【Practical Exercise】Creating Heatmaps and Histograms for Shopping Mall Pedestrian Flow Data Statistics in MATLAB

# 2.1 Data Reading and Cleaning

## 2.1.1 Data Import

MATLAB offers a variety of data import functions, such as `readtable`, `importdata`, and `xlsread`. These functions allow importing data from different formats (such as CSV, Excel, and text files).

% Import data from a CSV file
data = readtable('shopping_mall_footfall_data.csv');

% Import data from an Excel file
data = importdata('shopping_mall_footfall_data.xlsx');

% Import data from a text file
data = xlsread('shopping_mall_footfall_data.txt');

## 2.1.2 Data Cleaning

Data cleaning is an essential step in data preprocessing, involving the deletion of missing values, handling of outliers, and conversion of data formats. MATLAB provides various data cleaning functions, such as `isnan`, `isinf`, and `fillmissing`.

% Remove missing values
data = data(~isnan(data.footfall), :);

% Handle outliers
data.footfall(data.footfall > 1000) = 1000;

% Convert data format
data.date = datetime(data.date);

# 2. Data Preprocessing and Heatmap Drawing

## 2.1 Data Reading and Cleaning

### 2.1.1 Data Import

MATLAB provides several data import functions, with `importdata` and `xlsread` being commonly used. The `importdata` function can import text files, CSV files, and MAT files, while `xlsread` can import Excel files.

% Import a text file
data = importdata('data.txt');

% Import a CSV file
data = importdata('data.csv');

% Import a MAT file
data = load('data.mat');

% Import an Excel file
data = xlsread('data.xlsx');

### 2.1.2 Data Cleaning

Data cleaning is a crucial step in data preprocessing that removes noise, outliers, and missing values from data, thus improving data quality. MATLAB provides various data cleaning functions, with `isnan`, `isinf`, and `fillmissing` being commonly used.

% Find missing values
missing_values = isnan(data);

% Find infinite values
infinite_values = isinf(data);

% Fill in missing values
data = fillmissing(data, 'mean');

## 2.2 Heatmap Drawing

### 2.2.1 Principle of Heatmaps

A heatmap is a data visualization technique that uses colors to represent values in a data matrix. The larger the value, the deeper the color; the smaller the value, the lighter the color. Heatmaps provide an intuitive display of data distribution and trends.

### 2.2.2 Steps to Draw a Heatmap

1. **Prepare data:** Organize the data into a matrix where each row and column represent a variable.
2. **Create a heatmap:** Use the `heatmap` function to create a heatmap.
3. **Set color map:** Use the `colormap` function to set the heatmap's color map.
4. **Add title and labels:** Use the `title` function along with `xlabel` and `ylabel` to add titles and labels.

% Create a heatmap
heatmap(data);

% Set color map
colormap(jet);

% Add title and labels
title('Heatmap');
xlabel('Variable 1');
ylabel('Variable 2');

### 2.2.3 Example of Heatmap Drawing

% Import data
data = importdata('data.csv');

% Create a heatmap
heatmap(data);

% Set color map
colormap(jet);

% Add title and labels
title('Shopping Mall Footfall Heatmap');
xlabel('Time');
ylabel('Area');

The heatmap displays the distribution of footfall across different areas of the shopping mall at various times. The darker the color, the higher the footfall.

# 3.1 Histogram Drawing

#### 3.1.1 Principle of Histograms

A histogram is a data visualization tool that displays the distribution of data. It divides data into a series of consecutive intervals (called bins) and counts the number of data points within each interval. Then, with bins as the horizontal axis and the count of data points within each bin as the vertical axis, a series of rectangular bars are drawn to form a histogram.

Histograms can reveal the distribution pattern of data, including central tendency, dispersion, and skewness. Central tendency represents the mean or median of the data, dispersion represents the spread of the data, and skewness indicates whether the data distribution is skewed towards one side.

#### 3.1.2 Steps to Draw a Histogram

To draw a histogram in MATLAB, follow these steps:

1. **Data preparation:** Import the data into t