Cities, towns and populated places of the world from Natural Earth (Populated Places simple dataset) with additional columns. neargtid and neargtname are ID and name of the next city/town with bigger population, neargtdist and neargtdelta are the distance and the difference in population. These were obtained with my QGIS plugin QGIS nearest greater. Note: The plugin uses the spatial index of QGIS, which is working on the projected map plain, not on a globe. This is problematic for global data because it fails to find nearest neighbors on the other side of the datum line. However it is fine for regional data. I also added a continent column for quick filtering. Note that "pop_max" is the population of the metropolitan area of cities, not the population within the official administrative boundary. For countries check 'adm0name' and 'sov0name'.

About Dataset

This dataset provides a comprehensive view of the restaurant scene in the 13 metropolitan areas of India( 900 restaurants) . Researchers, analysts, and food enthusiasts can use this dataset to gain insights into various aspects such as dining and delivery ratings, customer reviews and preferences, popular cuisines, best-selling items, and pricing information across different cities. It enables the exploration of dining patterns, the comparison of restaurants and cuisines between cities, and the identification of trends in the food industry. This dataset serves as a valuable resource for understanding the culinary landscape and making data-driven decisions related to the restaurant business, customer satisfaction, and food choices in these metropolitan areas of India. In this dataset, we have more than 127000 rows and 12 columns, a fairly large dataset. You will be able to get hands-on experience while performing the following tasks and will be able to understand how real-world problem statement analysis is done. In Data Analysis what all things we do

Handling Missing Values Explore numerical features. Explore categorical features. Finding relations between features. You have to perform the following tasks:

Explore the Data

read the dataset understand each feature and write down the details. explore the dataset info, describe and find columns with categories, and numeric columns as well. Data Cleaning:

Deleting redundant columns. Renaming the columns. Dropping duplicates. Cleaning individual columns. Remove the NaN values from the dataset Check for some more Transformations Data Visualization:

find out few insights from dataset

• What is the average dining rating across all restaurants in the dataset
• Which metropolitan area has the highest average delivery rating
• What is the total number of dining votes received by all restaurants in each city
  • How many unique cuisines are represented in the dataset
• Which restaurant has the highest average dining rating in each city
• What is the average price of the best-selling dishes compared to the non-best-selling dishes

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Columbus City: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 0 households where the householder is under 25 years old, 15(14.42%) households with a householder aged between 25 and 44 years, 56(53.85%) households with a householder aged between 45 and 64 years, and 33(31.73%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of Columbus City, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Columbus City median household income by age. You can refer the same here

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Columbus City: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 0 households where the householder is under 25 years old, 18(13.43%) households with a householder aged between 25 and 44 years, 75(55.97%) households with a householder aged between 45 and 64 years, and 41(30.60%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of Columbus City, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Columbus City median household income by age. You can refer the same here

This table presents experimental counts of businesses that open, close, or continue their operations each month for various levels of geographic and industry detail across Canada going back to January 2015. The data are available as series that are adjusted for seasonality. The level of geographic detail includes national, provincial and territorial, as well as census metropolitan areas (CMA). The data are also broken down by two-digit North American Industry Classification System (NAICS) with some common aggregations, including one for the total business sector for national, provincial and territorial levels of geography.

As of 2019, most rural inhabitants in Africa resided close to small and mid-sized towns. The nearest city to almost ** percent of the rural population had between 10,000 and ****** inhabitants. Smaller shares of rural households, on the other hand, lived closer to larger urban areas. As of the same year, roughly half of the rural residents lived within ** kilometers from a city.

