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Texas A&M - Code Maroon

Texas A&M - Code Maroon

Texas A&M University

Education免费v1.0
App Store
评分

4.9

9 条评分

星级

★★★★★

最近更新

2020年9月16日

发布日期

2020年9月16日

应用信息

开发者
Texas A&M University
分类
Education
价格
免费
版本
1.0
App ID
1531757076

简介

Code Maroon is the official safety app of Texas A&M University - College Station. It is the only app that integrates with Texas A&M University - College Station's safety and security systems. University Police has worked to develop a unique app that provides students, faculty and staff with added safety on the Texas A&M University - College Station campus. The app will send you important safety alerts and provide instant access to campus safety resources. Code Maroon features include: - Emergency Contacts: Contact the correct services for the Texas A&M University - College Station area in case of an emergency or a non-emergency concern - Mobile Bluelight: Send your location to Texas A&M University - College Station security in real-time in case of a crisis - Friend Walk: Send your location to a friend through email or SMS on your device. Once the friend accepts the Friend Walk request, the user picks their destination and their friend tracks their location in real time; they can keep an eye on them to make sure they make it safely to their destination. - Safety Toolbox: Enhance your safety with the set of tools provided in one convenient app. - Campus Map: Navigate around the Texas A&M University - College Station area. - Transit Map: Find transit routes currently in service. - Emergency Plans: Campus emergency documentation that can prepare you for disasters or emergencies. This can be accessed even when users aren’t connected to Wi-Fi or cellular data. - Safety notifications: Receive instant notifications and instructions from campus safety when on-campus emergencies occur. - Campus safety resources: access all important safety resources in one convenient app. Download today and ensure that you’re prepared in the event of an emergency.

下载量预测

专业 · 预览

预估总下载量

6674501K
保守估计乐观估计

7

低 / 月

10

预估 / 月

19

高 / 月

基于9 条评分
假设评分率1.4%
应用年龄69 个月

基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。