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https://github.com/travishorn/nbi-data-analysis


https://github.com/travishorn/nbi-data-analysis

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README 

        
# Exploratory Data within the National Bridge Inventory (NBI)



## Objective



The scripts and instructions in this project set up a highly-optimized SQL

database for exploring a large dataset of bridge inspections in the U.S. state

of Georgia. Sample SQL queries are provided which perform various data

manipulations including filtering, sorting, grouping, aggregations, and joining

multiple tables to gather interesting insights from the data.



## Dataset



The National Bridge Inventory (NBI) data from 1990 to 2022 for various bridges

in the state of Georgia, USA retrieved from

https://www.kaggle.com/datasets/cynthiamengyuanli/nbi-data-1990-2022



## Insights



Go to [INSIGHTS](insights.md) - a collection of SQL queries I wrote which can provide insights into this data

that could be relevant to real-world applications.



## Methods Applied



1. Data Exploration: I began by performing an initial exploration of the

   dataset. I identified the various tables and entities present and worked to

   understand the relationships between them. I familiarize myself with the

   data's structure, data types, and any potential data quality issues.

2. Data Cleaning and Preparation: I wrote code to preprocess the data in a

   reproducible manner, so the data could be refreshed at any time. At this

   stage, I handled missing values, duplicate records, and any inconsistencies.

   This step was crucial as it set the foundation for accurate analysis.

3. Database Design: I create an appropriate database schema to store the data

   efficiently. I designed tables that reflect the relationships between

   different entities in the dataset. I gave consideration for indexing and

   optimizing the schema for the types of queries users might want to perform.

4. Documentation: Along the way, I wrote comments in the code and kept notes for

   myself. Once the project was working, I got to work writing clear

   documentation, explaining the dataset, the database schema, the SQL queries

   I’ve written, and the insights I’ve gained.

5. Data Manipulation: I wrote write SQL queries to perform various data

   manipulations including filtering, sorting, grouping, aggregations, and

   joining multiple tables to gather valuable insights from the data.

6. Insights: Using those queries, I derived meaningful insights from the data. I

   tried to explore the dataset from different angles to extract valuable

   information that could be relevant to real-world problems.

7. Performance Optimization: I optimized query performance where possible. Using

   indexes and tweaks to the queries themselves, I improved the speed and

   efficiency of certain queries.



## Prerequisites



- Node.js

- A copy of the [National Bridge Inventory (NBI) data from 1990 to 2022 for various

bridges in the state of

Georgia](https://www.kaggle.com/datasets/cynthiamengyuanli/nbi-data-1990-2022)



## Installation



Clone this repository (or download the files from GitHub's web UI)



```sh

git clone https://github.com/travishorn/nbi-data-analysis

```



Change into the directory



```sh

cd nbi-data-analysis

```



Install the dependencies



```sh

npm install

```



Migrate the database



```sh

npx knex migrate:latest

```



This will create `db.sqlite3` with a highly-optimized schema for this dataset.



Seed the database



```sh

npx knex seed:run

```



This will insert data related to NBI's condition rating system and some metadata

into the database.



The source file from Kaggle should be called `BridgesExport_AllYear.csv`.



Place the file in the `source-data` directory.



Load the data from the source file



```sh

node src/load-data.js

```



It may take a moment. This script is doing a lot of extraction and

transformation before loading the data into the database. Very strict checks and

foreign keys are applied to the imported data. If the structure of the source

data has changed since the creation of this project, you may need to adjust the

code in `src/load-data.js`.



When the script finishes, a SQLite database at `db.sqlite3` will be full of

bridge data from the source dataset.



![An ER diagram of the database created by the code in this

project](screenshots/er-diagram.png)



## Using



Explore the data using SQL. Here is an example:



```sql

SELECT        i.structureNumber,

              i.condition,

              s.built,

              s.latitude,

              s.longitude,

              m.name material,

              s.span,

              s.length,

              oa.name ownerAgency

FROM          Inspection i

JOIN          Structure s

ON            s.number = i.structureNumber

JOIN          Material m

ON            m.code = s.materialCode

JOIN          OwnerAgency oa

ON            oa.code = s.ownerAgencyCode

WHERE         i.year = 2022

AND           s.built >= 2012

ORDER BY      i.structureNumber DESC

```



![A table of bridge data in the U.S. state of

Georgia](screenshots/query-result.png)



You can get more information about each column from the `Metadata` table.



```sql

SELECT    m.*

FROM      Metadata m

WHERE     m.`table` = 'Inspection'

```



![A table showing the unit and description of each column in a

table](screenshots/metadata-example.png)



## License



The MIT License



Copyright 2023 Travis Horn



Permission is hereby granted, free of charge, to any person obtaining a copy of

this software and associated documentation files (the “Software”), to deal in

the Software without restriction, including without limitation the rights to

use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of

the Software, and to permit persons to whom the Software is furnished to do so,

subject to the following conditions:



The above copyright notice and this permission notice shall be included in all

copies or substantial portions of the Software.



THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS

FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR

COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER

IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN

CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.