Model overviewThe indicative Australian Urban Development Risk Model is based on an assumption that recent-past trends in urban expansion (i.e the transition from non-urban land use to urban land use) will continue linearly, and that parameters associated with past expansion are valid predictors of future expansion. The model is underpinned by a conceptual logic, derived within ERIN, based on known datasets and their reasonable association with patterns of urbanisation. Specifically, we predict a higher urban development risk for non-urban locations with:proximity to existing high urban development areashigh increasing trend in street address densityland uses evidently prone to urbanisation andattractive geomorphology.The model is stratified by Australia’s 109 Significant Urban Areas and eight Greater Capital City Statistical Areas (ABS, 2016) and the model output is limited to these zones.Users should note there are likely to areas of high urban development risk beyond these zones, as discussed in the limitations section below.The model draws on multiple datasets to derive values for the parameters above and then combines them into a single index, with a value for every cell in a 9-second grid (about 1.2 million 250 x 250m cells). Derivation of parameter values is described below, followed by the approaches used to combine them into the index, classifying values for mapping, and combining with non-index masks to make the model spatially complete. Model parameters:The model is based on four parameters or predictor variables. For each parameter the field name (in the GIS data spatial attribute table) is provided in square brackets.1. Proximity to existing high urban development areas [NEAR_DIST]This parameter assumes continuation of 2006-2016 trends in urban development within a given Significant Urban Area or Greater Capital City Statistical Area. Locations close to an urban fringe which had expanded significantly during this period are at higher risk of urbanisation. Identifying past change from non-urban to urban involved comparing 2006 and 2016 ABS mesh blocks data. These datasets use a land use classification comprising 10 categories which were reclassified into urban(commercial, education, medical, industrial, residential, transport) and non-urban(parkland, water, primary production, other). Centre points of all 2016 urbanmesh blocks were compared with 2006 non-urbanmesh blocks to identify new urbanmesh blocks, or those which had changed from non-urbanto urban.These new urbanmesh blocks were used to attribute individual cells in the 9sec grid.Distances were then calculated between each new urbancell and its nearest 2006 urbanmesh block.Means (2006 dist. mean) and standard deviations for these distances were derived within each of Australia’s 109 Significant Urban Areas and eight Greater Capital City Statistical Areas (Areas). Larger mean values indicate greater urban expansion over the period for the Areain question. The standard deviations indicate how variable the expansion was within the Areaand, as shown in the table below, were used to account for uncertainty.To extrapolate a risk rating, all 2016 non-urbancells were converted to points and analysed for their distance to the closest 2016 urbancells. This distance was then compared to the relevant Areamean (2006 dist. mean) as described above. Where a 2016 non-urbancell is closer to an urbancell than the mean distance for conversion in the period 2006-2016, it is rated at higher risk, and particularly so for an Areawhere standard deviations are lower.The following table shows thresholds and parameter values. Conditions for cellParameter Value2016 dist. ≥[2006 dist. mean + standard deviation] 0.12016 dist. ≥[2006 dist. mean] but 0.42016 dist. ≥[2006 dist. mean - standard deviation] 0.72016 dist. –standard deviation 12. Increasing trend in street address density [setGnaf]A density of 60 addresses per 250m cell roughly equates to a ‘quarter acre block’urban landscape. This parameter assumes the continuation of trends in street address densification apparent during 2009-2016. Lower density locations (less than 30/cell) are considered non-urban and not at risk. Moderate density locations demonstrating significant increase during 2009-2016 are considered at high risk of urbanisation.The Geocoded National Address File (GNAF) dataset was used to derive both the number of addresses/cell for February 2016, and the increase from May 2009 to February 2016. The following thresholds and parameter values were applied.Conditions for cellParameter ValueLow density, low densification areas (ie all other than the following)02016 GNAF density ≥30 addresses/cell and density change (2009-2016) ≥2012016 GNAF density ≥40 addresses/cell and density change (2009-2016) ≥1012016 GNAF density ≥60 addresses/cell13.Land uses evidently prone to urbanisation [setLandUse]This parameter builds on the analysis used for Parameter 1. At assumes that past high likelihoods for urbanisation associated with certain land use types in different Areaswill persist. The 2016 new urbancells from Parameter 1, above, were compared to a 9 sec grid of the 2006 land use categories (derived from 2006 mesh blocks). For each land use, in each Area(ie a Significant Urban Areas or Greater Capital City Statistical Areas) the proportion urbanised was calculated as a number between zero and one. This number was directly applied as a parameter value for all 2016 non-urbancells. For example, a non-urbancell on a land use which had demonstrated a 60% chance of conversion to urbanin the period 2006-2016, would be scored at 0.6 for this parameter.4. Attractive geomorphology [setSlope]This parameter assumes that past preferences for urbanisation of lower slope areas will continue, given lower costs associated with developing such sites. Slope was calculated,from Geoscience Australia’s 1sec digital elevation model, as the mean slope across each 9sec cell. The following thresholds and parameter values were applied, and are based on a limited research effort into accessible building codes for new dwellings.Conditions for cellParameter ValueSlope ≥20 0.1Slope ≥12 and 0.4Slope ≥6 and 0.7Slope 1Derivation of the indexParameters were combined with equal weight on the assumption that each makes an equal contribution to our capacity to predict future urban expansion. However, individual parameter values are included in the GIS dataset to allow weights to be adjusted to suit particular analyses.Index Value = 0.25 x (proximity to high urban development) + 0.25 x (street address density) + 0.25 x (urbanising land use) + 0.25 x (attractive geomorphology)Classification of index values The index derives values for all cells between zero and one. These were classified into five equal-sized categories from “very low” to “very high”risk.Derivation of mapping unitsMapping units comprise the five risk categories, masked by non-index values for protected areas and existing-urban areas, as follows:Cells identified as protected, either through their inclusion in the Collaborative Australian Protected Areas Database or as ‘offset’areas in an EPBC Strategic Assessment area are ascribed a value of zero.Cells assessed as 2016 likely-urban for the NEAR_DIST parameter, attributed as ‘residential’in the Mesh Block layer,and with greater than 60 GNAF addresses are predicted to be existing-urban areas and ascribed with a value of 1.Non-index maskIndex valueRisk categorySuggested RGB for map colours: Protected from development38, 115, 00 0.2, Very low56, 148, 00.2 0.4, Low152, 230, 00.4 0.6, Moderate255, 255, 00.6 0.8, High255, 163, 430.8, Very high255, 0, 01, Existing urban239, 228, 190

