Lampros DAO
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The heat maps below illustrate the correlation between various metrics calculated to assess sector growth. They highlight how different factors are interconnected and contribute to understanding the dynamics of growth within the sector, both before and during the incentive periods, separately.
The matrix shows that DAU and MAU are strongly correlated with each other and moderately with TVL, while Transaction Count has little correlation with other metrics.
The matrix shows that DAU and MAU are strongly correlated, with transactions moderately correlating with both. TVL has weak correlations with all metrics.
The cluster analysis clearly shows how different sectors are grouped based on different metrics, both before and during the incentive period.
Most sectors fall into Cluster 0 with relatively low DAU, while a few (Cluster 2 and Cluster 1) stand out with higher activity, with "quest" being the most significant outlier.
The scatter plot shows most sectors in Cluster 0 with low MAU, Cluster 2 (DEX) with moderate MAU, and an outlier in Cluster 1 (quest) with high MAU pre-incentive but a sharp drop during incentives.
The scatter plot shows three clusters by transaction count: Cluster 0 (low activity), Cluster 2 (moderate activity in DEX and quest sectors), and Cluster 1 (stables) as an outlier with high pre-incentive activity and a sharp drop during incentives
The scatter plot reveals distinct clusters based on TVL Most sectors fall into Cluster 0 with low TVL, while in Cluster 2 (dex) have moderate TVL. The outlier in Cluster 1 (lending) shows exceptionally high TVL before incentives and a significant increase during incentives.
This chart displays the average ARB value spent by users per transaction type. Each bar represents a transaction type, and the height of the bar shows the average ARB value spent within that category.
This chart illustrates the average ARB value spent by users in each sector. Each bar corresponds to a sector, with the bar height indicating the average ARB value spent within that sector.
This bubble chart shows a sector-based overview of transaction counts and ARB spending. Each bubble represents a transaction type, positioned by sector (x-axis) and transaction count (y-axis), with bubble size reflecting total ARB value. Larger bubbles indicate higher spending within a sector.
This bubble chart shows transaction types based on total transaction counts and ARB spending. Each bubble size reflects the total ARB spent, while the y-axis shows transaction count. The color represents different transaction types.
This bubble chart shows transaction counts and ARB spending by sector (Excluding selling/trading). Bubble size represents ARB value, and color differentiates transaction types, making it easy to compare sector activity and spending.
This stacked bar chart shows the percentage of ARB value spent across sectors and transaction types. Each bar represents a sector, with colored sections indicating the proportion of spending on different transaction types.
This stacked bar chart displays ARB spending by sector and transaction type, excluding "Selling/Trading." Each bar shows the percentage of ARB spent on different transaction types within each sector.
This scatter plot shows the correlation between transaction count and total ARB value by sector, with positive correlations in blue and negative correlations in red.
This Sankey diagram illustrates the actions taken by users after receiving ARB rewards from various distributors. It provides insights into how users allocate their ARB tokens across different activities within the ecosystem.
The visual shows the protocols users interacted with after claiming ARB rewards. It highlights the variety of platforms users engage with
This Sankey diagram shows the flow of ARB tokens from Arbitrum's LTIPP funding to various protocols and how recipients used their rewards
The Sankey diagram below visualizes ARB reward recipients actions across protocol cohorts, showing how users allocate tokens to various actions.
This Sankey diagram illustrates the actions taken by ARB reward recipients across distinct protocol cohorts, revealing key behavioral trends
The bar graph shows the distribution of TVL across the four main categories, along with their respective subcategories, highlighting the breakdown of liquidity across different incentive strategies .
The bar chart below visually represents the TVL generated per ARB token distributed, providing insights into the efficiency of different incentive strategies.
This graph shows us the ARB/User ratio for 69 protocols participating in LTIPP. The ARB/user ratio for each protocol is calculated by dividing the total amount of ARB tokens distributed by the total number of unique users engaged with each protocol.
This graph illustrates the fluctuations in the ARB/User ratio over time. This graph tracks the ARB/user ratio from June 3rd, 2024, to September 2nd, 2024, highlighting key trends and fluctuations in reward distribution per user throughout the period.
This graph compares daily active users (DAU) with the daily ARB rewards distributed across protocols.
This graph examines the relationship between daily transaction count and the ARB rewards distributed.
Correlation analysis measures the strength and direction of the relationship between two variables, with coefficients ranging from -1 (strong negative) to +1 (strong positive). In DeFi, it helps assess the impact of token distributions on metrics like daily active users (DAU) and transaction counts, revealing patterns and effectiveness of incentive programs.
This graph shows the correlation between daily active users (DAU) and daily ARB token distribution.
This graph demonstrates the correlation between daily transaction counts and daily ARB token distribution.
The graph below shows the percentage of users receiving rewards from more than one protocol. The x-axis represents the number of protocols from which users have received rewards, while the y-axis shows the number of users.