CONTENTS

Mapping Urban Dynamics: Visualizing Housing, Population Change, and

Land Cover Using QGIS

Mapping Values and Categories with Raster Data

Satellite Imagery - KERELA

What percentage of Newark's population lives within walking distance of a park? Where
walking distance is defined as within 1/4 mile of the perimeter of any park. (SPLIT
PERCENTAGE METHOD )

Mapping OSM Edits and Exploring Their impact on Socioeconomic Data in Newark.
Mapping Urban Dynamics: Visualizing Housing, Population Change, and Land Cover
Using QGIS

Objective:

To demonstrate GIS skills by visualizing spatial data using vector and raster
techniques.

Scope:
Newark
Part I
Map housing tenure in Newark to identify areas with the highest rental housing using
2020 Census data.

Part II
Analyze and visualize population change (2000–2020) using raster techniques.

Deliverables:
A series of thematic maps showcasing housing patterns, population changes, and land
cover analysis.

Significance:
This project highlights expertise in spatial data analysis and effective
Visualization techniques.

NEW JERSEY MAP

QUESTIONS AND INTERPRETATION THROUGH THIS ANALYSIS Mapping Values and Categories with Vector Data

From Part I (rental housing):

Start
To discuss which areas in Newark have the most rental housing units.

Select “Layer”
From Part II (population change)

Where has the population decreased between 2000-2020 in New Jersey? (To list the Go to “Add Layer” -> Select “Add Vector Layer”
counties which have experienced population loss since 2000)

Data Source Manager menu opens -> Choose “Source Type: File”

Click on “...” -> Navigate to the “data folder”

Locate “Vector > census_blocks folder” -> Select “tl_2010_-

blocks_newark.geojson”

Complete: Census blocks for Newark are added as a layer

CONVERTING CSV FILE TO POINT LAYER

Start

Open the top menu bar -> Select “Layer”

Go to “Add Layer” -> Select “Add Delimited Text Layer”

Data Source Manager menu opens -> Navigate to the “data

folder” for this assignment

Locate “Tabular > 2010_decennial_census folder”

Select ”DECENNIALSF12010. H4_data_with_over-

lays_2020-09-22T194740.csv”

Complete: Delimited text data is added as a layer

When Table Joint can be processed ?

There is a common identifier field in the vector dataset and the tabular dataset with
further attribute information that common identifier field has unique values (i.e. no
two census blocks have the same ID)

Metadata (which literally means 'a set of data that

describes and gives information about other data')
translates/decodes the field names in the census table.
Start Start

Open the “Add Vector Join” menu Right-click on “Newark block boundary file” -> Select “Export > Save
Features As”

Select “Join Layer”

Export menu opens:
- Set format to “geojson”
Select “Join Field” - Click the “...” button next to File name
- Navigate to “data folder > census_blocks folder”
- Save as “tl_2010_blocks_newark_joinedH4.geojson”
Select “Target Field”

Click “OK” to save the new file

Table join is a temporary relationship,the target layer's
underlying dataset are not modified, but are merely looking
at an association between two datasets in our QGIS project. Complete: New geojson file with Housing Tenure information saved
To permanently save the table join, we must save a copy of the
target layer after we perform the table join.

Start
Start

Right-click on the “Newark block boundaries layer” -> Select “Properties”

Right-click on the layer -> Select “Properties”

Go to the “Symbology tab” in the Layer Properties menu

Navigate to “Joins” -> Click “Add Vector Join”

Select “Graduated Colors” for the symbology type

Add Vector Join menu opens:
- Select “Join layer” From the “Value dropdown”, select the field for rental units:
- Select “Join field” - Field: “2010_H004004” (check metadata or attribute table if unsure)
- Select “Target field”
- Edit “Custom Field Name Prefix”
Classify the data:
- Set “Number of classes” to 5
- Set “Mode” to “Natural Breaks (Jenks)”
Click “OK” - Click “Classify” to generate data breaks

Complete: Vector join applied to the layer Click ”Apply” -> View the changes on the map

Complete: Graduated color map (choropleth) created for rental housing units
INTERPRETATION -

This map shows the Total number Total number of rental

of rental units in each block. units in each block
Areas with dense housing are
more prominent, as they have
higher rental units.

It highlights the sheer volume

of rental units within each
area,regardless of the total
number of housing units.

