itself is expressed as a representation of the scalar measurement of inequality. In the Lorenz Curve, the Gini Coefficient is expressed as the ratio of the area under the straight line The Lorenz curve is used to represent economic inequality as well as unequal wealth distribution. The farther away the curved line is way from the straight diagonal line, the higher the level of inequality. Constructing a Lorenz curve involves fitting a continuous function to some incomplete set of data, there is no guarantee that the values along a Lorenz curve (other than those actually observed in the data) correspond to the true distributions of income.  Most of the points along the curve are just guesses based on the shape of the curve that best fits the observed data points. So the shape of the Lorenz curve can be sensitive to the quality and sample size of the data and to the mathematical assumptions and judgments as to what constitutes the best fit curve, and these may represent sources of substantial error between the Lorenz curve and the actual distribution. The curve (B) is a graph showing the proportion of overall income or wealth assumed by the bottom x% of the people, although this is not rigorously true for a finite population. It is often used to represent income distribution, where it shows for the bottom x% of households, what percentage, represented by a straight line – A, (y%) of the total income they have. The percentage of households is plotted on the x-axis and the percentage of income is on the y-axis. It can also be used to show the distribution of assets. In such use, many economists consider it to be a measure of social inequality. The Gini coefficient is the ratio of the area between the line of perfect equality and the observed Lorenz curve to the area between the line of perfect equality and the line of perfect inequality.

47. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References GINNI RATIO The Gini coefficient is an index for the degree of inequality in the distribution of income/wealth, used to estimate how far a country's wealth or income distribution deviates from an equal distribution . The Gini coefficient is usually defined mathematically based on the Lorenz curve, which plots the proportion of the total income of the population (y-axis) that is cumulatively earned by the bottom x of the population (see diagram). The line at 45 degrees thus represents perfect equality of incomes. The Gini coefficient can then be thought of as the ratio of the area that lies between the line of equality and the Lorenz curve (marked A in the diagram) over the total area under the line of equality (marked A and B in the diagram); i.e., G = A/(A + B). If there are no negative incomes, it is also equal to 2A and 1 − 2B due to the fact that A + B = 0.5 . Assuming non-negative income or wealth for all, the Gini coefficient's theoretical range is from 0 (total equality) to 1 (absolute inequality). This measure is often rendered as a percentage, spanning 0 to 100. However, if negative values are factored in, as in cases of debt, the Gini index could exceed 1. Typically, we presuppose a positive mean or total, precluding a Gini coefficient below zero. The Gini coefficient is equal to the area marked A divided by the total area of A and B, i.e . Ginni = A/( A+B )   . The axes run from 0 to 1, so A and B form a triangle of area ½ and   Ginni = 2A =1-2B .

48. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References THEIL’S INDEX The Theil index is a statistic primarily used to measure economic inequality and other economic phenomena, though it has also been used to measure racial segregation.  The Theil index TT is the same as redundancy in information theory which is the maximum possible entropy of the data minus the observed entropy. It is a special case of the generalized entropy index. It can be viewed as a measure of redundancy, lack of diversity, isolation, segregation, inequality, non-randomness, and compressibility. It was proposed by a Dutch econometrician Henri Theil (1924–2000) at the Erasmus University Rotterdam . Henri Theil himself said (1967): "The (Theil) index can be interpreted as the expected information content of the indirect message which transforms the population shares as prior probabilities into the income shares as posterior probabilities." Amartya Sen noted, "But the fact remains that the Theil index is an arbitrary formula, and the average of the logarithms of the reciprocals of income shares weighted by income is not a measure that is exactly overflowing with intuitive sense."  The formula for the Theil index is: The Theil index is the sum of the product of the relative values of the distribution and the natural logarithm of those values . The Theil index is a measure of an entropic "distance" between the population and a state of complete equality. The numerical result is in terms of negative entropy, so a higher number indicates more order and further distance from complete equality. There are two types of Theil indexes: Theil's T and L. Both are special cases of the generalized entropy index :

49. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References URBAN CONCENTRATIONS Urban concentration refers to the clustering of people, resources, and activities in specific urban areas, resulting in dense population centers and economic activity. This phenomenon is driven by a combination of factors, including economic opportunities, infrastructure development, and migration patterns . Key Factors in Urban Concentration : Economic Opportunities : Cities attract businesses and industries due to the advantages of proximity, such as access to markets, labor, and reduced transportation costs (Krugman, 1991). These agglomeration economies create a self-reinforcing cycle of urban growth. Rural-to-Urban Migration : Migration plays a crucial role as people move to urban areas seeking better livelihoods, education, and healthcare (Harris & Todaro , 1970). This influx of people intensifies the concentration of population and economic activities. Infrastructure Development : Investment in transportation, communication, and utilities often centers on urban areas, further reinforcing their role as hubs of economic and social activity (World Bank, 2010). Urban Primacy : In some countries, a single dominant city absorbs a disproportionate share of resources, population, and activities. This is known as urban primacy, exemplified by cities like Bangkok and Mexico City (Jefferson, 1939).

50. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References URBAN CONCENTRATIONS Urban concentration refers to the clustering of people, resources, and activities in specific urban areas, resulting in dense population centers and economic activity. This phenomenon is driven by a combination of factors, including economic opportunities, infrastructure development, and migration patterns . Key Factors in Urban Concentration : Economic Opportunities : Cities attract businesses and industries due to the advantages of proximity, such as access to markets, labor, and reduced transportation costs (Krugman, 1991). These agglomeration economies create a self-reinforcing cycle of urban growth. Rural-to-Urban Migration : Migration plays a crucial role as people move to urban areas seeking better livelihoods, education, and healthcare (Harris & Todaro , 1970). This influx of people intensifies the concentration of population and economic activities. Infrastructure Development : Investment in transportation, communication, and utilities often centers on urban areas, further reinforcing their role as hubs of economic and social activity (World Bank, 2010). Urban Primacy : In some countries, a single dominant city absorbs a disproportionate share of resources, population, and activities. This is known as urban primacy, exemplified by cities like Bangkok and Mexico City (Jefferson, 1939). Positive and Negative Impacts: Positive: Urban concentration fosters innovation and economic productivity by bringing together diverse groups and resources (Jacobs, 1969). It also supports economies of scale in infrastructure and service provision (World Bank, 2010). Negative: Overcrowding can lead to housing shortages, traffic congestion, and the proliferation of informal settlements (Harvey, 1973). Environmental degradation, such as air pollution and the heat island effect, is exacerbated in concentrated urban areas (UN-Habitat, 2020).

51. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References METROPOLITAN CONCENTRATIONS Examples of Metropolitan Concentrations: Tokyo Metropolitan Area (Japan) : The largest metropolitan concentration in the world, hosting over 37 million people, Tokyo serves as an economic and cultural powerhouse. New York Metropolitan Area (USA) : This region is a global hub for finance, media, and culture, centered around New York City. Mumbai Metropolitan Region (India) : Known for its financial institutions and Bollywood, Mumbai is an economic and cultural center. London Metropolitan Area (UK) : As a global financial hub, London exerts significant influence on global markets and politics. Metropolitan Concentrations refer to the clustering of population, economic activities, and infrastructure within metropolitan regions, typically centered around a major city and its surrounding suburbs and satellite towns. These areas serve as economic, cultural, and administrative hubs, often displaying high levels of connectivity and interaction . Characteristics of Metropolitan Concentrations : High Population Density: Metropolitan regions house large populations, with a significant proportion of national or regional inhabitants living in these areas. For example, Tokyo and New York Metropolitan Areas each host millions of residents. Economic and Industrial Hubs: These concentrations are often the center of economic activities, hosting diverse industries, corporate hea`dquarters , and innovation centers. Agglomeration economies thrive here, as businesses benefit from proximity to resources and markets (Krugman, 1991). Connectivity : Metropolitan concentrations are usually well-connected internally and externally through robust transportation and communication networks, including highways, rail systems, and airports. Urban Sprawl : These areas often extend beyond the central city into surrounding regions, creating a blend of urban and suburban environments. Cultural and Political Significance : Metropolitan regions often host key cultural, educational, and political institutions, influencing the broader region or country. METROPOLITAN CONCENTRATIONS

52. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References Location dimensions of population groups refer to the spatial aspects of where different demographic groups reside within urban or regional settings. These dimensions highlight how population distribution relates to factors such as social, economic, and environmental influences. Understanding these dimensions is crucial for urban planning, policy-making, and addressing social equity issues. LOCATION DIMENSIONS OF POPULATION GROUPS Key Dimensions of Location for Population Groups: Spatial Distribution : Refers to how population groups are spread across a given area. Some areas may exhibit dense urban cores (e.g., city centers), while others feature sparse rural populations. The spatial patterns often reflect historical development, economic opportunities, and housing availability. Socioeconomic Segregation : Population groups often concentrate in areas based on income levels, education, or occupation. Wealthier groups may reside in suburban or affluent neighborhoods, while lower-income groups are often clustered in less developed areas or informal settlements (Harvey, 1973). Ethnic or Cultural Clustering : Certain population groups may cluster based on ethnicity, language, or cultural identity, creating ethnic enclaves (e.g., Chinatown, Little Italy). These clusters can result from migration patterns, discrimination, or a desire to preserve cultural ties. Urban vs. Rural Divide : Population groups may differ significantly between urban and rural areas. Urban areas tend to attract younger, working-age individuals, while rural areas may have aging populations or those engaged in agriculture and traditional livelihoods. Accessibility to Services and Amenities : Location impacts access to essential services such as healthcare, education, transportation, and employment. Marginalized groups often face disparities in access due to their geographic placement, such as living in peripheral or underserved areas (UN-Habitat, 2020). Environmental Exposure : Location determines the extent to which population groups are exposed to environmental risks, such as air pollution, floods, or industrial hazards. Vulnerable groups are often situated in high-risk areas, such as floodplains or near industrial zones. Housing and Land Use Patterns : Differences in housing types, land tenure, and land use can define the location dimensions of groups. For example, middle-class populations might dominate suburban areas with single-family homes, while lower-income populations reside in high-density apartment complexes or informal settlements.

53. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References LOCATION DIMENSIONS OF POPULATION GROUPS 1. Social Area Analysis: Definition: Social Area Analysis (SAA) examines the spatial organization of urban areas based on social characteristics such as income, ethnicity, education, and family status. It identifies patterns of residential segregation and social differentiation within cities. Application to Location Dimensions: Understanding Social Stratification: SAA helps map out where different socioeconomic groups live within an urban area. For instance, wealthier populations often reside in suburban areas, while lower-income groups may be concentrated in inner-city neighborhoods or informal settlements. Identifying Clusters of Similarity: By categorizing regions based on shared social characteristics, SAA highlights clusters, such as ethnic enclaves, high-income suburbs, or areas with high concentrations of elderly populations . Factors that can be Analyzed : Economic Status: Areas can be identified as affluent or economically disadvantaged. Family Structure: Differentiates areas dominated by nuclear families, singles, or multi-generational households. Ethnicity or Migration Status: Identifies regions with high concentrations of specific ethnic or migrant groups . Implications for Urban Planning : Reducing social disparities by targeting underprivileged areas for infrastructural and service improvements. Promoting mixed-use development to reduce segregation. Addressing accessibility gaps for vulnerable groups.

54. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References LOCATION DIMENSIONS OF POPULATION GROUPS 2. Strategic Choice Approach: Definition: The Strategic Choice Approach ( SCA ) is a decision-making framework designed to address complex and uncertain urban planning challenges. It involves stakeholders in identifying priorities and formulating adaptable strategies . Application to Location Dimensions: Engaging Stakeholders: SCA brings together government officials, urban planners, community leaders, and local residents to collaboratively address location-based disparities in population groups. Defining Strategic Objectives: Planners use SCA to set clear goals, such as improving housing for low-income families, reducing environmental risks for vulnerable groups, or enhancing public transportation connectivity. Exploring Alternatives: Multiple strategies are explored, considering constraints such as budget, political feasibility, and community preferences. For instance: Relocating vulnerable populations away from environmentally hazardous areas. Redeveloping neglected urban areas to attract diverse social groups. Addressing Uncertainty: SCA helps planners deal with uncertainties such as demographic changes, economic shifts, or climate risks. Flexible strategies are developed to adapt to these factors. Integrated Solutions: By combining social, economic, and environmental data, SCA ensures a holistic approach to resolving location-based disparities. Example : The redevelopment of Dharavi, Mumbai, involved SCA by engaging stakeholders to plan housing solutions for low-income populations while balancing economic development goals.

55. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References INTERCONNECTED DECISION AREA ANALYSIS Interconnected Decision Area Analysis is a strategic approach used in urban planning and decision-making to address complex, multifaceted problems where decisions in one area impact or are influenced by decisions in other areas. This method acknowledges the interdependence of various urban systems—such as housing, transportation, environment, and economic development—and seeks to create integrated solutions that balance competing priorities, optimize resource allocation, and enhance urban sustainability. The approach emphasizes the need to view cities as interlinked systems where decisions in one domain create ripple effects across others. For example, a decision to develop a new transportation corridor not only influences mobility patterns but also impacts land use, housing affordability, and environmental quality. Similarly, policies targeting economic growth, such as industrial expansion, may lead to environmental challenges that require mitigation efforts. Key Elements of Interconnected Decision Area Analysis : Interdependency of Decision Areas : Urban systems are inherently interconnected. For example, decisions on transportation infrastructure influence housing affordability and land use patterns, while environmental policies impact economic development and public health. Systemic Thinking : This analysis requires viewing cities as systems where all components interact. Decisions must account for feedback loops and ripple effects across sectors. Stakeholder Engagement : Effective decision-making involves diverse stakeholders, including government bodies, private entities, community organizations, and residents. Their input helps identify priorities and understand the implications of interconnected decisions. Scenario Development : Planners often create scenarios to model how changes in one area affect others. For example, expanding public transit might reduce traffic congestion, lower greenhouse gas emissions, and improve access to jobs. Trade-Offs and Prioritization : Since resources are limited, this approach helps planners balance competing priorities. For instance, a decision to allocate funds for affordable housing may require compromises in other areas like public amenities or green spaces. Example: In Curitiba, Brazil, interconnected decision area analysis was applied in its Bus Rapid Transit (BRT) system development. The transportation policy was linked to land use planning, encouraging mixed-use development along transit corridors. This integration reduced traffic congestion, supported economic activity, and preserved green spaces, demonstrating the benefits of a holistic approach to interconnected urban systems. By using interconnected decision area analysis, planners can navigate the complexity of urban systems, ensuring sustainable and inclusive solutions for modern cities.

56. FACULTY OF ARCHITECTURE PLANNING & DESIGN Planning Techniques & Quantitative Analysis Abdul Quadir (Architect & Urban Planner) Assistant Professor FOAPD , Integral University Lucknow

57. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow Unit – 4 References Unit 4- Syllabus Coverage: Module-A General concepts - statistical interference, population and samples variables, Sampling, simple statistical models, Measures of central Tendency, Measures of Dispersion, Measures of shape of distribution, Correlation, and regression. Master of Planning Syllabi, Faculty of Architecture Planning and Design, Integral University Lucknow Statistical Applications

58. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References GENERAL CONCEPTS: STATISTICAL INFERENCE, POPULATION, SAMPLES, AND VARIABLES In statics and data analysis, understanding the concepts of statistical inference, population, samples, and variables is crucial. These concepts form the foundation for analyzing data, deriving conclusions, and making predictions about broader contexts. 1. Statistical Inference Statistical inference is the process of using data from a sample to make conclusions about the larger population from which the sample is drawn. It involves generalizing findings, estimating population parameters, and testing hypotheses . Key Techniques: Estimation : Involves determining population parameters (e.g., mean, variance) using sample data. Examples include point estimation (single value) and interval estimation (range, like confidence intervals). Hypothesis Testing : Evaluates whether a claim about a population parameter is supported by sample data. It involves null and alternative hypotheses and uses statistical tests (e.g., t-test, chi-square test). Importance: Statistical inference is vital because analyzing an entire population is often impractical, and decisions must be based on incomplete data. For example, political polls use samples to predict election outcomes. 2. Population and Samples Population: The population is the entire group of individuals, items, or observations that are the subject of a study. Populations can be finite (e.g., employees in a company) or infinite (e.g., potential outcomes of a dice roll). Sample: A sample is a subset of the population selected for analysis. It is used to make inferences about the larger population. Sampling methods, such as random sampling, stratified sampling, or systematic sampling, ensure representativeness and reduce bias. Population vs. Sample: Population : Characteristics are called parameters (e.g., population mean, population standard deviation). These are often unknown. Sample : Characteristics are called statistics (e.g., sample mean, sample standard deviation) and are used to estimate parameters. 3. Variables Definition: Variables are measurable characteristics or attributes that can vary among the units of observation in a study. They are classified based on their nature and role in statistical analysis. Types of Variables: Based on Measurement Scale : Quantitative Variables : Numerical values (e.g., age, income). Discrete : Countable values (e.g., number of children). Continuous : Infinite values within a range (e.g., weight, height). Qualitative Variables : Non-numerical categories (e.g., gender, marital status). Nominal : Categories with no intrinsic order (e.g., blood type). Ordinal : Categories with a meaningful order (e.g., satisfaction levels). Based on Role in Analysis : Independent Variable : The variable manipulated or categorized to observe its effect (e.g., treatment type). Dependent Variable : The outcome variable influenced by the independent variable (e.g., patient recovery rate).

59. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References SAMPLING & SIMPLE STATISTICAL MODELS Sampling is the process of selecting a representative subset, or sample, from a larger group, known as the population, to make inferences about the population's characteristics. The population encompasses the entire group being studied, such as all residents of a city, while the sample is a smaller group selected for analysis, such as 1,000 residents surveyed from that city. The effectiveness of sampling lies in how well the sample represents the population, as a well-designed sampling process minimizes bias and errors, ensuring accurate inferences. The primary objectives of sampling include efficiency, as it saves time and resources compared to studying the entire population; feasibility, by enabling analysis when examining the whole population is impractical; and accuracy, as it provides reliable estimates of population parameters when proper methods are employed. Additionally, sampling supports decision-making, aiding policymakers, businesses, and researchers in deriving insights and making informed choices based on data. Determining Sample Size Choosing the right sample size is crucial to ensure the reliability of results. Key factors influencing sample size include: Population Size : Larger populations may require larger samples for accurate representation. However, the relationship is not linear; after a certain point, larger samples yield diminishing returns. Margin of Error : Represents the range within which the true population parameter lies. Smaller margins of error require larger sample sizes. Confidence Level : Indicates the probability that the sample accurately reflects the population. Common confidence levels are 90%, 95%, and 99%. Variability in the Population : More diverse populations require larger samples to capture the variation. Type of Analysis : Complex analyses, such as subgroup comparisons, often require larger samples.

60. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References SAMPLING & SIMPLE STATISTICAL MODELS Types of Sampling Methods A. Probability Sampling In probability sampling, each member of the population has a known, non-zero chance of being selected. This ensures randomness and reduces bias. Simple Random Sampling : Every member has an equal chance of selection. Example: Drawing names from a hat or using random number generators. Advantages : Easy to implement, unbiased. Disadvantages : Can be impractical for large populations without a clear sampling frame. Systematic Sampling : Selects every n- th member from a list after choosing a random starting point. Example: Sampling every 10th household on a street. Advantages : Simple and quick. Disadvantages : Can introduce bias if the list has hidden patterns. Stratified Sampling : Divides the population into subgroups (strata) based on shared characteristics (e.g., age, income) and samples proportionally from each stratum. Example: Surveying equal proportions of urban and rural residents. Advantages : Ensures representation of all subgroups, improves precision. Disadvantages : Requires detailed population data. Cluster Sampling : Divides the population into clusters (e.g., neighborhoods, schools) and randomly selects entire clusters for study. Example: Selecting 10 schools from a district and surveying all students in those schools. Advantages : Cost-effective for geographically dispersed populations. Disadvantages : Higher variability compared to other methods. Multi-Stage Sampling : Combines multiple sampling methods in stages. Example: First selecting districts (cluster sampling) and then households within those districts (random sampling). Advantages : Flexible and adaptable. Disadvantages : Involves complex planning.

61. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References SAMPLING & SIMPLE STATISTICAL MODELS B. Non-Probability Sampling In non-probability sampling, selection is not random, and not every population member has a chance of being chosen. While easier to implement, these methods often introduce bias. Convenience Sampling : Selection is based on accessibility or ease of data collection. Example: Interviewing passersby at a shopping mall. Advantages : Quick and inexpensive. Disadvantages : Not representative; high bias risk. Quota Sampling : Ensures that specific subgroups are represented in the sample based on quotas. Example: Surveying a fixed number of people from each age group. Advantages : Ensures subgroup representation. Disadvantages : Can introduce selection bias. Snowball Sampling : Participants recruit others from their network, often used for hard-to-reach populations. Example: Studying undocumented immigrants by referrals from initial participants. Advantages : Useful for niche or sensitive populations. Disadvantages : Non-representative; dependent on initial participants. Judgmental (Purposive) Sampling : Researchers select participants based on their expertise or judgment. Example: Interviewing subject-matter experts on climate change. Advantages : Focused and efficient for specific research objectives. Disadvantages : High risk of researcher bias.

62. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References SAMPLING & SIMPLE STATISTICAL MODELS Statistical models are structured mathematical frameworks that represent data, enabling researchers and analysts to identify patterns, explore relationships, test hypotheses, and predict outcomes. These models use mathematical equations to explain how variables relate to each other and to generalize findings beyond the observed data. By distilling complex datasets into simplified relationships, statistical models provide insights into the underlying structure and dynamics of the data . Simple statistical models are characterized by their focus on limited complexity, typically examining a single variable or a straightforward relationship between two variables. These models are particularly useful when the goal is to understand or describe a specific aspect of the data without introducing unnecessary complexity. For instance, a simple statistical model might explore how changes in temperature affect ice cream sales, isolating the direct relationship between these two variables while excluding other potential factors. Descriptive models summarize data to provide insights into its main features, using measures such as the mean (average value), median (middle value in ordered data), and variance or standard deviation (indicators of data spread). Inferential models allow generalizations about populations based on sample data, employing tools like confidence intervals to estimate population parameters and hypothesis testing to evaluate the validity of assumptions. Regression models explain relationships between variables, such as in simple linear regression, which models the effect of an independent variable (xxx) on a dependent variable ( yyy ) using the equation Probability models analyze random events and uncertainty, with examples like the binomial model, which addresses success/failure outcomes.

63. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References SAMPLING & SIMPLE STATISTICAL MODELS Applications of Simple Statistical Models: 1. Describing Data: Descriptive statistics provide an essential starting point for understanding data. By summarizing the main features of a dataset, these measures allow researchers to get a quick overview of key trends and patterns, making it easier to communicate findings to others. Averages (Mean or Median): Mean is the most commonly used measure of central tendency, calculated by summing all the data points and dividing by the number of observations. It gives an overall sense of the "average" value in a dataset. However, the mean can be heavily influenced by outliers (extreme values), which is why other measures like the median are also used. Median represents the middle value when the data points are arranged in ascending order. It is less affected by extreme values and is especially useful when dealing with skewed data or outliers, offering a better sense of the typical value in such cases. Variance and Standard Deviation: Variance measures the spread or dispersion of data points around the mean. A higher variance means that the data points are more spread out, while a lower variance suggests that they are clustered closer to the mean. Variance is calculated by averaging the squared differences between each data point and the mean. Standard Deviation is the square root of the variance and offers a more intuitive understanding of variability, as it is expressed in the same units as the original data. It represents the average distance of data points from the mean, allowing researchers to gauge how consistent or variable the data is. These measures help summarize large datasets, providing both an overall picture (central tendency) and an understanding of how varied the data points are (dispersion). 2. Testing Hypotheses: Hypothesis testing allows researchers to assess whether there is enough evidence in the data to support or reject a particular claim about a population parameter. Simple statistical models often employ basic tests to compare groups or assess relationships between variables. t-Test (Independent t-test): A t-test is used to compare the means of two independent groups to determine whether they are statistically significantly different from each other. This test is commonly used when the sample size is small and the population variance is unknown. For example, a t-test could be used to compare the average test scores between two different teaching methods to see if one method results in significantly higher performance than the other. The null hypothesis ( H0​ ) typically assumes that there is no difference between the two groups, and the alternative hypothesis (H1 ​) assumes there is a difference. If the p-value from the test is less than the chosen significance level (e.g., 0.05), the null hypothesis is rejected, and the conclusion is made that the difference between the groups is statistically significant.

64. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References SAMPLING & SIMPLE STATISTICAL MODELS 3. Understanding Relationships : Understanding how variables relate to each other is a core component of statistical analysis, and simple models like correlation and regression provide straightforward ways to explore these relationships. Correlation: Correlation measures the strength and direction of a linear relationship between two variables. The correlation coefficient, often represented as r, ranges from -1 to 1. An r of 1 indicates a perfect positive relationship, while an r of -1 indicates a perfect negative relationship. An r of 0 suggests no linear relationship. For example, a correlation coefficient of 0.8 between the amount of exercise and overall health would suggest a strong positive relationship, indicating that more exercise is associated with better health. However, correlation does not imply causation—just because two variables are correlated does not mean one causes the other. Simple Linear Regression: Simple linear regression models the relationship between one independent variable (xxx) and one dependent variable ( y) using a linear equation. The goal is to fit a line that best describes how changes in x affect y. The equation of this line is given by: 4. Estimating Probabilities: Statistical models also play a key role in probability theory, helping to estimate the likelihood of different outcomes. This is especially useful for understanding uncertainty and making predictions based on observed data. Normal Distribution: The normal distribution is a probability model that describes how data points are distributed in a symmetric, bell-shaped curve. It is widely used because many natural phenomena, such as human heights or measurement errors, follow a normal distribution. The mean and standard deviation are key parameters of this distribution. The normal distribution is used to calculate probabilities associated with specific outcomes. For example, if the distribution of test scores in a class follows a normal curve, you can calculate the probability that a student will score above a certain threshold. Other Probability Models: Binomial Distribution : Used to model situations with two possible outcomes (success or failure) in a fixed number of trials, such as flipping a coin multiple times. Poisson Distribution : Used to model the number of events occurring within a fixed interval of time or space, such as the number of cars passing through a toll booth in an hour.

65. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References MEASURES OF CENTRAL TENDENCY

66. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References MEASURES OF DISPERSION

67. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References MEASURES OF DISPERSION

68. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References MEASURES OF SHAPE OF DISTRIBUTION

69. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References MEASURES OF SHAPE OF DISTRIBUTION

70. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References CORRELATION AND REGRESSION

71. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References CORRELATION AND REGRESSION

72. Ar. Abdul Quadir – Assistant Professor Integral University Lucknow References CORRELATION AND REGRESSION