Context

This dataset includes the nearest pickup and drop off city names for each trip record from New York City Taxi Trip Duration Competition.

Content

The dataset introduces two new columns namely "Nearest_PickupCity" and "Nearest_DropoffCity" in addition to the original trip features. The city names may not be the exact geo cities in some cases, they are the nearest city to the trip records, therefore the term "Nearest" describes them best.

Acknowledgements

Implemented the offline package Reverse Geocoder (author - Ajay Thampi ) to get these data attributes. The original package is developed by Richard Pennman.

Inspiration

The idea is that this extension to the NYC Trip data can provide interesting and informative city trends about the taxi trips in NYC area.

1. Which cities drive the taxi trip demand ?
2. Does the trip demand vary based on city as the day moves from morning to night?
3. Can pickup and drop off city information improve trip duration prediction ?

All feedback is welcome

Arizona cities and census designated places (CDP) with ACS 2020 population, counties, coordinates, 2010 Census Tract SVI data, and Rurality for Cities with a 15 minute drive time to nearest Vaccine Site. Travel time to nearest Vaccine Site included.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Columbus. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2022

Based on our analysis ACS 2022 1-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Columbus, the median income for all workers aged 15 years and older, regardless of work hours, was $37,858 for males and $26,206 for females.

These income figures highlight a substantial gender-based income gap in Columbus. Women, regardless of work hours, earn 69 cents for each dollar earned by men. This significant gender pay gap, approximately 31%, underscores concerning gender-based income inequality in the city of Columbus.

- Full-time workers, aged 15 years and older: In Columbus, among full-time, year-round workers aged 15 years and older, males earned a median income of $49,794, while females earned $43,904, resulting in a 12% gender pay gap among full-time workers. This illustrates that women earn 88 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Columbus.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Columbus.

https://i.neilsberg.com/ch/columbus-ga-income-by-gender.jpeg" alt="Columbus, GA gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Columbus median household income by gender. You can refer the same here