Use Case:

Useful for understanding where

most rental units are located
in
Newark.

It gives urban planners an idea

of the overall rental housing
market size in different parts
of the city.
Creating a normalized graduated color map

INTERPRETATION -
Highest percentage of
Next you will create a second map, renters.
showing rental housing units normal-
ized by the total number of occupied
housing units to express which areas
of the city of Newark are predomi-
nantly rental versus not

Ratio of Rental to Total Housing Units.

Dividing the rental Units by total hous-

ing units. It shows the highest
percentage of renters, even if the total
numbers of housing is low in an
area.

Use Case:

Useful for understanding the housing

dynamic in each area. It
identifies neighborhoods where renting
is the predominant form of housing,
which might not be visible in Map 1 if
those areas have fewer overall
housing units.
Dot density map

INTERPRETATION -

Dot density maps are a way of symboliz-

ing quantitative data in a manner that
is unclassified in a dot density map
dots are drawn within some geographic
boundary, and each dot represents some
number of a phenomena being visualized.

The dot representation helps to find the

cluster of housing rentals, which
is not prominent in other maps.

It highlights areas where renting is

more common relative to homeownership
or other forms of housing.
VISUALIZATION OF THE MAP
Sequential Gradient:
WHY NATURAL BREAKS ?
Recommended Color Range:
For rental housing units, Natural Breaks is often the preferred
choice because it: Light Blue → Dark Blue or Light Green → Dark Green

Highlights disparities: Allows to see areas with significantly high Why:

or low rental units.
Total renter-occupied units represent a continuous numeric variable where higher
Reveals natural patterns: Helps stakeholders or planners identify values indicate a greater concentration of renters.
clusters or problem areas.
A sequential gradient effectively communicates this increasing intensity.
Blue or green is neutral and professional, minimizing emotional connotations.
Mapping Values and Categories with Raster Data
Population change between 2000-2020 in the New York Metropolitan region using the Gridded Population of the World data set produced by Columbia
University's CIESEN

Where has the population decreased between 2000-2020 in New Jersey?

County of Essex.
County of Cape May.
Satellite Imagery

The location for this false color analysis is

Kerala, India, which is Known for its diverse
landscapes, including dense forests, hilly
Terrains, urban areas, agricultural fields, and
numerous water Bodies.

Kerala is also prone to landslides, especially

during the Monsoon season, due to its hilly
geography and heavy rainfall.

The false color composite created using Landsat

data, is a Combination of Bands 5, 4, and 3
from Landsat 8.

Bright red areas indicate healthy, dense

Vegetation. These are forested or agricultural
regions with high vegetation density and rela-
tively stable land.

Greenish areas could represent sparse

Vegetation or mixed land cover.
These might be less dense agricultural areas.

Cyan or light blue areas likely represent urban

or built-up areas.

In false color composites, buildings and urban

infrastructure or non-vegetated surfaces. Dark
blue represents water bodies such as rivers or
lakes.
This portion of the exercise will introduce you to multispectral satellite imagery, and to the process of
The red-to-pink gradient may indicate a visualizing phenomena through 'false color composites'. As an introduction we will create false color com-
Transition from dense to less dense vegetation. posites using Landsat satellite imagery of Puerto Rico captured just before and after Hurricane Maria (on
September 17 2017 and October 3 2017).
Cyan areas in flat regions indicate urban
Development or industrial zones. After completing this exercise you will:

Rivers or water bodies (dark blue) may affect Have familiarity with basic characteristics of multispectral satellite imagery
nearby areas, making those slopes prone to Learned how to acquire Landsat satellite imagery through the U.S. Geological Survey
Created a false color composite from multispectral Landsat dataset
Overlaps, Intersections and Paths. Spatial Analysis of Parks and Accessibility in
Newark, NJ
Spatial Join
In a spatial join we are using the geometric and geographic relationships between two data layers to associate attribute information from
one dataset with the attribute table for the other. In a spatial join, just as in a table join, the order matters and will impact the re-
sults of the spatial join.
What percentage of Newark's population lives within walking distance of a park? Where
walking distance is defined as within 1/4 mile of the perimeter of any park.

Estimated the percentage of Newark's population within walking distance (1/4 mile) of a park using Census block data.