All government primary and secondary schools, including Prep/Foundation to Year 9 and multi-campus schools have school zones. This does not include schools with specific enrolment criteria including …Show full descriptionAll government primary and secondary schools, including Prep/Foundation to Year 9 and multi-campus schools have school zones. This does not include schools with specific enrolment criteria including English Language Schools and Select Entry Schools. Specialist schools also do not have school zones and have special enrolment criteria. Designated neighbourhood schools are generally the public school within closest proximity to the student’s permanent residential address, unless the Minister for Education or Regional Director has restricted the zone of the school. Closest proximity is calculated as the nearest school by straight line distance in metropolitan areas (including Geelong, Ballarat and Bendigo), or the nearest school by shortest practical route (in regional areas). School zones were produced in GDA94 VicGrid projection (EPSG: 3111) using locations that represent the front of the school or driveway access. Voronoi polygons define the measure of straight-line distance and calculations using road classes 0 to 7 in the VicMap Roads dataset were used to define the measure of shortest practical route. School zones defined as metropolitan have taken preference over school zones of regional schools where they interface. A small number of school zones have been restricted by the Minister for Education to support schools in managing their enrolments. Some school zones have been aligned with structural and geographic barriers recognising the significant accessibility issues they impose. School zones are reviewed annually and updated as government school provision changes. The school zones ZIP file consists of distinct spatial datasets for primary school zones and school zones for each year of secondary school, acknowledging the different year levels offered by schools. The spatial datasets can be used in conjunction with the school locations CSV file also available on data.vic.gov.au. School zones have been published on the findmyschool.vic.gov.au website. The Victorian government school zones are intended to be relied on by parents and families for the purposes of making enrolment decisions for their children. They should not be relied on for making property purchase decisions, or by any party other than parents and families seeking to enrol their child in a government school.

All government primary and secondary schools, including Prep/Foundation to Year 9 and multi-campus schools have school zones. This does not include schools with specific enrolment criteria including English Language Schools and Select Entry Schools. Specialist schools also do not have school zones and have special enrolment criteria.\r
\r
Designated neighbourhood schools are generally the public school within closest proximity to the student’s permanent residential address, unless the Minister for Education or Regional Director has restricted the zone of the school. Closest proximity is calculated as the nearest school by straight line distance in metropolitan areas (including Geelong, Ballarat and Bendigo), or the nearest school by shortest practical route (in regional areas).\r
\r
School zones were produced in GDA94 VicGrid projection (EPSG: 3111) using locations that represent the front of the school or driveway access. Voronoi polygons define the measure of straight-line distance and calculations using road classes 0 to 7 in the VicMap Roads dataset were used to define the measure of shortest practical route. \r
\r
School zones defined as metropolitan have taken preference over school zones of regional schools where they interface. A small number of school zones have been restricted by the Minister for Education to support schools in managing their enrolments. Some school zones have been aligned with structural and geographic barriers recognising the significant accessibility issues they impose.\r
\r
School zones are reviewed annually and updated as government school provision changes. The school zones ZIP file consists of distinct spatial datasets for primary school zones and school zones for each year of secondary school, acknowledging the different year levels offered by schools. The spatial datasets can be used in conjunction with the school locations CSV file also available on data.vic.gov.au. School zones have been published on the findmyschool.vic.gov.au website.\r
\r
The Victorian government school zones are intended to be relied on by parents and families for the purposes of making enrolment decisions for their children. They should not be relied on for making property purchase decisions, or by any party other than parents and families seeking to enrol their child in a government school.

This dataset presents the school zones for Senior Secondary Colleges in Victoria for the year of 2020.

All public primary and secondary schools, including Prep/Foundation to Year 9 and multi-campus schools have enrolment zones. This does not include schools with specific enrolment criteria including English Language Schools and Select Entry Schools. Specialist schools also do not have zones and have special enrolment criteria.

Designated neighbourhood schools are generally the public school within closest proximity to the student’s permanent residential address, unless the Minister for Education or Regional Director has restricted the zone of the school. Closest proximity is calculated as the nearest school by straight line distance in metropolitan areas (including Geelong, Ballarat and Bendigo), or the nearest school by shortest practical route (in regional areas). Zones were produces in Datum 1994 VicGrid projection (EPSG: 3111) using locations that represent the front of the school or driveway access. Voronoi polygons define the measure of straight line distance and calculations using road classes 0 to 7 in the VicMap road network layer were used to define the measure of shortest practical route.

The zones of schools defined as metropolitan have taken preference over the zones of regional schools where they interface. A small number of zones have been restricted by the Minister for Education to support schools in managing their enrolments. Some schools zones have been aligned with structural and geographic barriers recognising the significant accessibility issues they impose. School enrolment zones are reviewed annually and updated as government school provision changes. The school zone dataset is comprised of distinct map layers for primary schools and for each year of secondary school, acknowledging the different year levels offered by schools.

For more information please visit the Victorian Government Data Portal or the Find My School website.

Please note:

• The Victorian school zone layers can be used in conjunction with the Victorian school location layers.

The geographic extent of a school district or attendance zone. This layer contains all the school districts that serve the City of Santa Ana and the nearby cities