Employed two methods:

a basic approach that includes all Census blocks touching park buffers and a refined proportional split estimation, accounting for the precise over-
lap between blocks and buffers for greater accuracy.

Estimating population with select by location

In the first estimation method we will overestimate the total population by selecting
all of those census blocks which intersect or are within the 1/4 mile buffers around
each park. And we will consider the total population living within those census blocks
as the estimated population. This is illustrated in the diagram below:
Proportional Split Estimation
Dissolve
In GIS, “dissolve” is a geoprocessing operation used to merge or aggre-
gate adjacent or overlapping polygons in a dataset based on shared at-
tribute values. It simplifies spatial data by removing internal bound-
aries, creating a single, unified polygon or shape for analysis or vi-
sualization purposes.
Intersection
Analyzed spatial relationships in Newark by com-
puting the intersection of census blocks and areas
within 1/4 mile of parks. Utilized QGIS's Inter-
section tool to isolate census block portions
overlapping with park buffers, refining the output
attribute table to include only essential fields
for streamlined analysis.

Calculated the proportion of original block areas

within 1/4 mile of parks using the Field Calculator
in QGIS. Configured the tool to divide the new
block area by the original, generating precise met-
rics for spatial accessibility analysis.

Added a new field, *proportionarea*, to the attri-

bute table to represent the proportion of each
census block area within 1/4 mile of parks. Used the
Field Calculator to estimate the proportion of the
population living in these areas, leveraging spatial
data for demographic accessibility insights.
INTERPRETATION -
The **simple method** overestimates the population by including entire Census blocks that merely touch the park buffer, regardless of actual overlap.
In contrast, the **proportional split estimation** provides a more accurate measure by calculating the population based on the proportion of each block
that overlaps with the buffer. This difference impacts the precision of accessibility analysis.
 Mapping OSM Edits and Exploring Their impact on Socioeconomic Data in Newark.

Software - QGIS

Aim -

The aim of the project is to investigate the connection between NEWARK

socioeconomic factors and Open Street Map (OSM) editing activity NORTH
in Newark, assessing how demographic, economic factors influence
contributions to OSM edits and updates.

Objective -
NEW JERSEY

Analyze the temporal trends of building updates (e.g.2011–2024). CENTRAL

Identify areas with the highest concentration of updates. WEST

Correlate mapping activity with socio-economic indicators

(E.g., population density, income).

Key question -

How do the temporal patterns of OSM updates (based on year) correlate with
socio-economic factors in different regions?
EAST
SOUTH
Expected results -

Identification of spatial and temporal patterns in OSM edits, high-

lighting correlations with socioeconomic factors such as
Poverty and unemployment.

NEWARK, NEW JERSEY

PROJECT TITLE - Mapping OSM Edits and Exploring Their impact on Socioeconomic Data in Newark.
 METHODOLOGY AND WORKFLOW

Extracted geographic data from Overpass Turbo (OSM) and demographic data from
DATA COLLECTION
Census 2019.

Key Points
Standardized, filtered, and removed inconsistencies.
DATA CLEANING

Custom Queries: Used Overpass

Turbo to write custom queries,
VISUALIZATION Mapped spatial data in QGIS
extracting specific features like
buildings from OSM.

Tag-Based Filtering: Leveraged

OSM's tagging system to filter and
retrieve relevant geographic data
OVERPASS TURBO OPEN STREET MAP (OSM) efficiently.

Gained insights into OSM's core

?
data types (nodes, ways, and
A web-based tool for querying and A free, open-source map of the world created by Relations) and their representa-
Visualizing OSM data. Volunteers. tion in QGIS as points, lines, and
Contains geographic data like roads, buildings, parks. polygons, enabling seamless visu-
alization and spatial analysis.
Key Features of Overpass Turbo:
OSM provides the data. Overpass
Volunteers: Individuals worldwide updating data. Turbo allows users to filter,
Custom Queries: Organizations: Governments, NGOs, and companies en- query and visualize that data for
hancing maps. specific purpose.
Filter data by tags used in OSM
(E.g., amenity=school, highway=primary).
Define geographic areas of interest or zoom to the
Core OSM Data Types https://wiki.openstreetmap.org/wiki/Tags

Node: A single geographic point #TAGS Tagging System: In OSM Tags are used to describe
a feature on the map, in order to maintain a
(E.g., a tree or bus stop).
standardizing naming rule
Way: A series of nodes forming
Lines or areas Building=residential
(E.g., roads, building outlines).
Highway=primary
Relation: A group of nodes/ways
Defining complex Structures

(E.g., routes, boundaries).

PROJECT TITLE - Mapping OSM Edits and Exploring Their impact on Socioeconomic Data in Newark.
UNDERSTANDING OVERPASS TURBO

Key Points

Bounding boxes limit the query scope to a

specific geographic area, optimizing data
retrieval for precise spatial analyses.

Extracted and interpreted metadata (e.g.,

user edits, timestamps, changesets) to ana-
lyze the origin, recency, and contributors.

Metadata is data that provides information

about other data. In the context of tools
like Overpass Turbo and OpenStreetMap (OSM),
metadata refers to additional information
that describes the attributes, origins, and
history of the geographic data being que-
ried.
Explanation:
Timestamps: The date and time when the data
was last modified or created.
[out:json][timeout:25];: Sets the output format to JSON and
Changesets: A group of edits made by a user
Specifies a 25-second timeout for the query.
during a single session, which helps track
the context of edits.
Way["building"]({{bbox}});: Queries for all ways with the building tag within
the current map's bounding box. Version History: Information about how many
times the data has been edited and its pre-
Relation["building"]({{bbox}});: Queries for all relations with the building vious versions.
tag within the bounding box (to include complex building structures).

(._;>;);: Recursively fetches all nodes and member elements of the queried
ways and relations.

Out meta;: Outputs all elements with full metadata, including user, change-
set, and timestamp information.

Output metadata (e.g., user, timestamp).

PROJECT TITLE - Mapping OSM Edits and Exploring Their impact on Socioeconomic Data in Newark.
METHODOLOGY AND WORKFLOW
Which year has the highest updates from 2011 to 2024 ?

Using field calculator extract year to

Building footprint with OSM edits(year) Separate column Symbology- Categorize - classify Visualize the data
Year(to_datetime("@timestamp"))

Which areas have the highest or lowest values of population ?

Population numbers by census tract Extracting lat and Lon Converting the required data to Creating points layer Symbology
Of the Geometry using CSV file to change into a point From lat/Lon data
Field calculator layer.

PROJECT TITLE - Mapping OSM Edits and Exploring Their impact on Socioeconomic Data in Newark.
LIST OF MAPS

Poverty % map - Choropleth map.

@ Version Map

Total labour force and total people unemployed.

Building footprints.

Osm edits mapped from 2012 - 2020.

Osm edits mapped from 2021 -2024.

PROJECT TITLE - Mapping OSM Edits and Exploring Their impact on Socioeconomic Data in Newark.
 INTERSECTING PATTERNS: POVERTY AND YEARLY OSM EDITS

RANGE OF POVERTY

200-600

600-1000

Mapping Effort vs. Poverty: Areas with higher

poverty levels (dark basemap) tend to have fewer 1000-1400
OSM edits, particularly in the West and South,
suggesting a possible disparity in mapping focus
1400-2200
or slower urban development in these regions.

Post-2020 Focus: The North and East show higher

concentrations of post-2020 edits, potentially
reflecting increased attention to urban develop-
ment or geographic updates in areas with varied
poverty levels.

Central Region Activity: The central area serves

as a transition zone, reflecting both pre-2020
and post-2020 mapping efforts, likely due to its
mixed socioeconomic characteristics.

OSM EDITS

BEFORE 2020

AFTER 2020

PROJECT TITLE - Mapping OSM Edits and Exploring Their impact on Socioeconomic Data in Newark.
 INTERSECTING PATTERNS: POPULATION DENSITY AND YEARLY OSM EDITS

Population Density & Mapping: Densely popu-

lated areas (North and East) receive more
mapping focus, while less populated regions
(South and West) show gaps in activity.

Post-2020 Trends: Recent edits are concen-

trated in high-density areas, reflecting
urban prioritization, with sparse updates in
less dense regions.

Central Region: Balanced mapping activity

indicates its role as a transition zone be-
tween developed and less developed areas.

Urban Prioritization: Mapping efforts align

with urban needs like infrastructure planning
and service delivery.

OSM EDITS

BEFORE 2020

AFTER 2020

PROJECT TITLE - Mapping OSM Edits and Exploring Their impact on Socioeconomic Data in Newark.
 NUMBER OF TIMES THE FEATURES ARE EDITED

Frequent Edits in North and East: Higher edit

frequencies (4–12 times) indicate priority map-
ping in these urban and active regions.

Low Updates in South and West: Sparse edits (0–2

times) highlight potential gaps in mapping and
slower development focus.

Balanced Central Region: Moderate updates (2–6

times) reflect its transitional role between de-
veloped and less active areas.

Urban Development Correlation: Mapping updates

align with urban activity and development trends.

0-2

2-4

4-6

6 - 12

PROJECT TITLE - Mapping OSM Edits and Exploring Their impact on Socioeconomic Data in Newark.
OSM EDITS FROM 2012 - 2020

Analysis of OSM Edits (2012–2024)

Pre-2020 Mapping Efforts (2012–2020):

- Focused on foundational mapping, with extensive

edits

In central and northern urban areas.

- Priority given to high-density, economically

active Regions, while southern and western regions
saw sparse Updates.

- Built the groundwork for comprehensive urban

datasets.

Significant Updates in 2016:

- Driven by increased awareness, urban development,

and advancements in mapping technologies.

PROJECT TITLE - Mapping OSM Edits and Exploring Their impact on Socioeconomic Data in Newark.
 OSM EDITS FROM 2020 - 2024

Post-2020 Trends (2020–2024):

- Shifted focus to targeted updates and re-

finement of existing data rather than expanding
coverage.

- Concentrated edits in central and eastern

regions reflect urban development and infra-
structure projects.

- Emerging focus on underrepresented areas

like the south and west, aiming for geographic
data equity.

Evolving Mapping Focus:

- Post-2020 edits prioritize accuracy, rele-

vance, and filling critical data gaps.

- Reflects the growing reliance on OSM for

navigation, urban planning, and disaster man-
agement.

Conclusion:

- Mapping trends highlight the transition

from widespread foundational efforts to pre-
cise, equity-focused updates, aligning with
urban growth and technological advancements.

PROJECT TITLE - Mapping OSM Edits and Exploring Their impact on Socioeconomic Data in Newark.
 Urban Core (Central Newark):

Reflects a densely populated region with

Significant workforce presence but also major
Unemployment challenges.

Northern Areas (Branch Brook Park Vicinity):

Indicates balanced economic conditions with
Stable workforce participation and relatively
Low unemployment.

PROJECT TITLE - Mapping OSM Edits and Exploring Their impact on Socioeconomic Data in Newark.
 SUMMARY

Learnings and Interpretations Related to OSM and Overpass Turbo

1. Custom Query Capabilities: Using Overpass Turbo enabled precise extraction of OSM
data through customized queries, facilitating targeted analysis of urban areas and
infrastructure.

2. Tag-Based Filtering: Gained insights into how OSM tags (e.g., `building=residen-
tial`, `highway=primary`) structure data, allowing for granular filtering and map-
ping.

3. Spatial Data Analysis: Leveraged Overpass Turbo to define bounding boxes, limiting
queries to specific geographic areas and optimizing data retrieval.

4. Integration with GIS: Learned how Overpass Turbo outputs seamlessly integrate with
GIS tools (e.g., QGIS), enhancing spatial visualization and analysis.

5. Real-Time Data Retrieval: Overpass Turbo provided access to real-time OSM data,
ensuring analysis was based on the most recent updates and edits.

6. Community Contributions: Explored how volunteer contributions shape OSM’s dynamic

dataset, with Overpass Turbo making it easier to analyze edits over time.

7. Metadata Analysis: Used Overpass Turbo to extract metadata, including timestamps,

user contributions, and version histories, for deeper insights into mapping trends.

8. Efficient Data Workflow: Developed streamlined workflows for querying, visualiz-

ing, and refining OSM data using Overpass Turbo and GIS platforms.

9. Mapping Accuracy: Identified how Overpass Turbo queries help validate and refine
mapped features, improving overall data accuracy.

10. Scalable Applications: Recognized Overpass Turbo’s scalability for urban plan-
ning, socioeconomic analysis, making it a versatile tool for geographic data proj-
ects.

PROJECT TITLE - Mapping OSM Edits and Exploring Their impact on Socioeconomic Data in Newark.